https://github.com/picrust/picrust2/wiki/Infer-pathway-abundances

1. Difference between unstratified and stratified

   In the context of PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), unstratified and stratified outputs refer to different ways of presenting the predicted functional profiles of microbial communities.
   
   1. Unstratified Output:
   
       Definition: Unstratified output provides the overall predicted abundance of each function (e.g., gene families, metabolic pathways) across the entire microbial community.
       Characteristics:
           Summarized Data: It aggregates the functional predictions for all taxa in a sample, giving a single abundance value for each function.
           No Taxonomic Information: Does not break down the contribution of each specific taxon to the predicted function. It only provides the total abundance of each function without detailing which taxa are contributing to those functions.
           Use Case: Useful when the overall functional potential of a microbial community is of interest without needing to know the contribution of individual taxa. It simplifies the data and reduces complexity.
       Example: If you are interested in the total predicted abundance of a specific gene across all microbes in a sample, you would use the unstratified output.
   
   2. Stratified Output:
   
       Definition: Stratified output provides the predicted abundance of each function, but also stratifies (or breaks down) this data by the taxonomic origin of the microbes contributing to each function.
       Characteristics:
           Detailed Data: It provides more granular information by showing the predicted abundance of each function for each taxon in the community.
           Taxonomic Breakdown: This output allows you to see how much each taxon (e.g., a specific species or genus) contributes to the predicted abundance of each function.
           Use Case: Useful for understanding the functional contributions of specific taxa within a microbial community. It provides insight into which organisms are potentially driving certain functions within a community.
       Example: If you want to know which specific microbes are contributing to the abundance of a certain gene, the stratified output will give you this information by listing the abundance of that gene for each taxon.
   
   Key Differences:
   
       Level of Detail: Unstratified output provides a high-level summary, whereas stratified output offers a detailed breakdown by taxon.
       Data Granularity: Stratified output is more granular and complex, while unstratified output is simpler and more straightforward.
       Purpose: The choice between unstratified and stratified depends on whether you are interested in the total functional potential of the community (unstratified) or in understanding the functional roles of specific taxa (stratified).
   
   Summary:
   
       Unstratified: Overall predicted functional abundance without taxonomic breakdown.
       Stratified: Predicted functional abundance with detailed taxonomic breakdown for each function.
   
   在PICRUSt2（通过重建未观测状态进行群落的系统发育调查）的背景下，**未分层（unstratified）和分层（stratified）**输出是指呈现微生物群落的预测功能特征的不同方式。
   1. 未分层（Unstratified）输出：
   
       定义：未分层输出提供了整个微生物群落中每个功能（例如，基因家族、代谢途径）的总体预测丰度。
       特点：
           汇总数据：它汇总了样本中所有分类单元的功能预测，为每个功能提供一个总的丰度值。
           无分类信息：不显示每个具体分类单元对预测功能的贡献，仅提供每个功能的总丰度，而不细分哪些分类单元在贡献这些功能。
           适用场景：当对微生物群落的总体功能潜力感兴趣，而不需要知道单个分类单元的贡献时，未分层输出是有用的。它简化了数据，减少了复杂性。
       示例：如果你感兴趣的是一个样本中所有微生物的特定基因的总预测丰度，你可以使用未分层输出。
   
   2. 分层（Stratified）输出：
   
       定义：分层输出提供了每个功能的预测丰度，同时按贡献这些功能的微生物的分类来源进行了分层。
       特点：
           详细数据：通过显示群落中每个分类单元的每个功能的预测丰度，提供了更详细的信息。
           分类细分：这种输出方式可以让你看到每个分类单元（例如，具体的物种或属）对每个功能的预测丰度的贡献。
           适用场景：当需要了解特定分类单元在微生物群落中的功能贡献时，分层输出是有用的。它提供了哪些微生物可能在群落中驱动特定功能的见解。
       示例：如果你想知道哪些具体的微生物在贡献某个基因的丰度，分层输出将提供此信息，列出每个分类单元的该基因丰度。
   
   关键区别：
   
       细节层次：未分层输出提供的是一个高级概述，而分层输出则提供按分类单元的详细细分。
       数据粒度：分层输出更为细化和复杂，而未分层输出更为简单和直接。
       用途：选择未分层还是分层，取决于你是对群落的总体功能潜力感兴趣（未分层），还是希望了解特定分类单元的功能作用（分层）。
   
   总结：
   
       未分层（Unstratified）：总体的预测功能丰度，不包含分类细分。
       分层（Stratified）：包含详细分类细分的预测功能丰度。
   
   For analyzing differential pathways expressed between two sample groups, you should use the unstratified input in PICRUSt2.
   Reason for Choosing Unstratified Input:
   
       Focus on Overall Functional Differences: When comparing the functional profiles of two groups of samples, the primary interest is often in identifying which pathways are differentially abundant overall between the groups, regardless of which specific taxa are contributing to these differences. Unstratified input provides a summary of the total abundance of each function or pathway across the entire microbial community in each sample group, making it easier to compare the overall functional profiles.
   
       Simpler and More Direct Comparison: Unstratified data aggregates the functional predictions for all taxa within each sample. This aggregation simplifies the comparison between groups because it provides a single value per function or pathway for each sample, allowing for straightforward statistical testing of differential abundance.
   
       Reduces Complexity: Stratified input, which breaks down functional contributions by taxa, adds a layer of complexity that is not necessary for identifying overall differential pathways between groups. The unstratified output eliminates this complexity and focuses purely on the functions themselves, rather than on which specific taxa are contributing to these functions.
   
   When to Use Stratified Input:
   
       If you are interested in which specific taxa are responsible for the differences in pathway abundances between the two groups, then stratified input would be useful. It allows you to see not only which pathways are differentially expressed but also how the contribution of these pathways varies across different taxa.
   
   Summary:
   
       For identifying differential pathways expressed between two sample groups, use unstratified input to focus on the overall differences in functional profiles without considering the taxonomic breakdown.
       Use stratified input if you need to understand the taxonomic origins of these functional differences.

2. Pathway inference

   Input files:
       *_metagenome_out/*unstrat.tsv.gz
   
   Mapfiles:
       KEGG_pathways_to_KO.tsv
       KEGG_modules_to_KO.tsv
       * ec_level4_to_metacyc_rxn.tsv
       * metacyc_path2rxn_struc_filt_pro.txt
       metacyc_path2rxn_struc_filt_euk.txt
       metacyc_pathways_structured_filtered
       metacyc_path2rxn_struc_filt_fungi.txt
       metacyc_path2rxn_struc_filt_fungi_present.txt
       metacyc_rxn_to_level4ec.tsv
   
   Output files:
       ./MetaCyc_pathways_out/path_abun_unstrat.tsv
       ./KEGG_pathways_out/path_abun_unstrat.tsv
   
   #The default is to map the EC numbers to Metacyc reactions and then to Metacyc Pathways. ERROR: runtime is too long!
   #pathway_pipeline.py -i EC_metagenome_out/pred_metagenome_contrib.tsv.gz -o pathways_out -p 80
   
   #FILE_GENERATED_FOR_DOWNSTREAM: Map EC numbers to MetaCyc pathways and get stratified output corresponding to contribution of predicted gene family abundances within each predicted genome:
   pathway_pipeline.py -i EC_metagenome_out/pred_metagenome_unstrat.tsv.gz -o MetaCyc_pathways_out_per_seq_contrib -p 80 --per_sequence_contrib --per_sequence_abun EC_metagenome_out/seqtab_norm.tsv.gz --per_sequence_function EC_predicted.tsv.gz
   
   ##ERROR: pred_metagenome_strat.tsv.gz does not exist. Mapping predicted KO abundances to legacy KEGG pathways (with stratified output that represents contributions to community-wide abundances):
   ##Why use '--no_gregroup'? no rows remain after regrouping input table. The default pathway and regroup mapfiles are meant for EC numbers. Note that KEGG pathways are not supported since KEGG is a closed-source database, but you can input custom pathway mapfiles if you have access. If you are using a custom function database did you mean to set the --no-regroup flag and/or change the default pathways mapfile used?
   
   #pathway_pipeline.py -i KO_metagenome_out/pred_metagenome_strat.tsv.gz -o KEGG_pathways_out -p 80  --no_regroup --map /home/jhuang/Tools/picrust2/picrust2/default_files/pathway_mapfiles/KEGG_pathways_to_KO.tsv
   
   #FILE_GENERATED_FOR_DOWNSTREAM
   pathway_pipeline.py -i KO_metagenome_out/pred_metagenome_unstrat.tsv.gz -o KEGG_pathways_out -p 80  --no_regroup --map /home/jhuang/Tools/picrust2/picrust2/default_files/pathway_mapfiles/KEGG_pathways_to_KO.tsv
   pathway_pipeline.py -i KO_metagenome_out/pred_metagenome_unstrat.tsv.gz -o KEGG_pathways_out_per_seq_contrib -p 80  --per_sequence_contrib --per_sequence_abun KO_metagenome_out/seqtab_norm.tsv.gz --per_sequence_function KO_predicted.tsv.gz  --no_regroup --map /home/jhuang/Tools/picrust2/picrust2/default_files/pathway_mapfiles/KEGG_pathways_to_KO.tsv
   
   #Note that the path of map files is under /home/jhuang/Tools/picrust2/picrust2/default_files/pathway_mapfiles
   #ERROR: COG does not fit the pathway_mapfiles KEGG_pathways_to_KO.tsv??
   #pathway_pipeline.py -i COG_metagenome_out/pred_metagenome_contrib.tsv.gz -o COG_pathways_out -p 80 --no_regroup --map /home/jhuang/Tools/picrust2/picrust2/default_files/pathway_mapfiles/KEGG_pathways_to_KO.tsv
   
   # The files KEGG_pathways_out/path_abun_unstrat.tsv KEGG_pathways_out_per_seq_contrib/path_abun_unstrat.tsv are the same!!!!!!!
   diff KEGG_pathways_out/path_abun_unstrat.tsv KEGG_pathways_out_per_seq_contrib/path_abun_unstrat.tsv

3. Add descriptions to 5(gene_family)+2(pathway) tables

   #description_mapfiles
       KEGG_pathways_info.tsv.gz
       KEGG_modules_info.tsv.gz
       metacyc_pathways_info.txt.gz
       ec_level4_info.tsv.gz
       cog_info.tsv.gz
       tigrfam_info.tsv.gz
       pfam_info.tsv.gz
       ko_info.tsv.gz
   
   #--6.1. Add descriptions in gene family tables
   # EC and METACYC is a pair, EC for gene_annotation and METACYC for pathway_annotation, therefore we have 5 m-options for gene family tables, 1 m-option for pathway abundance table, for KEGG a custom description_mapfile is needed.
   add_descriptions.py -i COG_metagenome_out/pred_metagenome_unstrat.tsv.gz -m COG -o COG_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz
   add_descriptions.py -i EC_metagenome_out/pred_metagenome_unstrat.tsv.gz -m EC -o EC_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz
   add_descriptions.py -i KO_metagenome_out/pred_metagenome_unstrat.tsv.gz -m KO -o KO_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz
   add_descriptions.py -i PFAM_metagenome_out/pred_metagenome_unstrat.tsv.gz -m PFAM -o PFAM_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz
   add_descriptions.py -i TIGRFAM_metagenome_out/pred_metagenome_unstrat.tsv.gz -m TIGRFAM -o TIGRFAM_metagenome_out/pred_metagenome_unstrat_descrip.tsv.gz
   
   #--6.2. Add descriptions in pathway abundance tables -m {METACYC,COG,EC,KO,PFAM,TIGRFAM}
   cd MetaCyc_pathways_out_per_seq_contrib
   add_descriptions.py -i path_abun_unstrat.tsv.gz -m METACYC -o path_abun_unstrat_descrip.tsv.gz
   gunzip path_abun_unstrat_descrip.tsv.gz
   cd ..
   cd KEGG_pathways_out_per_seq_contrib
   add_descriptions.py -i path_abun_unstrat.tsv.gz -o path_abun_unstrat_descrip.tsv.gz --custom_map_table /home/jhuang/Tools/picrust2/picrust2/default_files/description_mapfiles/KEGG_pathways_info.tsv.gz
   gunzip path_abun_unstrat_descrip.tsv.gz
   cd ..

4. Difference between Kxxxxxxx (gene or protein) and koxxxxxxx (pathway)

   The terms "ORTHOLOGY: K10989" and "ko00001" refer to different concepts and components within the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, which is used for understanding high-level functions and utilities of biological systems.
   1. ORTHOLOGY: K10989
       Definition: K10989 refers to a specific KEGG Orthology (KO) identifier.
       What It Represents: This identifier is assigned to a specific group of orthologous genes or proteins that perform the same function across different species. For example, K10989 might correspond to a particular enzyme or protein that is conserved across multiple organisms.
       Usage: K10989 is used to refer to a specific function at the gene/protein level. When you see "ORTHOLOGY: K10989," it indicates that this specific gene or protein in a genome has been classified under this orthology group.
   2. ko00001
       Definition: ko00001 refers to a specific KEGG pathway map identifier.
       What It Represents: This identifier is associated with a KEGG pathway, which is a collection of manually drawn pathway maps representing molecular interaction and reaction networks, such as metabolic pathways, signaling pathways, and more.
       Usage: ko00001 typically refers to a high-level map, like the KEGG pathway overview, which includes an entire collection of pathways or a very broad view of metabolism or other cellular processes. The "ko" prefix indicates that it is a KEGG Orthology-based pathway map.
   Summary of Differences:
       Scope:
           K10989 is specific to a particular orthologous group of genes/proteins.
           ko00001 refers to a broad KEGG pathway or map.
       Focus:
           K10989 focuses on the function of specific genes/proteins across species.
           ko00001 provides a visual representation of biological processes or pathways.
       Level of Detail:
           K10989 is detailed at the molecular or functional level of specific proteins/genes.
           ko00001 covers a broader, more comprehensive overview of biological systems or networks.
   These identifiers help researchers navigate between specific gene functions and broader biological processes within the KEGG database.

5. Preparing the input files for STAMP, e.g. path_abun_unstrat_descrip.tsv.gz and metadata.tsv

   Input files needed for STAMP are:
       * pred_metagenome_unstrat_descrip.tsv.gz / path_abun_unstrat_descrip.tsv.gz (from STEP 3)
       * metadata.tsv (see below)
   
   cut -d$'\t' -f1 map_corrected.txt > 1
   cut -d$'\t' -f5 map_corrected.txt > 5
   cut -d$'\t' -f6 map_corrected.txt > 6
   paste -d$'\t' 1 5 > 1_5
   paste -d$'\t' 1_5 6 > metadata.tsv
   
   # NOTE_1: Modify '#SampleID' to 'SampleID' !!
       SampleID        Group   Sex_age
       1       Group1  f.aged
       2       Group1  f.aged
       5       Group1  f.aged
       ...
   
   # NOTE_2: for loading of EC[COG|KO|PFAM|TIGRFAM]_metagenome_out/pred_metagenome_unstrat_descrip.tsv, it doesn't work since 'Data does not form a strict hierarchy. Child FAD binding domain has multiple parents (e.g., PF00667, PF00890)'.
   
   # NOTE_3: for each pathway type (e.g. KEGG or MetaCyc), we need to restart the program. An example setting see STAMP_Screenshot.png.

   

6. Install STAMP

   #https://github.com/picrust/picrust2/wiki/STAMP-example
   conda activate base
   conda install mamba
   
   # -- Install method 1 (Failed) --
   #https://beikolab.cs.dal.ca/software/Quick_installation_instructions_for_STAMP
   mamba create -n stamp_py2 python=2 pyqt=4 numpy scipy matplotlib biom-format stamp
   #pip install matplotlib
   pip install STAMP
   #Alternative: mamba create -n stamp bioconda::stamp
   
   # -- Install method 2 (Failed) --
   cd ~/Tools/STAMP-2.1.3
   python setup.py install
   #byte-compiling /home/jhuang/miniconda3/envs/stamp_py2/lib/python2.7/site-packages/stamp/metagenomics/StringHelper.py to StringHelper.pyc
   #running install_scripts
   #copying build/scripts-2.7/checkHierarchy.py -> /home/jhuang/miniconda3/envs/stamp_py2/bin
   #copying build/scripts-2.7/STAMP -> /home/jhuang/miniconda3/envs/stamp_py2/bin
   #changing mode of /home/jhuang/miniconda3/envs/stamp_py2/bin/checkHierarchy.py to 775
   #changing mode of /home/jhuang/miniconda3/envs/stamp_py2/bin/STAMP to 775
   #running install_data
   #copying LICENSE.txt -> /home/jhuang/miniconda3/envs/stamp_py2/.
   #creating /home/jhuang/miniconda3/envs/stamp_py2/manual
   #copying ./manual/STAMP_Users_Guide.pdf -> /home/jhuang/miniconda3/envs/stamp_py2/./manual
   #copying README.md -> /home/jhuang/miniconda3/envs/stamp_py2/.
   #running install_egg_info
   #Writing /home/jhuang/miniconda3/envs/stamp_py2/lib/python2.7/site-packages/STAMP-2.1.3-py2.7.egg-info
   python STAMP_test.py -v
   python STAMP.py
   #BUG: The two methods above could successfully install STAMP successfully, however, it stalls if starts? Try to install it on notebook!
   curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
   bash Miniforge3-$(uname)-$(uname -m).sh
   mamba install bioconda::stamp pyqt=4
   
   # -- Install method 3 (Failed) --
   #--Quick installation instructions for STAMP--
   #SUCCESSFUL install on Virtualbox 14 or 16
   sudo apt-get install libblas-dev liblapack-dev gfortran
   sudo apt-get install freetype* python-pip python-dev python-numpy python-scipy python-matplotlib
   sudo pip install STAMP  #pip could not find, manually download the pip-package and install with the following command
   sudo python setup.py install #in the STAMP-pip-library.
   #ImportError: No module named biom.parse
   sudo pip install --upgrade biom-format
   
   conda remove -n stamp --all
   #conda create -n stamp pyqt=4
   #conda activate stamp
   #conda install -c bioconda stamp
   conda config --show channels
   
   mamba create -n stamp_py2 pip python=2 pyqt=4 numpy scipy biom-format
   mamba activate stamp_py2
   #pip install matplotlib
   pip install stamp
   
   # -- Install method 4 (Failed) --
   conda remove stamp_pyqt4
   mamba install pyqt=4 stamp
   #conda install icu=56
   
   # -- Install method 5: Windows system on Virtualbox (Failed) --
   sudo apt update
   sudo apt install virtualbox
   sudo apt install virtualbox-ext-pack
   virtualbox
   #http://www.winwin7.com/Win7QiJianBan/XTZJWin7QiJianBan-116517.html
   #http://win.hgyji.com/fanqiexp.html
   #https://eprebys.faculty.ucdavis.edu/2020/04/08/installing-windows-xp-in-virtualbox-or-other-vm/
   https://jingyan.baidu.com/article/a17d52851540e08098c8f219.html
   https://msdn.cyanlemon.net/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/Windows%20XP/%E4%B8%AD%E6%96%87-%E7%AE%80%E4%BD%93/
   MRX3F-47B9T-2487J-KWKMF-RPWBY
   https://blog.51cto.com/u_16213618/11137698
   https://msdn.itellyou.cn/
   
   # -- Install method 6: STAMP_2_1_3.exe on Windows 7 in VirtualBox (Successful) --

7. ALDEx2 (Not_Used!)

   https://bioconductor.org/packages/release/bioc/html/ALDEx2.html

8. Convert png to svg and pdf

   inkscape error_bar.png --export-plain-svg=error_bar.svg (embbed)
   sudo apt update
   sudo apt install autotrace
   sudo apt-get install -y libpng-dev libtiff-dev imagemagick
   git clone https://github.com/autotrace/autotrace.git
   cd autotrace
   #sudo apt install intltool
   #sudo apt install gettext libglib2.0-dev
   #sudo apt install libtool libtool-bin
   #sudo apt install automake
   sudo apt-get install libxml-parser-perl
   ./autogen.sh
   ./configure
   make
   autotrace -output-format svg -output-file error_bar.svg error_bar.png

I’m pleased to know the graphs have been useful. There is a significant difference in beta diversity between Group 2 and Group 6, as demonstrated by the PCA analysis and the attached boxplot. This plot clearly shows significant differences in beta diversity within Group 2 and Group 6, as well as between the two groups. The pairwise Bray-Curtis distances indicate substantial variability when comparing the distances from Group 2 to Group 6 against the distances within each group itself (Group 2 vs. Group 2 and Group 6 vs. Group 6). Additionally, I have included the pairwise Bray-Curtis distance values in an Excel table for the figure.

In line with the principle of beta diversity, we expect the diversity between samples from different groups to be greater than the diversity within groups. Based on this principle, the adjusted p-values for the first two comparisons should be small (e.g., < 0.05), while the last value should approach 1. However, as per the standard, the beta diversity comparisons in Group1_vs_Group5, Group4_vs_Group8, and Group3_vs_Group7 are not significant (see details below).

    # in the file ~/DATA/Data_Marius_16S/core_diversity_e42369/bdiv_even42369_Group/unweighted_unifrac_boxplots/Group_Stats.txt

    #Group2_vs_Group6
    Group2_vs_Group2       Group6_vs_Group2       -3.79732911184  0.000339270171256 (0.0019)      0.238506930393     
    Group6_vs_Group6       Group6_vs_Group2       -4.28406536899  5.44434521753e-05 (4.9e-5)       0.0382737468793    
    Group6_vs_Group6       Group2_vs_Group2       -0.309361435009 0.758613384091 (0.32)  1            

    #Group1_vs_Group5
    Beta-diversity1 Beta-diversity2 t-statistic p-value Bonferroni-corrected p-value
    Group1_vs_Group1    Group5_vs_Group1    2.00417798376   0.0548104465256 1
    Group5_vs_Group5    Group5_vs_Group1    -3.25546483256  0.00335706567643    1
    Group5_vs_Group5    Group1_vs_Group1    -3.32676003333  0.00498942378132    1

    #Group4_vs_Group8
    Group4_vs_Group4    Group4_vs_Group8    1.05703763767   0.293598585458  1
    Group8_vs_Group8    Group4_vs_Group8    -2.81684107057  0.00619523703667    1
    Group4_vs_Group4    Group8_vs_Group8    3.60722199799   0.00074734776948    0.525385481945

    #Group3_vs_Group7
    Group7_vs_Group7    Group7_vs_Group3    -1.69330299127  0.0967469273622 1
    Group3_vs_Group3    Group7_vs_Group3    1.83751562431   0.07220131956   1

    #0.0003  0.2109
    #0.0002  0.1406
    #0.7601  1.0000

    #0.0590  1.0000
    #0.0033  1.0000
    #0.0066  1.0000

    #0.2950  1.0000
    #0.0063  1.0000
    #0.0007  0.4921

    #0.0945  1.0000
    #0.0684  1.0000
    #0.0155  1.0000

For pathway analysis, when we set the p-value threshold to < 0.01, no pathways were found to be enriched between Group1 and Group5, Group4 and Group8, or Group3 and Group7.

1, MicrobiotaProcess_Group1_vs_Group5.R

# https://bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc//MicrobiotaProcess.html
# -----------------------------------
# ---- prepare the R environment ----
#Rscript MicrobiotaProcess.R
#NOTE: exit R script, then login again R-environment; rm -rf Phyloseq*_cache
#mkdir figures
rmarkdown::render('Phyloseq.Rmd',output_file='Phyloseq.html')
# with #alpha = 2.0, running the following script further!
# ("1","2","5","6","7",  "15","16","17","18","19","20",  "29","30","31","32",  "40","41","42","43","44","46")
#div.df2[div.df2 == "Group1"] <- "aged.post"
#div.df2[div.df2 == "Group3"] <- "young.post"
#div.df2[div.df2 == "Group5"] <- "aged.post"
#div.df2[div.df2 == "Group7"] <- "young.post"
# ("8","9","10","12","13","14",  "21","22","23","24","25","26","27","28",  "33","34","35","36","37","38","39","51",  "47","48","49","50","52","53","55")
#div.df2[div.df2 == "Group2"] <- "aged.pre"
#div.df2[div.df2 == "Group4"] <- "young.pre"
#div.df2[div.df2 == "Group6"] <- "aged.pre"
#div.df2[div.df2 == "Group8"] <- "young.pre"
#Group1: f.aged and post
#Group2: f.aged and pre
#Group3: f.young and post
#Group4: f.young and pre
#Group5: m.aged and post
#Group6: m.aged and pre
#Group7: m.young and post
#Group8: m.young and pre
#[,c("1","2","5","6","7",                "8","9","10","12","13","14")]
#[,c("15","16","17","18","19","20",      "21","22","23","24","25","26","27","28")]
#[,c("29","30","31","32",                "33","34","35","36","37","38","39","51")]
#[,c("40","41","42","43","44","46",      "47","48","49","50","52","53","55")]
#For the first set:
    #a6cee3: This is a light blue color, somewhat pastel and soft.
    #b2df8a: A soft, pale green, similar to a light lime.
    #fb9a99: A soft pink, slightly peachy or salmon-like.
    #cab2d6: A pale purple, reminiscent of lavender or a light mauve.
#For the second set:
    #1f78b4: This is a strong, vivid blue, close to cobalt or a medium-dark blue.
    #33a02c: A medium forest green, vibrant and leafy.
    #e31a1c: A bright red, very vivid, similar to fire engine red.
    #6a3d9a: This would be described as a deep purple, akin to a dark lavender or plum.

# -----------------------------
# ---- 3.1. bridges other tools
##https://github.com/YuLab-SMU/MicrobiotaProcess
##https://www.bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html
##https://chiliubio.github.io/microeco_tutorial/intro.html#framework
##https://yiluheihei.github.io/microbiomeMarker/reference/plot_cladogram.html
#BiocManager::install("MicrobiotaProcess")
#install.packages("microeco")
#install.packages("ggalluvial")
#install.packages("ggh4x")
library(MicrobiotaProcess)
library(microeco)
library(ggalluvial)
library(ggh4x)
library(gghalves)
library(tidyr)
## Convert the phyloseq object to a MicrobiotaProcess object
#mp <- as.MicrobiotaProcess(ps.ng.tax)
#mt <- phyloseq2microeco(ps.ng.tax) #--> ERROR
#abundance_table <- mt$abun_table
#taxonomy_table <- mt$tax_table
#ps.ng.tax_abund <- phyloseq::filter_taxa(ps.ng.tax, function(x) sum(x > total*0.01) > 0, TRUE)
#ps.ng.tax_most = phyloseq::filter_taxa(ps.ng.tax_rel, function(x) mean(x) > 0.001, TRUE)
##OPTION1 (NOT_USED): take all samples, prepare ps.ng.tax_abund --> mpse_abund
##mpse <- ps.ng.tax %>% as.MPSE()
#mpse_abund <- ps.ng.tax_abund %>% as.MPSE()
##OPTION2 (USED!): take partial samples, prepare ps.ng.tax or ps.ng.tax_abund (2 replacements!)--> ps.ng.tax_sel --> mpse_abund
ps.ng.tax_sel <- ps.ng.tax_abund
##otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("1","2","5","6","7",  "15","16","17","18","19","20",  "29","30","31","32",  "40","41","42","43","44","46")]
##NOTE: Only choose Group2, Group4, Group6, Group8
#> ps.ng.tax_sel
#otu_table()   OTU Table:         [ 37465 taxa and 29 samples ]
#sample_data() Sample Data:       [ 29 samples by 10 sample variables ]
#tax_table()   Taxonomy Table:    [ 37465 taxa by 7 taxonomic ranks ]
#phy_tree()    Phylogenetic Tree: [ 37465 tips and 37461 internal nodes ]
otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax_abund)[,c("1","2","5","6","7",  "29","30","31","32")]
mpse_abund <- ps.ng.tax_sel %>% as.MPSE()
# A MPSE-tibble (MPSE object) abstraction: 2,352 × 20
# NOTE mpse_abund contains 20 variables: OTU, Sample, Abundance, BarcodeSequence, LinkerPrimerSequence, FileInput, Group,
#   Sex_age 

https://bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc//MicrobiotaProcess.html

1. code of MicrobiotaProcess_Group2_vs_Group6.R

   # https://bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc//MicrobiotaProcess.html
   
   # -----------------------------------
   # ---- prepare the R environment ----
   #Rscript MicrobiotaProcess.R
   #NOTE: exit R script, then login again R-environment; rm -rf Phyloseq*_cache
   rmarkdown::render('Phyloseq.Rmd',output_file='Phyloseq.html')
   
   # -----------------------------
   # ---- 3.1. bridges other tools
   ##https://github.com/YuLab-SMU/MicrobiotaProcess
   ##https://www.bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html
   ##https://chiliubio.github.io/microeco_tutorial/intro.html#framework
   ##https://yiluheihei.github.io/microbiomeMarker/reference/plot_cladogram.html
   #BiocManager::install("MicrobiotaProcess")
   #install.packages("microeco")
   #install.packages("ggalluvial")
   #install.packages("ggh4x")
   
   library(MicrobiotaProcess)
   library(microeco)
   library(ggalluvial)
   library(ggh4x)
   library(gghalves)
   
   ## Convert the phyloseq object to a MicrobiotaProcess object
   #mp <- as.MicrobiotaProcess(ps.ng.tax)
   
   #mt <- phyloseq2microeco(ps.ng.tax) #--> ERROR
   #abundance_table <- mt$abun_table
   #taxonomy_table <- mt$tax_table
   
   #ps.ng.tax_abund <- phyloseq::filter_taxa(ps.ng.tax, function(x) sum(x > total*0.01) > 0, TRUE)
   #ps.ng.tax_most = phyloseq::filter_taxa(ps.ng.tax_rel, function(x) mean(x) > 0.001, TRUE)
   
   ##OPTION1 (NOT_USED): take all samples, prepare ps.ng.tax_abund --> mpse_abund
   ##mpse <- ps.ng.tax %>% as.MPSE()
   #mpse_abund <- ps.ng.tax_abund %>% as.MPSE()
   
   ##OPTION2 (USED!): take partial samples, prepare ps.ng.tax or ps.ng.tax_abund (2 replacements!)--> ps.ng.tax_sel --> mpse_abund
   ps.ng.tax_sel <- ps.ng.tax_abund
   ##otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("1","2","5","6","7",  "15","16","17","18","19","20",  "29","30","31","32",  "40","41","42","43","44","46")]
   ##NOTE: Only choose Group2, Group4, Group6, Group8
   #> ps.ng.tax_sel
   #otu_table()   OTU Table:         [ 37465 taxa and 29 samples ]
   #sample_data() Sample Data:       [ 29 samples by 10 sample variables ]
   #tax_table()   Taxonomy Table:    [ 37465 taxa by 7 taxonomic ranks ]
   #phy_tree()    Phylogenetic Tree: [ 37465 tips and 37461 internal nodes ]
   #-Group4: "21","22","23","24","25","26","27","28",
   #-Group8: ,  "47","48","49","50","52","53","55"
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax_abund)[,c("8","9","10","12","13","14",  "33","34","35","36","37","38","39","51")]
   mpse_abund <- ps.ng.tax_sel %>% as.MPSE()
   # A MPSE-tibble (MPSE object) abstraction: 2,352 × 20
   # NOTE mpse_abund contains 20 variables: OTU, Sample, Abundance, BarcodeSequence, LinkerPrimerSequence, FileInput, Group,
   #   Sex_age 

   , pre_post_stroke , Conc , Vol_50ng , Vol_PCR , Description , # Domain , Phylum , Class , Order , Family , Genus , Species # ———————————– # —- 3.2. alpha diversity analysis # Rarefied species richness + RareAbundance mpse_abund %<>% mp_rrarefy() # ‘chunks’ represent the split number of each sample to calculate alpha # diversity, default is 400. e.g. If a sample has total 40000 # reads, if chunks is 400, it will be split to 100 sub-samples # (100, 200, 300,…, 40000), then alpha diversity index was # calculated based on the sub-samples. # ‘.abundance’ the column name of abundance, if the ‘.abundance’ is not be # rarefied calculate rarecurve, user can specific ‘force=TRUE’. mpse_abund %<>% mp_cal_rarecurve( .abundance = RareAbundance, chunks = 400 ) # The RareAbundanceRarecurve column will be added the colData slot # automatically (default action=”add”) #NOTE mpse_abund contains 22 varibles = 20 varibles + RareAbundance + RareAbundanceRarecurve # default will display the confidence interval around smooth. # se=TRUE # NOTE that two colors #c(“#00A087FF”, “#3C5488FF”) for .group = pre_post_stroke; four colors c(“#1f78b4”, “#33a02c”, “#e31a1c”, “#6a3d9a”) for .group = Group; p1 <- mpse_abund %>% mp_plot_rarecurve( .rare = RareAbundanceRarecurve, .alpha = Observe, ) p2 <- mpse_abund %>% mp_plot_rarecurve( .rare = RareAbundanceRarecurve, .alpha = Observe, .group = Group ) + scale_color_manual(values=c(“#1f78b4”, “#e31a1c”)) + scale_fill_manual(values=c(“#1f78b4”, “#e31a1c”), guide=”none”) # combine the samples belong to the same groups if plot.group=TRUE p3 <- mpse_abund %>% mp_plot_rarecurve( .rare = RareAbundanceRarecurve, .alpha = “Observe”, .group = Group, plot.group = TRUE ) + scale_color_manual(values=c(“#1f78b4”, “#e31a1c”)) + scale_fill_manual(values=c(“#1f78b4”, “#e31a1c”),guide=”none”) png(“rarefaction_of_samples_or_groups.png”, width=1080, height=600) p1 + p2 + p3 dev.off() # —————————————— # 3.3. calculate alpha index and visualization library(ggplot2) library(MicrobiotaProcess) mpse_abund %<>% mp_cal_alpha(.abundance=RareAbundance) mpse_abund #NOTE mpse_abund contains 28 varibles = 22 varibles + Observe , Chao1 , ACE , Shannon , Simpson , Pielou f1 <- mpse_abund %>% mp_plot_alpha( .group=Group, .alpha=c(Observe, Chao1, ACE, Shannon, Simpson, Pielou) ) + scale_fill_manual(values=c(“#1f78b4”, “#e31a1c”), guide=”none”) + scale_color_manual(values=c(“#1f78b4”, “#e31a1c”), guide=”none”) f2 <- mpse_abund %>% mp_plot_alpha( .alpha=c(Observe, Chao1, ACE, Shannon, Simpson, Pielou) ) #ps.ng.tax_sel contais only pre samples –> f1 cannot be generated! png(“alpha_diversity_comparison.png”, width=1400, height=600) f1 / f2 dev.off() # ——————————————- # 3.4. The visualization of taxonomy abundance (Class) mpse_abund %<>% mp_cal_abundance( # for each samples .abundance = RareAbundance ) %>% mp_cal_abundance( # for each groups .abundance=RareAbundance, .group=Group ) mpse_abund #NOTE mpse_abund contains 29 varibles = 28 varibles + RelRareAbundanceBySample # visualize the relative abundance of top 20 phyla for each sample. # .group=time, p1 <- mpse_abund %>% mp_plot_abundance( .abundance=RareAbundance, taxa.class = Class, topn = 20, relative = TRUE ) # visualize the abundance (rarefied) of top 20 phyla for each sample. # .group=time, p2 <- mpse_abund %>% mp_plot_abundance( .abundance=RareAbundance, taxa.class = Class, topn = 20, relative = FALSE ) png(“relative_abundance_and_abundance.png”, width= 1200, height=600) #NOT PRODUCED! p1 / p2 dev.off() #—- h1 <- mpse_abund %>% mp_plot_abundance( .abundance = RareAbundance, .group = Group, taxa.class = Class, relative = TRUE, topn = 20, geom = ‘heatmap’, features.dist = ‘euclidean’, features.hclust = ‘average’, sample.dist = ‘bray’, sample.hclust = ‘average’ ) h2 <- mpse_abund %>% mp_plot_abundance( .abundance = RareAbundance, .group = Group, taxa.class = Class, relative = FALSE, topn = 20, geom = ‘heatmap’, features.dist = ‘euclidean’, features.hclust = ‘average’, sample.dist = ‘bray’, sample.hclust = ‘average’ ) # the character (scale or theme) of figure can be adjusted by set_scale_theme # refer to the mp_plot_dist png(“relative_abundance_and_abundance_heatmap.png”, width= 1200, height=600) aplot::plot_list(gglist=list(h1, h2), tag_levels=”A”) dev.off() # visualize the relative abundance of top 20 class for each .group (Group) p3 <- mpse_abund %>% mp_plot_abundance( .abundance=RareAbundance, .group=Group, taxa.class = Class, topn = 20, plot.group = TRUE ) # visualize the abundance of top 20 phyla for each .group (time) p4 <- mpse_abund %>% mp_plot_abundance( .abundance=RareAbundance, .group= Group, taxa.class = Class, topn = 20, relative = FALSE, plot.group = TRUE ) png(“relative_abundance_and_abundance_groups.png”, width= 1000, height=1000) p3 / p4 dev.off() # ————————— # 3.5. Beta diversity analysis # ——————————————— # 3.5.1 The distance between samples or groups # standardization # mp_decostand wraps the decostand of vegan, which provides # many standardization methods for community ecology. # default is hellinger, then the abundance processed will # be stored to the assays slot. mpse_abund %<>% mp_decostand(.abundance=Abundance) mpse_abund #NOTE mpse_abund contains 30 varibles = 29 varibles + hellinger # calculate the distance between the samples. # the distance will be generated a nested tibble and added to the # colData slot. mpse_abund %<>% mp_cal_dist(.abundance=hellinger, distmethod=”bray”) mpse_abund #NOTE mpse_abund contains 31 varibles = 30 varibles + bray # mp_plot_dist provides there methods to visualize the distance between the samples or groups # when .group is not provided, the dot heatmap plot will be return p1 <- mpse_abund %>% mp_plot_dist(.distmethod = bray) png(“distance_between_samples.png”, width= 1000, height=1000) p1 dev.off() # when .group is provided, the dot heatmap plot with group information will be return. p2 <- mpse_abund %>% mp_plot_dist(.distmethod = bray, .group = Group) # The scale or theme of dot heatmap plot can be adjusted using set_scale_theme function. p2 %>% set_scale_theme( x = scale_fill_manual( values=c(“#1f78b4”, “#e31a1c”), #c(“orange”, “deepskyblue”), guide = guide_legend( keywidth = 1, keyheight = 0.5, title.theme = element_text(size=8), label.theme = element_text(size=6) ) ), aes_var = Group # specific the name of variable ) %>% set_scale_theme( x = scale_color_gradient( guide = guide_legend(keywidth = 0.5, keyheight = 0.5) ), aes_var = bray ) %>% set_scale_theme( x = scale_size_continuous( range = c(0.1, 3), guide = guide_legend(keywidth = 0.5, keyheight = 0.5) ), aes_var = bray ) png(“distance_between_samples_with_group_info.png”, width= 1000, height=1000) p2 dev.off() # when .group is provided and group.test is TRUE, the comparison of different groups will be returned # Assuming p3 is a ggplot object after mp_plot_dist call p3 <- mpse_abund %>% mp_plot_dist(.distmethod = bray, .group = Group, group.test = TRUE, textsize = 6) + theme( axis.title.x = element_text(size = 14), # Customize x-axis label face = “bold” axis.title.y = element_text(size = 14), # Customize y-axis label axis.text.x = element_text(size = 14), # Customize x-axis ticks axis.text.y = element_text(size = 14) # Customize y-axis ticks ) # Save the plot with the new theme settings png(“Comparison_of_Bray_Distances.png”, width = 1000, height = 1000) print(p3) # Ensure that p3 is explicitly printed in the device dev.off() # Extract Bray-Curtis Distance Values and save them in a Excel-table. library(dplyr) library(tidyr) library(openxlsx) # Define the sample numbers vector sample_numbers <- c("8", "9", "10", "12", "13", "14", "33", "34", "35", "36", "37", "38", "39", "51") # Consolidate the list of tibbles using the actual sample numbers bray_data <- bind_rows( lapply(seq_along(mpse_abund$bray), function(i) { tibble( Sample1 = sample_numbers[i], # Use actual sample number Sample2 = mpse_abund$bray[[i]]$braySampley, BrayDistance = mpse_abund$bray[[i]]$bray ) }), .id = "PairID" ) # Print the data frame to check the output print(bray_data) # Write the data frame to an Excel file write.xlsx(bray_data, file = "Bray_Curtis_Distances.xlsx") #DELETE the column "PairID" in Excel file # ----------------------- # 3.5.2 The PCoA analysis #install.packages("corrr") library(corrr) #install.packages("ggside") library(ggside) mpse_abund %<>% mp_cal_pcoa(.abundance=hellinger, distmethod=”bray”) # The dimensions of ordination analysis will be added the colData slot (default). mpse_abund mpse_abund %>% print(width=380, n=2) #NOTE mpse_abund contains 34 varibles = 31 varibles + `PCo1 (30.16%)` , `PCo2 (15.75%)` , `PCo3 (10.53%)` #BUG why 36 variables in mpse_abund %>% print(width=380, n=1) [RareAbundanceBySample , RareAbundanceByGroup ] #> methods(class=class(mpse_abund)) # [1] [ [[<- [<- # [4] $ $<- arrange # [7] as_tibble as.data.frame as.phyloseq #[10] coerce coerce<- colData<- #[13] distinct filter group_by #[16] left_join mp_adonis mp_aggregate_clade #[19] mp_aggregate mp_anosim mp_balance_clade #[22] mp_cal_abundance mp_cal_alpha mp_cal_cca #[25] mp_cal_clust mp_cal_dca mp_cal_dist #[28] mp_cal_nmds mp_cal_pca mp_cal_pcoa #[31] mp_cal_pd_metric mp_cal_rarecurve mp_cal_rda #[34] mp_cal_upset mp_cal_venn mp_decostand #[37] mp_diff_analysis mp_diff_clade mp_envfit #[40] mp_extract_abundance mp_extract_assays mp_extract_dist #[43] mp_extract_feature mp_extract_internal_attr mp_extract_rarecurve #[46] mp_extract_refseq mp_extract_sample mp_extract_taxonomy #[49] mp_extract_tree mp_filter_taxa mp_mantel #[52] mp_mrpp mp_plot_abundance mp_plot_alpha #[55] mp_plot_diff_boxplot mp_plot_diff_res mp_plot_dist #[58] mp_plot_ord mp_plot_rarecurve mp_plot_upset #[61] mp_plot_venn mp_rrarefy mp_select_as_tip #[64] mp_stat_taxa mutate otutree #[67] otutree<- print pull #[70] refsequence refsequence<- rename #[73] rownames<- select show # [ reached getOption("max.print") -- omitted 6 entries ] #see '?methods' for accessing help and source code # We also can perform adonis or anosim to check whether it is significant to the dissimilarities of groups. mpse_abund %<>% mp_adonis(.abundance=hellinger, .formula=~Group, distmethod=”bray”, permutations=9999, action=”add”) mpse_abund %>% mp_extract_internal_attr(name=adonis) #NOTE mpse_abund contains 34 varibles, no new variable, it has been saved in mpse_abund and can be extracted with “mpse_abund %>% mp_extract_internal_attr(name=’adonis’)” #The result of adonis has been saved to the internal attribute ! #It can be extracted using this-object %>% mp_extract_internal_attr(name=’adonis’) #The object contained internal attribute: PCoA ADONIS #Permutation test for adonis under reduced model #Terms added sequentially (first to last) #Permutation: free #Number of permutations: 9999 # #vegan::adonis2(formula = .formula, data = sampleda, permutations = permutations, method = distmethod) # Df SumOfSqs R2 F Pr(>F) #Group 1 0.23448 0.22659 3.5158 5e-04 *** #Residual 12 0.80032 0.77341 #Total 13 1.03480 1.00000 #— #Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # (“1″,”2″,”5″,”6″,”7”, “15”,”16″,”17″,”18″,”19″,”20″, “29”,”30″,”31″,”32″, “40”,”41″,”42″,”43″,”44″,”46″) #div.df2[div.df2 == “Group1”] <- "aged.post" #div.df2[div.df2 == "Group3"] <- "young.post" #div.df2[div.df2 == "Group5"] <- "aged.post" #div.df2[div.df2 == "Group7"] <- "young.post" # ("8","9","10","12","13","14", "21","22","23","24","25","26","27","28", "33","34","35","36","37","38","39","51", "47","48","49","50","52","53","55") #div.df2[div.df2 == "Group2"] <- "aged.pre" #div.df2[div.df2 == "Group4"] <- "young.pre" #div.df2[div.df2 == "Group6"] <- "aged.pre" #div.df2[div.df2 == "Group8"] <- "young.pre" #Group1: f.aged and post #Group2: f.aged and pre #Group3: f.young and post #Group4: f.young and pre #Group5: m.aged and post #Group6: m.aged and pre #Group7: m.young and post #Group8: m.young and pre #[,c("1","2","5","6","7", "8","9","10","12","13","14")] #[,c("15","16","17","18","19","20", "21","22","23","24","25","26","27","28")] #[,c("29","30","31","32", "33","34","35","36","37","38","39","51")] #[,c("40","41","42","43","44","46", "47","48","49","50","52","53","55")] #For the first set: #a6cee3: This is a light blue color, somewhat pastel and soft. #b2df8a: A soft, pale green, similar to a light lime. #fb9a99: A soft pink, slightly peachy or salmon-like. #cab2d6: A pale purple, reminiscent of lavender or a light mauve. #For the second set: #1f78b4: This is a strong, vivid blue, close to cobalt or a medium-dark blue. #33a02c: A medium forest green, vibrant and leafy. #e31a1c: A bright red, very vivid, similar to fire engine red. #6a3d9a: This would be described as a deep purple, akin to a dark lavender or plum. p1 <- mpse_abund %>% mp_plot_ord( .ord = pcoa, .group = Group, .color = Group, .size = 2.4, .alpha = 1, ellipse = TRUE, show.legend = FALSE # don’t display the legend of stat_ellipse ) + scale_fill_manual( #values = c(“#a6cee3”, “#1f78b4”, “#b2df8a”, “#33a02c”, “#fb9a99”, “#e31a1c”, “#cab2d6”, “#6a3d9a”), #values = c(“#a6cee3”, “#b2df8a”, “#fb9a99”, “#cab2d6”), values = c(“#1f78b4”, “#e31a1c”), guide = guide_legend(keywidth=1.6, keyheight=1.6, label.theme=element_text(size=12)) ) + scale_color_manual( #values=c(“#a6cee3”, “#1f78b4”, “#b2df8a”, “#33a02c”, “#fb9a99”, “#e31a1c”, “#cab2d6”, “#6a3d9a”), #values = c(“#a6cee3”, “#b2df8a”, “#fb9a99”, “#cab2d6”), values = c(“#1f78b4”, “#e31a1c”), guide = guide_legend(keywidth=1.6, keyheight=1.6, label.theme=element_text(size=12)) ) #scale_fill_manual(values=c(“#FF0000”, “#000000”, “#0000FF”, “#C0C0C0”, “#00FF00”, “#FFFF00”, “#00FFFF”, “#FFA500”)) + #scale_color_manual(values=c(“#FF0000”, “#000000”, “#0000FF”, “#C0C0C0”, “#00FF00”, “#FFFF00”, “#00FFFF”, “#FFA500”)) #scale_fill_manual(values=c(“#00A087FF”, “#3C5488FF”)) + #scale_color_manual(values=c(“#00A087FF”, “#3C5488FF”)) #png(“PCoA.png”, width= 1000, height=1000) #svg(“PCoA.svg”, width= 11, height=10) #svg(“PCoA_.svg”, width=10, height=10) #svg(“PCoA.svg”) pdf(“PCoA_Group2_vs_Group6.pdf”) p1 dev.off() #FF0000: Red #000000: Black #0000FF: Blue #C0C0C0: Silver #00FF00: Lime (often referred to simply as Green in web colors) #FFFF00: Yellow #00FFFF: Aqua (also known as Cyan) #FFA500: Orange # The size of point also can be mapped to other variables such as Observe, or Shannon # Then the alpha diversity and beta diversity will be displayed simultaneously. p2 <- mpse_abund %>% mp_plot_ord( .ord = pcoa, .group = Group, .color = Group, .size = Shannon, .alpha = Observe, ellipse = TRUE, show.legend = FALSE # don’t display the legend of stat_ellipse ) + scale_fill_manual( values = c(“#1f78b4”, “#e31a1c”), #only needs four colors. #values = c(“#FF0000”, “#000000”, “#0000FF”, “#C0C0C0”, “#00FF00”, “#FFFF00”, “#00FFFF”, “#FFA500”), guide = guide_legend(keywidth=0.6, keyheight=0.6, label.theme=element_text(size=8)) ) + scale_color_manual( values = c(“#1f78b4”, “#e31a1c”), #only needs four colors. #values=c(“#FF0000”, “#000000”, “#0000FF”, “#C0C0C0”, “#00FF00”, “#FFFF00”, “#00FFFF”, “#FFA500”), guide = guide_legend(keywidth=0.6, keyheight=0.6, label.theme=element_text(size=8)) ) + scale_size_continuous( range=c(0.5, 3), guide = guide_legend(keywidth=0.6, keyheight=0.6, label.theme=element_text(size=8)) ) pdf(“PCoA2_Group2_vs_Group6.pdf”) p2 dev.off() # —————————————— # 3.5.3 Hierarchical cluster (tree) analysis #input should contain hellinger! mpse_abund %<>% mp_cal_clust( .abundance = hellinger, distmethod = “bray”, hclustmethod = “average”, # (UPGAE) action = “add” # action is used to control which result will be returned ) mpse_abund mpse_abund %>% print(width=380, n=2) #NOTE mpse_abund contains 34 varibles, no new variable, the column bray has been new calculated! # if action = ‘add’, the result of hierarchical cluster will be added to the MPSE object # mp_extract_internal_attr can extract it. It is a treedata object, so it can be visualized # by ggtree. sample.clust <- mpse_abund %>% mp_extract_internal_attr(name=’SampleClust’) #The object contained internal attribute: PCoA ADONIS SampleClust sample.clust #–> The associated data tibble abstraction: 27 × 30 library(ggtree) p <- ggtree(sample.clust) + geom_tippoint(aes(color=Group)) + geom_tiplab(as_ylab = TRUE) + ggplot2::scale_x_continuous(expand=c(0, 0.01)) png("hierarchical_cluster1.png", width= 1000, height=800) p dev.off() #https://bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc//MicrobiotaProcess.html # mapping = aes(x = RelRareAbundanceBySample-->Group, # y = Sample–>Group, # fill = Phyla # ), library(ggtreeExtra) library(ggplot2) # Extract relative abundance of phyla phyla.tb <- mpse_abund %>% mp_extract_abundance(taxa.class=Phylum, topn=30) # The abundance of each samples is nested, it can be flatted using the unnest of tidyr. phyla.tb %<>% tidyr::unnest(cols=RareAbundanceBySample) %>% dplyr::rename(Phyla=”label”) phyla.tb phyla.tb %>% print(width=380, n=10) p1 <- p + geom_fruit( data=phyla.tb, geom=geom_col, mapping = aes(x = RelRareAbundanceBySample, y = Sample, fill = Phyla ), orientation = "y", #offset = 0.4, pwidth = 3, axis.params = list(axis = "x", title = "The relative abundance of phyla (%)", title.size = 4, text.size = 2, vjust = 1), grid.params = list() ) png("hierarchical_cluster2_Phyla.png", width = 1000, height = 800) p1 dev.off() # Extract relative abundance of classes class.tb <- mpse_abund %>% mp_extract_abundance(taxa.class = Class, topn = 30) # Flatten and rename the columns class.tb %<>% tidyr::unnest(cols = RareAbundanceBySample) %>% dplyr::rename(Class = “label”) # View the data frame class.tb # Create the plot p1 <- p + geom_fruit( data = class.tb, geom = geom_col, mapping = aes(x = RelRareAbundanceBySample, y = Sample, fill = Class ), orientation = "y", pwidth = 3, axis.params = list(axis = "x", title = "The relative abundance of classes (%)", title.size = 4, text.size = 2, vjust = 1), grid.params = list() ) # Save the plot to a file #ERROR-->NEED to be DEBUGGED! png(“hierarchical_cluster2_Class.png”, width = 1000, height = 800) print(p1) dev.off() # ———————– # 3.6 Biomarker discovery library(ggtree) library(ggtreeExtra) library(ggplot2) library(MicrobiotaProcess) library(tidytree) library(ggstar) library(forcats) mpse_abund %>% print(width=150) #mpse_abund %<>% # mp_cal_abundance( # for each samples # .abundance = RareAbundance # ) %>% # mp_cal_abundance( # for each groups # .abundance=RareAbundance, # .group=Group # ) #mpse_abund mpse_abund %<>% mp_diff_analysis( .abundance = RelRareAbundanceBySample, .group = Group, first.test.alpha = 0.01, filter.p=”pvalue” ) # The result is stored to the taxatree or otutree slot, you can use mp_extract_tree to extract the specific slot. taxa.tree <- mpse_abund %>% mp_extract_tree(type=”taxatree”) taxa.tree ## And the result tibble of different analysis can also be extracted ## with tidytree (>=0.3.5) taxa.tree %>% select(label, nodeClass, LDAupper, LDAmean, LDAlower, Sign_Group, pvalue, fdr) %>% dplyr::filter(!is.na(fdr)) taxa.tree %>% print(width=150, n=1) # Since taxa.tree is treedata object, it can be visualized by ggtree and ggtreeExtra p1 <- ggtree( taxa.tree, layout="radial", size = 0.3 ) + geom_point( data = td_filter(!isTip), fill="white", size=1, shape=21 ) # display the high light of phylum clade. p2 <- p1 + geom_hilight( data = td_filter(nodeClass == "Phylum"), mapping = aes(node = node, fill = label) ) # display the relative abundance of features(OTU) p3 <- p2 + ggnewscale::new_scale("fill") + geom_fruit( data = td_unnest(RareAbundanceBySample), geom = geom_star, mapping = aes( x = fct_reorder(Sample, Group, .fun=min), size = RelRareAbundanceBySample, fill = Group, subset = RelRareAbundanceBySample > 0 ), starshape = 13, starstroke = 0.25, offset = 0.03, pwidth = 0.4, grid.params = list(linetype=2) ) + scale_size_continuous( name=”Relative Abundance (%)”, range = c(.5, 3) ) + scale_fill_manual(values=c(“#1B9E77”, “#D95F02”)) # display the tip labels of taxa tree p4 <- p3 + geom_tiplab(size=6, offset=4.0) # display the LDA of significant OTU. #p5 <- p4 + # ggnewscale::new_scale("fill") + # geom_fruit( # geom = geom_col, # mapping = aes( # x = LDAmean, # fill = Sign_Group, # subset = !is.na(LDAmean) # ), # orientation = "y", # offset = 0.3, # pwidth = 0.5, # axis.params = list(axis = "x", # title = "Log10(LDA)", # title.height = 0.01, # title.size = 2, # text.size = 1.8, # vjust = 1), # grid.params = list(linetype = 2) # ) # display the significant (FDR-->pvalue) taxonomy after kruskal.test (default) #shape = 21, #scale_size_continuous(range=c(1, 3)) + p6 <- p4 + ggnewscale::new_scale("size") + geom_point( data=td_filter(!is.na(Sign_Group)), mapping = aes(size = -log10(pvalue), fill = Sign_Group, ), shape = 21, ) + scale_size_continuous(range=c(1, 4)) + scale_fill_manual(values=c("#1B9E77", "#D95F02")) svg("diff_analysis_Group2_vs_Group6.svg",width=22, height=22) #png("differently_expressed_otu.png", width=2000, height=2000) p6 + theme( legend.key.height = unit(1.0, "cm"), legend.key.width = unit(1.0, "cm"), legend.spacing.y = unit(0.01, "cm"), legend.text = element_text(size = 20), legend.title = element_text(size = 20) #legend.position = c(0.99, 0.01) ) dev.off() #-- save the data in an Excel-file -- #install.packages("tidyverse") #library(tidyverse) install.packages("dplyr") install.packages("readr") library(dplyr) library(readr) # Extracting different parts of mpse_abund, assuming these functions are applicable: abundance_data <- as.data.frame(mp_extract_abundance(mpse_abund)) taxonomy_data <- as.data.frame(mp_extract_taxonomy(mpse_abund)) # Joining the data together by a common identifier, assuming 'OTU' is present in both combined_data <- left_join(abundance_data, taxonomy_data, by = "OTU") # Install and load openxlsx package if not already installed if (!require(openxlsx)) { install.packages("openxlsx") library(openxlsx) } # Assuming `abundance_data` has lists that need flattening; example for one column: abundance_data <- abundance_data %>% mutate(RareAbundanceBySample = sapply(RareAbundanceBySample, function(x) paste(x, collapse = “, “))) %>% mutate(RareAbundanceByGroup = sapply(RareAbundanceByGroup, function(x) paste(x, collapse = “, “))) # Check the structure now str(abundance_data) # Rename the ‘label’ column in abundance_data to ‘OTU’ colnames(abundance_data)[colnames(abundance_data) == “label”] <- "OTU" # Merge the cleaned abundance data with taxonomy data combined_data <- left_join(abundance_data, taxonomy_data, by = "OTU") # Check the resulting data frame str(combined_data) library(openxlsx) # Write the data frame to an Excel file write.xlsx(combined_data, "diff_analysis_Group2_vs_Group6.xlsx") #delete RareAbundanceBySample! # ------ Deprecated ------ #.data, layout, tree.type, .taxa.class, tiplab.size, offset.abun, pwidth.abun, offset.effsize, pwidth.effsize, group.abun, tiplab.linetype p <- mpse_abund %>% mp_plot_diff_res( group.abun = FALSE, pwidth.abun=0.01, offset.abun=0.2, pwidth.effsize=0.01, offset.effsize=0.2, tiplab.size = 5.0, tiplab.linetype = 3 ) + scale_fill_manual(values=c(“deepskyblue”, “orange”)) + scale_fill_manual( aesthetics = “fill_new”, # The fill aes was renamed to “fill_new” for the abundance dotplot layer values = c(“deepskyblue”, “orange”) ) + scale_fill_manual( aesthetics = “fill_new_new”, # The fill aes for hight light layer of tree was renamed to ‘fill_new_new’ values = c(“#E41A1C”, “#377EB8”, “#4DAF4A”, “#984EA3”, “#FF7F00”, “#FFFF33”, “#A65628”, “#F781BF”, “#00FFFF”, “#999999” ) ) + theme( axis.title = element_text(size = 28), # Font size for axis titles axis.text = element_text(size = 28), # Font size for axis text plot.title = element_text(size = 16), # Font size for plot title legend.title = element_text(size = 16), # Font size for legend title legend.text = element_text(size = 14) # Font size for legend text ) #p$layers[[2]]$geom <- geom_tiplab(fontsize = 22) # Change 12 to the desired font size p <- mpse_abund %>% mp_plot_diff_res( group.abun = TRUE, pwidth.abun=0.05, offset.abun=0.04, pwidth.effsize=0.3, offset.effsize=0.6, tiplab.size = 4.9, barplot.x = -log10(pvalue) ) + scale_fill_manual(values=c(“deepskyblue”, “orange”)) + scale_fill_manual( aesthetics = “fill_new”, # The fill aes was renamed to “fill_new” for the abundance dotplot layer values = c(“deepskyblue”, “orange”) ) + scale_fill_manual( aesthetics = “fill_new_new”, # The fill aes for hight light layer of tree was renamed to ‘fill_new_new’ values = c(“#E41A1C”, “#377EB8”, “#4DAF4A”, “#984EA3”, “#FF7F00”, “#FFFF33”, “#A65628”, “#F781BF”, “#00FFFF”, “#999999” ) ) + theme( axis.title = element_text(size = 28), # Font size for axis titles axis.text = element_text(size = 28), # Font size for axis text plot.title = element_text(size = 28), # Font size for plot title legend.title = element_text(size = 16), # Font size for legend title legend.text = element_text(size = 14) # Font size for legend text ) #png(“differently_expressed_otu.png”, width=2000, height=2000) svg(“p7.svg”,width=22, height=22) p dev.off() p <- mpse_abund %>% mp_plot_diff_res( group.abun = FALSE, pwidth.abun=0.1, tiplab.size = 0.1, offset.effsize=0.1, pwidth.effsize=0.2 ) + scale_fill_manual(values=c(“deepskyblue”, “orange”)) + scale_fill_manual( aesthetics = “fill_new”, # The fill aes was renamed to “fill_new” for the abundance dotplot layer values = c(“deepskyblue”, “orange”) ) + scale_fill_manual( aesthetics = “fill_new_new”, # The fill aes for hight light layer of tree was renamed to ‘fill_new_new’ values = c(“#E41A1C”, “#377EB8”, “#4DAF4A”, “#984EA3”, “#FF7F00”, “#FFFF33”, “#A65628”, “#F781BF”, “#00FFFF”, “#999999” ) ) #svg(“p8.svg”,width=22, height=22) png(“differently_expressed_otu.png”, width=2000, height=2000) p dev.off() f <- mpse_abund %>% mp_plot_diff_cladogram( label.size = 2.5, hilight.alpha = .3, bg.tree.size = .5, bg.point.size = 2, bg.point.stroke = .25 ) + scale_fill_diff_cladogram( # set the color of different group. values = c(‘deepskyblue’, ‘orange’) ) + scale_size_continuous(range = c(1, 4)) #png(“cladogram.png”, width=1000, height=1000) svg(“cladogram.svg”, width=10, height=10) f dev.off() ## Extract the OTU table and taxonomy table from the phyloseq object otu_table <- phyloseq::otu_table(ps.ng.tax_abund) %>% as.data.frame() %>% as.matrix() tax_table <- phyloseq::tax_table(ps.ng.tax_abund) %>% as.data.frame() %>% as.matrix() write.csv(otu_table, file=”otu_table.csv”) write.csv(tax_table, file=”tax_table.csv”) #~/Tools/csv2xls-0.4/csv_to_xls.py otu_table.csv tax_table.csv -d’,’ -o otu_tax.xls

2. code of Phyloseq.Rmd

   author: ""
   date: '`r format(Sys.time(), "%d %m %Y")`'
   header-includes:
     - \usepackage{color, fancyvrb}
   output:
     rmdformats::readthedown:
       highlight: kate
       number_sections : yes
     pdf_document:
       toc: yes
       toc_depth: 2
       number_sections : yes
   ---
   
   ```{r, echo=FALSE, warning=FALSE}
   ## Global options
   # TODO: reproduce the html with the additional figure/SVN-files for editing.
   # IMPORTANT NOTE: needs before "mkdir figures"
   #rmarkdown::render('Phyloseq.Rmd',output_file='Phyloseq.html')
   ```
   
   ```{r load-packages, include=FALSE}
   library(knitr)
   library(rmdformats)
   library(readxl)
   library(dplyr)
   library(kableExtra)
   
   options(max.print="75")
   knitr::opts_chunk$set(fig.width=8,
                         fig.height=6,
                         eval=TRUE,
                         cache=TRUE,
                         echo=TRUE,
                         prompt=FALSE,
                         tidy=TRUE,
                         comment=NA,
                         message=FALSE,
                         warning=FALSE)
   opts_knit$set(width=85)
   # Phyloseq R library
   #* Phyloseq web site : https://joey711.github.io/phyloseq/index.html
   #* See in particular tutorials for
   #    - importing data: https://joey711.github.io/phyloseq/import-data.html
   #    - heat maps: https://joey711.github.io/phyloseq/plot_heatmap-examples.html
   ```
   
   # Data
   
   Import raw data and assign sample key:
   
   ```{r, echo=TRUE, warning=FALSE}
   #extend map_corrected.txt with Diet and Flora
   #setwd("~/DATA/Data_Laura_16S_2/core_diversity_e4753")
   map_corrected <- read.csv("map_corrected.txt", sep="\t", row.names=1)
   knitr::kable(map_corrected) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   ```
   
   # Prerequisites to be installed
   
   * R : https://pbil.univ-lyon1.fr/CRAN/
   * R studio : https://www.rstudio.com/products/rstudio/download/#download
   
   ```R
   install.packages("dplyr")     # To manipulate dataframes
   install.packages("readxl")    # To read Excel files into R
   install.packages("ggplot2")   # for high quality graphics
   install.packages("heatmaply")
   source("https://bioconductor.org/biocLite.R")
   biocLite("phyloseq")
   ```
   
   ```{r libraries, echo=TRUE, message=FALSE}
   library("readxl") # necessary to import the data from Excel file
   library("ggplot2") # graphics
   library("picante")
   library("microbiome") # data analysis and visualisation
   library("phyloseq") # also the basis of data object. Data analysis and visualisation
   library("ggpubr") # publication quality figures, based on ggplot2
   library("dplyr") # data handling, filter and reformat data frames
   library("RColorBrewer") # nice color options
   library("heatmaply")
   library(vegan)
   library(gplots)
   ```
   
   # Read the data and create phyloseq objects
   
   Three tables are needed
   
   * OTU
   * Taxonomy
   * Samples
   
   ```{r, echo=TRUE, warning=FALSE}
       #Change your working directory to where the files are located
       ps.ng.tax <- import_biom("./table_even42369.biom", "./clustering/rep_set.tre")
       sample <- read.csv("./map_corrected.txt", sep="\t", row.names=1)
       SAM = sample_data(sample, errorIfNULL = T)
       rownames(SAM) <-
       c("1","2","3","5","6","7","8","9","10","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","46","47","48","49","50","51","52","53","55")
       ps.ng.tax <- merge_phyloseq(ps.ng.tax, SAM)
       print(ps.ng.tax)
       colnames(tax_table(ps.ng.tax)) <- c("Domain","Phylum","Class","Order","Family","Genus","Species")
       saveRDS(ps.ng.tax, "./ps.ng.tax.rds")
   ```
   
   Visualize data
   ```{r, echo=TRUE, warning=FALSE}
     sample_names(ps.ng.tax)
     rank_names(ps.ng.tax)
     sample_variables(ps.ng.tax)
   ```
   
   Normalize number of reads in each sample using median sequencing depth.
   ```{r, echo=TRUE, warning=FALSE}
   # RAREFACTION
   #set.seed(9242)  # This will help in reproducing the filtering and nomalisation.
   #ps.ng.tax <- rarefy_even_depth(ps.ng.tax, sample.size = 42369)
   #total <- 42369
   
   # NORMALIZE number of reads in each sample using median sequencing depth.
   total = median(sample_sums(ps.ng.tax))
   #> total
   #[1] 42369
   standf = function(x, t=total) round(t * (x / sum(x)))
   ps.ng.tax = transform_sample_counts(ps.ng.tax, standf)
   ps.ng.tax_rel <- microbiome::transform(ps.ng.tax, "compositional")
   
   saveRDS(ps.ng.tax, "./ps.ng.tax.rds")
   hmp.meta <- meta(ps.ng.tax)
   hmp.meta$sam_name <- rownames(hmp.meta)
   ```
   
   # Heatmaps
   
   ```{r, echo=TRUE, warning=FALSE}
   #MOVE_FROM_ABOVE: The number of reads used for normalization is **`r sprintf("%.0f", total)`**.
   #A basic heatmap using the default parameters.
   #  plot_heatmap(ps.ng.tax, method = "NMDS", distance = "bray")
   #NOTE that giving the correct OTU numbers in the text (1%, 0.5%, ...)!!!
   ```
   
   We consider the most abundant OTUs for heatmaps. For example one can only take OTUs that represent at least 1% of reads in at least one sample. Remember we normalized all the sampples to median number of reads (total).  We are left with only 168 OTUS which makes the reading much more easy.
   ```{r, echo=TRUE, warning=FALSE}
   
   # Custom function to plot a heatmap with the specified sample order
   #plot_heatmap_custom <- function(ps, sample_order, method = "NMDS", distance = "bray") {
   ps.ng.tax_abund <- phyloseq::filter_taxa(ps.ng.tax, function(x) sum(x > total*0.01) > 0, TRUE)
   kable(otu_table(ps.ng.tax_abund)) %>%
   kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   # Calculate the relative abundance for each sample
   ps.ng.tax_abund_rel <- transform_sample_counts(ps.ng.tax_abund, function(x) x / sum(x))
   
   datamat_ = as.data.frame(otu_table(ps.ng.tax_abund))
   #datamat <- datamat_[c("1","2","5","6","7",  "8","9","10","12","13","14",    "15","16","17","18","19","20",  "21","22","23","24","25","26","27","28",    "29","30","31","32",  "33","34","35","36","37","38","39","51",    "40","41","42","43","44","46",  "47","48","49","50","52","53","55")]
   datamat <- datamat_[c("8","9","10","12","13","14",    "21","22","23","24","25","26","27","28",    "33","34","35","36","37","38","39","51",    "47","48","49","50","52","53","55")]
   hr <- hclust(as.dist(1-cor(t(datamat), method="pearson")), method="complete")
   hc <- hclust(as.dist(1-cor(datamat, method="spearman")), method="complete")
   mycl = cutree(hr, h=max(hr$height)/1.08)
   mycol = c("YELLOW", "DARKBLUE", "DARKORANGE", "DARKMAGENTA", "DARKCYAN", "DARKRED",  "MAROON", "DARKGREEN", "LIGHTBLUE", "PINK", "MAGENTA", "LIGHTCYAN","LIGHTGREEN", "BLUE", "ORANGE", "CYAN", "RED", "GREEN");
   
   mycol = mycol[as.vector(mycl)]
   sampleCols <- rep('GREY',ncol(datamat))
   #names(sampleCols) <- c("Group1", "Group1", "Group1", "Group1", "Group1",   "Group2", "Group2", "Group2", "Group2", "Group2","Group2",    "Group3", "Group3", "Group3", "Group3",   "Group1", "Group1", "Group1", "Group1", "Group1",   "Group1", "Group1", "Group1", "Group1", "Group1",  "Group1", "Group1", "Group1", "Group1", "Group1",  "Group1", "Group1", "Group1", "Group1", "Group1",  "Group1", "Group1", "Group1", "Group1", "Group1",    "Group1", "Group1", "Group1", "Group1", "Group1",  "Group1", "Group1", "Group1", "Group1", "Group1")
   
   #sampleCols[colnames(datamat)=='1'] <- '#a6cee3'
   #sampleCols[colnames(datamat)=='2'] <- '#a6cee3'
   #sampleCols[colnames(datamat)=='5'] <- '#a6cee3'
   #sampleCols[colnames(datamat)=='6'] <- '#a6cee3'
   #sampleCols[colnames(datamat)=='7'] <- '#a6cee3'
   
   sampleCols[colnames(datamat)=='8'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='9'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='10'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='12'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='13'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='14'] <- '#1f78b4'
   
   #sampleCols[colnames(datamat)=='15'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='16'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='17'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='18'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='19'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='20'] <- '#b2df8a'
   
   sampleCols[colnames(datamat)=='21'] <- '#33a02c'
   sampleCols[colnames(datamat)=='22'] <- '#33a02c'
   sampleCols[colnames(datamat)=='23'] <- '#33a02c'
   sampleCols[colnames(datamat)=='24'] <- '#33a02c'
   sampleCols[colnames(datamat)=='25'] <- '#33a02c'
   sampleCols[colnames(datamat)=='26'] <- '#33a02c'
   sampleCols[colnames(datamat)=='27'] <- '#33a02c'
   sampleCols[colnames(datamat)=='28'] <- '#33a02c'
   
   #sampleCols[colnames(datamat)=='29'] <- '#fb9a99'
   #sampleCols[colnames(datamat)=='30'] <- '#fb9a99'
   #sampleCols[colnames(datamat)=='31'] <- '#fb9a99'
   #sampleCols[colnames(datamat)=='32'] <- '#fb9a99'
   
   sampleCols[colnames(datamat)=='33'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='34'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='35'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='36'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='37'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='38'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='39'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='51'] <- '#e31a1c'
   
   #sampleCols[colnames(datamat)=='40'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='41'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='42'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='43'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='44'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='46'] <- '#cab2d6'
   
   sampleCols[colnames(datamat)=='47'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='48'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='49'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='50'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='52'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='53'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='55'] <- '#6a3d9a'
   #bluered(75)
   #color_pattern <- colorRampPalette(c("blue", "white", "red"))(100)
   library(RColorBrewer)
   custom_palette <- colorRampPalette(brewer.pal(9, "Blues"))
   heatmap_colors <- custom_palette(100)
   #colors <- heatmap_color_default(100)
   png("figures/heatmap.png", width=1200, height=2400)
   #par(mar=c(2, 2, 2, 2))  , lwid=1    lhei=c(0.7, 10)) # Adjust height of color keys   keysize=0.3,
   heatmap.2(as.matrix(datamat),Rowv=as.dendrogram(hr),Colv = NA, dendrogram = 'row',
               scale='row',trace='none',col=heatmap_colors, cexRow=1.2, cexCol=1.5,
               RowSideColors = mycol, ColSideColors = sampleCols, srtCol=15, labRow=row.names(datamat), key=TRUE, margins=c(10, 15), lhei=c(0.7, 15), lwid=c(1,8))
   dev.off()
   ```
   ```{r, echo=TRUE, warning=FALSE, fig.cap="Heatmap", out.width = '100%', fig.align= "center"}
   knitr::include_graphics("./figures/heatmap.png")
   ```
   
   ```{r, echo=FALSE, warning=FALSE}
     #It is possible to use different distances and different multivaraite methods. Many different built-in distances can be used.
     #dist_methods <- unlist(distanceMethodList)
     #print(dist_methods)
   ```
   
   \pagebreak
   
   # Taxonomic summary
   
   ## Bar plots in phylum level
   
   ```{r, echo=FALSE, warning=FALSE}
   #Make the bargraph nicer by removing OTUs boundaries. This is done by adding ggplot2 modifier.
   # 1: uniform color. Color is for the border, fill is for the inside
   #ggplot(mtcars, aes(x=as.factor(cyl) )) +
   #  geom_bar(color="blue", fill=rgb(0.1,0.4,0.5,0.7) )
   # 2: Using Hue
   #ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) +
   #  geom_bar( ) +
   #  scale_fill_hue(c = 40) +
   #  theme(legend.position="none")
   # 3: Using RColorBrewer
   #ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) +
   #  geom_bar( ) +
   #  scale_fill_brewer(palette = "Set1") +
   #  theme(legend.position="none")
   # 4: Using greyscale:
   #ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) +
   #  geom_bar( ) +
   #  scale_fill_grey(start = 0.25, end = 0.75) +
   #  theme(legend.position="none")
   # 5: Set manualy
   #ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) +
   #  geom_bar( ) +
   #  scale_fill_manual(values = c("red", "green", "blue") ) +
   #  theme(legend.position="none")
   #NOT SUCCESSFUL!
   #allGroupsColors<- c(
   #  "grey0", "grey50", "dodgerblu", "deepskyblue",
   #  "red", "darkred", "green", "green4")
   #  plot_bar(ps.ng.tax_rel, fill="Phylum") +
   #  geom_bar(stat="identity", position="stack") + scale_color_manual(values = allGroupsColors) #, fill=Phylum   + scale_fill_brewer(palette = "Set1")
     # ##### Keep only the most abundant phyla and
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Actinobacteria")) #1.57
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Bacteroidetes"))  #27.27436
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Cyanobacteria"))  #0.02244249
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Epsilonbacteraeota"))  #0.01309145
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Euryarchaeota"))  #0.1210024
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Firmicutes"))     #32.50589
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Lentisphaerae"))  #0.0001870208
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Patescibacteria")) #0.008789976
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Planctomycetes")) #0.01365252
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Proteobacteria")) #6.769216
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Synergistetes"))  #0.005049561
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Tenericutes"))    #0.0005610623
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Verrucomicrobia"))  #2.076304
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c(NA))  #sum(otu_table(ps.ng.tax_most)) = 2.619413
   ```
   ```{r, echo=TRUE, warning=FALSE}
     library(ggplot2)
     geom.text.size = 6
     theme.size = 8 #(14/5) * geom.text.size
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Actinobacteria", "D_1__Bacteroidetes", "D_1__Firmicutes", "D_1__Proteobacteria", "D_1__Verrucomicrobia", NA))
     ps.ng.tax_most = phyloseq::filter_taxa(ps.ng.tax_rel, function(x) mean(x) > 0.001, TRUE)
     #CONSOLE(OPTIONAL): for sampleid in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73; do
     #echo "otu_table(ps.ng.tax_most)[,${sampleid}]=otu_table(ps.ng.tax_most)[,${sampleid}]/sum(otu_table(ps.ng.tax_most)[,${sampleid}])" done
     #OR
     ps.ng.tax_most_ = transform_sample_counts(ps.ng.tax_most, function(x) x / sum(x))
   ```
   
   ```{r, echo=FALSE, warning=FALSE}
     ##--Creating 100% stacked bar plots with less abundant taxa in a sub-category #901--
     ##https://github.com/joey711/phyloseq/issues/901
     ##ps.ng.tax_most_df <- psmelt(ps.ng.tax_most_)  #5986x19
     #glom <- tax_glom(ps.ng.tax_most_, taxrank = 'Phylum')
     #tax_table(glom) # should list # taxa as # phyla
     #data <- psmelt(glom) # create dataframe from phyloseq object
     #data$Phylum <- as.character(data$Phylum) #convert to character
     ##simple way to rename phyla with < 1% abundance
     #data$Phylum[data$Abundance < 0.001] <- "< 0.1% abund."
     #
     #library(plyr)
     #medians <- ddply(data, ~Phylum, function(x) c(median=median(x$Abundance)))
     #remainder <- medians[medians$median <= 0.001,]$Phylum
     #data[data$Phylum %in% remainder,]$Phylum <- "Phyla < 0.1% abund."
     #data$Phylum[data$Abundance < 0.001] <- "Phyla < 0.1% abund."
     ##--> data are not used!
     #
     ##in class level
     #glom <- tax_glom(ps.ng.tax_most_, taxrank = 'Class')
     #tax_table(glom) # should list # taxa as # phyla
     #data <- psmelt(glom) # create dataframe from phyloseq object
     #data$Class <- as.character(data$Class) #convert to character
     #
     ##simple way to rename phyla with < 1% abundance
     #data$Class[data$Abundance < 0.001] <- "< 0.1% abund."
     #Count = length(unique(data$Class))
     #
     ##unique(data$Class)
     ##data$Class <- factor(data$Class, levels = c("Bacilli", "Bacteroidia", "Verrucomicrobiae", "Clostridia", "Gammaproteobacteria", "Alphaproteobacteria", "Actinobacteria", "Negativicutes", "Erysipelotrichia", "Methanobacteria", "< 0.1% abund."))
     ##------- Creating 100% stacked bar plots END --------
   
     library(stringr)
   #FITTING1:
   # tax_table(ps.ng.tax_most_)[1,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Domain"], "__")[[1]][2]
   # ... ...
   # tax_table(ps.ng.tax_most_)[167,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Species"], "__")[[1]][2]
   #ps.ng.tax_most_
   #in total [ 89 taxa and 55 samples ]
   #otu_table()   OTU Table:         [ 166 taxa and 54 samples ]
   #otu_table()   OTU Table:         [ 168 taxa and 50 samples ]
   #for id in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100  101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166; do
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Domain\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Domain\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Phylum\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Phylum\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Class\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Class\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Order\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Order\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Family\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Family\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Genus\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Genus\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Species\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Species\"], \"__\")[[1]][2]"
   #done
   
   tax_table(ps.ng.tax_most_)[1,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Species"], "__")[[1]][2]
   
   tax_table(ps.ng.tax_most_)[166,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Species"], "__")[[1]][2]
   
   tax_table(ps.ng.tax_most_)[167,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Species"], "__")[[1]][2]
   ```
   
   ```{r, echo=TRUE, warning=FALSE}
     #aes(color="Phylum", fill="Phylum") --> aes()
     #ggplot(data=data, aes(x=Sample, y=Abundance, fill=Phylum))
     plot_bar(ps.ng.tax_most_, fill="Phylum") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=2))                                  #6 instead of theme.size
   ```
   ```{r, echo=FALSE, warning=FALSE}
     #png("abc.png")
     #knitr::include_graphics("./Phyloseq_files/figure-html/unnamed-chunk-7-1.png")
     #dev.off()
   ```
   
   \pagebreak
   
   Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
   
   ```{r, echo=TRUE, warning=FALSE}
     ps.ng.tax_most_pre_post_stroke <- merge_samples(ps.ng.tax_most_, "pre_post_stroke")
     ps.ng.tax_most_pre_post_stroke_ = transform_sample_counts(ps.ng.tax_most_pre_post_stroke, function(x) x / sum(x))
     #plot_bar(ps.ng.tax_most_SampleType_, fill = "Phylum") + geom_bar(aes(color=Phylum, fill=Phylum), stat="identity", position="stack")
     plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Phylum") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = theme.size, colour="black"))
   ```
   
   \pagebreak
   
   Use color according to phylum. Do separate panels Stroke and Sex_age.
   ```{r, echo=TRUE, warning=FALSE}
     ps.ng.tax_most_copied <- data.table::copy(ps.ng.tax_most_)
     #FITTING6: regulate the bar height if it has replicates: 5+6+6+8+4+8+6+7=25+25=50
     otu_table(ps.ng.tax_most_)[,c("1")] <- otu_table(ps.ng.tax_most_)[,c("1")]/5
     otu_table(ps.ng.tax_most_)[,c("2")] <- otu_table(ps.ng.tax_most_)[,c("2")]/5
     otu_table(ps.ng.tax_most_)[,c("5")] <- otu_table(ps.ng.tax_most_)[,c("5")]/5
     otu_table(ps.ng.tax_most_)[,c("6")] <- otu_table(ps.ng.tax_most_)[,c("6")]/5
     otu_table(ps.ng.tax_most_)[,c("7")] <- otu_table(ps.ng.tax_most_)[,c("7")]/5
   
     otu_table(ps.ng.tax_most_)[,c("8")] <- otu_table(ps.ng.tax_most_)[,c("8")]/6
     otu_table(ps.ng.tax_most_)[,c("9")] <- otu_table(ps.ng.tax_most_)[,c("9")]/6
     otu_table(ps.ng.tax_most_)[,c("10")] <- otu_table(ps.ng.tax_most_)[,c("10")]/6
     otu_table(ps.ng.tax_most_)[,c("12")] <- otu_table(ps.ng.tax_most_)[,c("12")]/6
     otu_table(ps.ng.tax_most_)[,c("13")] <- otu_table(ps.ng.tax_most_)[,c("13")]/6
     otu_table(ps.ng.tax_most_)[,c("14")] <- otu_table(ps.ng.tax_most_)[,c("14")]/6
   
     otu_table(ps.ng.tax_most_)[,c("15")] <- otu_table(ps.ng.tax_most_)[,c("15")]/6
     otu_table(ps.ng.tax_most_)[,c("16")] <- otu_table(ps.ng.tax_most_)[,c("16")]/6
     otu_table(ps.ng.tax_most_)[,c("17")] <- otu_table(ps.ng.tax_most_)[,c("17")]/6
     otu_table(ps.ng.tax_most_)[,c("18")] <- otu_table(ps.ng.tax_most_)[,c("18")]/6
     otu_table(ps.ng.tax_most_)[,c("19")] <- otu_table(ps.ng.tax_most_)[,c("19")]/6
     otu_table(ps.ng.tax_most_)[,c("20")] <- otu_table(ps.ng.tax_most_)[,c("20")]/6
   
     otu_table(ps.ng.tax_most_)[,c("21")] <- otu_table(ps.ng.tax_most_)[,c("21")]/8
     otu_table(ps.ng.tax_most_)[,c("22")] <- otu_table(ps.ng.tax_most_)[,c("22")]/8
     otu_table(ps.ng.tax_most_)[,c("23")] <- otu_table(ps.ng.tax_most_)[,c("23")]/8
     otu_table(ps.ng.tax_most_)[,c("24")] <- otu_table(ps.ng.tax_most_)[,c("24")]/8
     otu_table(ps.ng.tax_most_)[,c("25")] <- otu_table(ps.ng.tax_most_)[,c("25")]/8
     otu_table(ps.ng.tax_most_)[,c("26")] <- otu_table(ps.ng.tax_most_)[,c("26")]/8
     otu_table(ps.ng.tax_most_)[,c("27")] <- otu_table(ps.ng.tax_most_)[,c("27")]/8
     otu_table(ps.ng.tax_most_)[,c("28")] <- otu_table(ps.ng.tax_most_)[,c("28")]/8
   
     otu_table(ps.ng.tax_most_)[,c("29")] <- otu_table(ps.ng.tax_most_)[,c("29")]/4
     otu_table(ps.ng.tax_most_)[,c("30")] <- otu_table(ps.ng.tax_most_)[,c("30")]/4
     otu_table(ps.ng.tax_most_)[,c("31")] <- otu_table(ps.ng.tax_most_)[,c("31")]/4
     otu_table(ps.ng.tax_most_)[,c("32")] <- otu_table(ps.ng.tax_most_)[,c("32")]/4
   
     otu_table(ps.ng.tax_most_)[,c("33")] <- otu_table(ps.ng.tax_most_)[,c("33")]/8
     otu_table(ps.ng.tax_most_)[,c("34")] <- otu_table(ps.ng.tax_most_)[,c("34")]/8
     otu_table(ps.ng.tax_most_)[,c("35")] <- otu_table(ps.ng.tax_most_)[,c("35")]/8
     otu_table(ps.ng.tax_most_)[,c("36")] <- otu_table(ps.ng.tax_most_)[,c("36")]/8
     otu_table(ps.ng.tax_most_)[,c("37")] <- otu_table(ps.ng.tax_most_)[,c("37")]/8
     otu_table(ps.ng.tax_most_)[,c("38")] <- otu_table(ps.ng.tax_most_)[,c("38")]/8
     otu_table(ps.ng.tax_most_)[,c("39")] <- otu_table(ps.ng.tax_most_)[,c("39")]/8
     otu_table(ps.ng.tax_most_)[,c("51")] <- otu_table(ps.ng.tax_most_)[,c("51")]/8
   
     otu_table(ps.ng.tax_most_)[,c("40")] <- otu_table(ps.ng.tax_most_)[,c("40")]/6
     otu_table(ps.ng.tax_most_)[,c("41")] <- otu_table(ps.ng.tax_most_)[,c("41")]/6
     otu_table(ps.ng.tax_most_)[,c("42")] <- otu_table(ps.ng.tax_most_)[,c("42")]/6
     otu_table(ps.ng.tax_most_)[,c("43")] <- otu_table(ps.ng.tax_most_)[,c("43")]/6
     otu_table(ps.ng.tax_most_)[,c("44")] <- otu_table(ps.ng.tax_most_)[,c("44")]/6
     otu_table(ps.ng.tax_most_)[,c("46")] <- otu_table(ps.ng.tax_most_)[,c("46")]/6
   
     otu_table(ps.ng.tax_most_)[,c("47")] <- otu_table(ps.ng.tax_most_)[,c("47")]/7
     otu_table(ps.ng.tax_most_)[,c("48")] <- otu_table(ps.ng.tax_most_)[,c("48")]/7
     otu_table(ps.ng.tax_most_)[,c("49")] <- otu_table(ps.ng.tax_most_)[,c("49")]/7
     otu_table(ps.ng.tax_most_)[,c("50")] <- otu_table(ps.ng.tax_most_)[,c("50")]/7
     otu_table(ps.ng.tax_most_)[,c("52")] <- otu_table(ps.ng.tax_most_)[,c("52")]/7
     otu_table(ps.ng.tax_most_)[,c("53")] <- otu_table(ps.ng.tax_most_)[,c("53")]/7
     otu_table(ps.ng.tax_most_)[,c("55")] <- otu_table(ps.ng.tax_most_)[,c("55")]/7
   
     #plot_bar(ps.ng.tax_most_swab_, x="Phylum", fill = "Phylum", facet_grid = Patient~RoundDay) + geom_bar(aes(color=Phylum, fill=Phylum), stat="identity", position="stack") + theme(axis.text = element_text(size = theme.size, colour="black"))
     plot_bar(ps.ng.tax_most_, x="Phylum", fill="Phylum", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=2))
   ```
   ```{r, echo=FALSE, warning=FALSE}
     #knitr::include_graphics("./Phyloseq_files/figure-html/unnamed-chunk-10-1.png")
     #> tax_table(carbom)
     #Taxonomy Table:     [205 taxa by 7 taxonomic ranks]:
     #     Domain      Supergroup       Division      Class
     #Otu001 "Eukaryota" "Archaeplastida" "Chlorophyta" "Mamiellophyceae"
     #       Order                      Family                 Genus
     #Otu001 "Mamiellales"              "Bathycoccaceae"       "Ostreococcus"
     #sample_data(ps.ng.tax)
   ```
   
   ## Bar plots in class level
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_copied, fill="Class") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=3))
   ```
   
   Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Class") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = theme.size, colour="black"))
   ```
   \pagebreak
   
   Use color according to class. Do separate panels Stroke and Sex_age.
   ```{r, echo=TRUE, warning=FALSE}
     #-- If existing replicates, to be processed as follows --
     plot_bar(ps.ng.tax_most_, x="Class", fill="Class", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=3))
   ```
   
   ## Bar plots in order level
   
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_copied, fill="Order") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=4))
   ```
   
   Regroup together pre vs post stroke and normalize number of reads in each group using median sequencing depth.
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Order") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = theme.size, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=4))
   ```
   \pagebreak
   
   Use color according to order. Do separate panels Stroke and Sex_age.
   ```{r, echo=TRUE, warning=FALSE}
     #FITTING7: regulate the bar height if it has replicates
     plot_bar(ps.ng.tax_most_, x="Order", fill="Order", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=4))
   ```
   
   ## Bar plots in family level
   
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_copied, fill="Family") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=8))
   ```
   
   Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Family") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = theme.size, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=8))
   ```
   \pagebreak
   
   Use color according to family. Do separate panels Stroke and Sex_age.
   ```{r, echo=TRUE, warning=FALSE}
     #-- If existing replicates, to be processed as follows --
     plot_bar(ps.ng.tax_most_, x="Family", fill="Family", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = 5, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=8))
   ```
   
   ```{r, echo=FALSE, warning=FALSE}
     #MOVE_FROM_ABOVE: ## Bar plots in genus level
     #MOVE_FROM_ABOVE: Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
     #plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Genus") + geom_bar(aes(), stat="identity", position="stack") +
     #scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = theme.size, colour="black")) + theme(legend.position="bottom")
   ```
   \pagebreak
   
   ```{r, echo=FALSE, warning=FALSE}
     #MOVE_FROM_ABOVE: Use color according to genus. Do separate panels Stroke and Sex_age.
     ##-- If existing replicates, to be processed as follows --
     #plot_bar(ps.ng.tax_most_, x="Genus", fill="Genus", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     #scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = 6, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=18))
   ```
   
   \pagebreak
   
   # Alpha diversity
   Plot Chao1 richness estimator, Observed OTUs, Shannon index, and Phylogenetic diversity.
   Regroup together samples from the same group.
   ```{r, echo=FALSE, warning=FALSE}
   # using rarefied data
   #FITTING2: CONSOLE:
   #gunzip table_even4753.biom.gz
   #alpha_diversity.py -i table_even42369.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering/rep_set.tre
   #gunzip table_even4753.biom.gz
   #alpha_diversity.py -i table_even4753.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering_stool/rep_set.tre
   #gunzip table_even4753.biom.gz
   #alpha_diversity.py -i table_even4753.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering_swab/rep_set.tre
   ```
   
   ```{r, echo=TRUE, warning=FALSE}
   hmp.div_qiime <- read.csv("adiv_even.txt", sep="\t")
   colnames(hmp.div_qiime) <- c("sam_name", "chao1", "observed_otus", "shannon", "PD_whole_tree")
   row.names(hmp.div_qiime) <- hmp.div_qiime$sam_name
   div.df <- merge(hmp.div_qiime, hmp.meta, by = "sam_name")
   div.df2 <- div.df[, c("Group", "chao1", "shannon", "observed_otus", "PD_whole_tree")]
   colnames(div.df2) <- c("Group", "Chao-1", "Shannon", "OTU", "Phylogenetic Diversity")
   #colnames(div.df2)
   options(max.print=999999)
   #27     H47 830.5000 5.008482 319               10.60177
   #FITTING4: if occuring "Computation failed in `stat_signif()`:not enough 'y' observations"
   #means: the patient H47 contains only one sample, it should be removed for the statistical p-values calculations.
   #delete H47(1)
   #div.df2 <- div.df2[-c(3), ]
   #div.df2 <- div.df2[-c(55,54, 45,40,39,27,26,25,1), ]
   knitr::kable(div.df2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   #https://uc-r.github.io/t_test
   #We can perform the test with t.test and transform our data and we can also perform the nonparametric test with the wilcox.test function.
   stat.test.Shannon <- compare_means(
   Shannon ~ Group, data = div.df2,
   method = "t.test"
   )
   knitr::kable(stat.test.Shannon) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   div_df_melt <- reshape2::melt(div.df2)
   #head(div_df_melt)
   
   #https://plot.ly/r/box-plots/#horizontal-boxplot
   #http://www.sthda.com/english/wiki/print.php?id=177
   #https://rpkgs.datanovia.com/ggpubr/reference/as_ggplot.html
   #http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/82-ggplot2-easy-way-to-change-graphical-parameters/
   #https://plot.ly/r/box-plots/#horizontal-boxplot
   #library("gridExtra")
   #par(mfrow=c(4,1))
   p <- ggboxplot(div_df_melt, x = "Group", y = "value",
                 facet.by = "variable",
                 scales = "free",
                 width = 0.5,
                 fill = "gray", legend= "right")
   #ggpar(p, xlab = FALSE, ylab = FALSE)
   lev <- levels(factor(div_df_melt$Group)) # get the variables
   #FITTING4: delete H47(1) in lev
   #lev <- lev[-c(3)]
   # make a pairwise list that we want to compare.
   #my_stat_compare_means
   #https://stackoverflow.com/questions/47839988/indicating-significance-with-ggplot2-in-a-boxplot-with-multiple-groups
   L.pairs <- combn(seq_along(lev), 2, simplify = FALSE, FUN = function(i) lev[i]) #%>% filter(p.signif != "ns")
   my_stat_compare_means  <- function (mapping = NULL, data = NULL, method = NULL, paired = FALSE,
       method.args = list(), ref.group = NULL, comparisons = NULL,
       hide.ns = FALSE, label.sep = ", ", label = NULL, label.x.npc = "left",
       label.y.npc = "top", label.x = NULL, label.y = NULL, tip.length = 0.03,
       symnum.args = list(), geom = "text", position = "identity",
       na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)
   {
       if (!is.null(comparisons)) {
           method.info <- ggpubr:::.method_info(method)
           method <- method.info$method
           method.args <- ggpubr:::.add_item(method.args, paired = paired)
           if (method == "wilcox.test")
               method.args$exact <- FALSE
           pms <- list(...)
           size <- ifelse(is.null(pms$size), 0.3, pms$size)
           color <- ifelse(is.null(pms$color), "black", pms$color)
           map_signif_level <- FALSE
           if (is.null(label))
               label <- "p.format"
           if (ggpubr:::.is_p.signif_in_mapping(mapping) | (label %in% "p.signif")) {
               if (ggpubr:::.is_empty(symnum.args)) {
                   map_signif_level <- c(`****` = 1e-04, `***` = 0.001,
                     `**` = 0.01, `*` = 0.05, ns = 1)
               } else {
                 map_signif_level <- symnum.args
               }
               if (hide.ns)
                   names(map_signif_level)[5] <- " "
           }
           step_increase <- ifelse(is.null(label.y), 0.12, 0)
           ggsignif::geom_signif(comparisons = comparisons, y_position = label.y,
               test = method, test.args = method.args, step_increase = step_increase,
               size = size, color = color, map_signif_level = map_signif_level,
               tip_length = tip.length, data = data)
       } else {
           mapping <- ggpubr:::.update_mapping(mapping, label)
           layer(stat = StatCompareMeans, data = data, mapping = mapping,
               geom = geom, position = position, show.legend = show.legend,
               inherit.aes = inherit.aes, params = list(label.x.npc = label.x.npc,
                   label.y.npc = label.y.npc, label.x = label.x,
                   label.y = label.y, label.sep = label.sep, method = method,
                   method.args = method.args, paired = paired, ref.group = ref.group,
                   symnum.args = symnum.args, hide.ns = hide.ns,
                   na.rm = na.rm, ...))
       }
   }
   
   p2 <- p +
   stat_compare_means(
     method="t.test",
     comparisons = list(c("Group1", "Group2"), c("Group1", "Group3"), c("Group1", "Group4"), c("Group1", "Group6"), c("Group1", "Group8"), c("Group2", "Group5"),c("Group4", "Group5"),c("Group4", "Group6"),c("Group4", "Group7"),c("Group6", "Group7")),
     label = "p.signif",
     symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns"))
   )
   #comparisons = L.pairs,
   #symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05), symbols = c("****", "***", "**", "*")),
   #stat_pvalue_manual
   #print(p2)
   #https://stackoverflow.com/questions/20500706/saving-multiple-ggplots-from-ls-into-one-and-separate-files-in-r
   #FITTING3: mkdir figures
   ggsave("./figures/alpha_diversity_Group.png", device="png", height = 10, width = 12)
   ggsave("./figures/alpha_diversity_Group.svg", device="svg", height = 10, width = 12)
   
   p3 <- p +
   stat_compare_means(
     method="t.test",
     comparisons = list(c("Group2", "Group4"), c("Group2", "Group6"), c("Group4", "Group8"), c("Group6", "Group8")),
     label = "p.signif",
     symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns")),
   )
   #symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05), symbols = c("****", "***", "**", "*")),
   #stat_pvalue_manual
   #print(p2)
   #https://stackoverflow.com/questions/20500706/saving-multiple-ggplots-from-ls-into-one-and-separate-files-in-r
   #FITTING3: mkdir figures
   ggsave("./figures/alpha_diversity_Group2.png", device="png", height = 10, width = 12)
   ggsave("./figures/alpha_diversity_Group2.svg", device="svg", height = 10, width = 12)
   ```
   
   # Selected alpha diversity
   ```{r, echo=TRUE, warning=FALSE, fig.cap="Alpha diversity", out.width = '100%', fig.align= "center"}
   knitr::include_graphics("./figures/alpha_diversity_Group2.png")
   
   selected_alpha_diversities<-read.csv("selected_alpha_diversities.txt",sep="\t")
   knitr::kable(selected_alpha_diversities) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   ```
   
   # Beta diversity
   ```{r, echo=TRUE, warning=FALSE, fig.cap="Beta diversity", out.width = '100%', fig.align= "center"}
   #file:///home/jhuang/DATA/Data_Marius_16S/core_diversity_e42369/bdiv_even42369_Group/unweighted_unifrac_boxplots/Group_Stats.txt
   beta_diversity_group_stats<-read.csv("unweighted_unifrac_boxplots_Group_Stats.txt",sep="\t")
   knitr::kable(beta_diversity_group_stats) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   #NOTE: Run this Phyloseq0.Rmd, then run the code of MicrobiotaProcess.R to manually generate PCoA.png, then run this Phyloseq.Rmd!
   #NOTE: AT_FIRST_DEACTIVATE_THIS_LINE: knitr::include_graphics("./figures/PCoA.png")
   ```
   
   # Differential abundance analysis
   
   Differential abundance analysis aims to find the differences in the abundance of each taxa between two groups of samples, assigning a significance value to each comparison.
   
   ## Group2 vs Group4
   
   ```{r, echo=TRUE, warning=FALSE}
   library("DESeq2")
   #ALTERNATIVE using ps.ng.tax_most_copied: ps.ng.tax (40594) vs. ps.ng.tax_most_copied (166)
   ps.ng.tax_sel <- ps.ng.tax
   #FITTING5: correct the id of the group members, see FITTING6
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("8","9","10","12","13","14",  "21","22","23","24","25","26","27","28")]
   diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~Group)
   diagdds$Group <- relevel(diagdds$Group, "Group4")
   diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
   resultsNames(diagdds)
   
   res = results(diagdds, cooksCutoff = FALSE)
   alpha = 0.05
   sigtab = res[which(res$padj < alpha), ]
   sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
   sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
   kable(sigtab) %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   #rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied))
   #subv %in% v
   ### returns a vector TRUE FALSE
   #is.element(subv, v)
   ### returns a vector TRUE FALSE
   
   library("ggplot2")
   theme_set(theme_bw())
   scale_fill_discrete <- function(palname = "Set1", ...) {
       scale_fill_brewer(palette = palname, ...)
   }
   x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
   x = sort(x)
   sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
   x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
   x = sort(x)
   sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
   ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
     theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
   
   #Error in checkForExperimentalReplicates(object, modelMatrix) :
   #  The design matrix has the same number of samples and coefficients to fit,
   #  so estimation of dispersion is not possible. Treating samples
   #  as replicates was deprecated in v1.20 and no longer supported since v1.22.
   ```
   
   ## Group2 vs Group6
   
   ```{r, echo=TRUE, warning=FALSE}
   ps.ng.tax_sel <- ps.ng.tax
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("8","9","10","12","13","14",    "33","34","35","36","37","38","39","51")]
   diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~Group)
   diagdds$Group <- relevel(diagdds$Group, "Group6")
   diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
   resultsNames(diagdds)
   
   res = results(diagdds, cooksCutoff = FALSE)
   alpha = 0.05
   sigtab = res[which(res$padj < alpha), ]
   sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
   sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
   kable(sigtab) %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   library("ggplot2")
   theme_set(theme_bw())
   scale_fill_discrete <- function(palname = "Set1", ...) {
       scale_fill_brewer(palette = palname, ...)
   }
   x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
   x = sort(x)
   sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
   x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
   x = sort(x)
   sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
   ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
     theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
   ```
   
   ## Group4 vs Group8
   
   ```{r, echo=TRUE, warning=FALSE}
   ps.ng.tax_sel <- ps.ng.tax
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("21","22","23","24","25","26","27","28",    "47","48","49","50","52","53","55")]
   diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~Group)
   diagdds$Group <- relevel(diagdds$Group, "Group8")
   diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
   resultsNames(diagdds)
   
   res = results(diagdds, cooksCutoff = FALSE)
   alpha = 0.05
   sigtab = res[which(res$padj < alpha), ]
   sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
   sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
   kable(sigtab) %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   library("ggplot2")
   theme_set(theme_bw())
   scale_fill_discrete <- function(palname = "Set1", ...) {
       scale_fill_brewer(palette = palname, ...)
   }
   x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
   x = sort(x)
   sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
   x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
   x = sort(x)
   sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
   ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
     theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
   ```
   
   ## Group6 vs Group8
   
   ```{r, echo=TRUE, warning=FALSE}
   ps.ng.tax_sel <- ps.ng.tax
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("33","34","35","36","37","38","39","51",    "47","48","49","50","52","53","55")]
   diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~Group)
   diagdds$Group <- relevel(diagdds$Group, "Group8")
   diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
   resultsNames(diagdds)
   
   res = results(diagdds, cooksCutoff = FALSE)
   alpha = 0.05
   sigtab = res[which(res$padj < alpha), ]
   sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
   sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
   kable(sigtab) %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   library("ggplot2")
   theme_set(theme_bw())
   scale_fill_discrete <- function(palname = "Set1", ...) {
       scale_fill_brewer(palette = palname, ...)
   }
   x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
   x = sort(x)
   sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
   x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
   x = sort(x)
   sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
   ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
     theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
   ```

# https://bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc//MicrobiotaProcess.html

# -----------------------------------
# ---- prepare the R environment ----
#Rscript MicrobiotaProcess.R
#NOTE: exit R script, then login again R-environment; rm -rf Phyloseq*_cache
#mkdir figures
rmarkdown::render('Phyloseq.Rmd',output_file='Phyloseq.html')

# with #alpha = 2.0, running the following script further!

# -----------------------------
# ---- 3.1. bridges other tools
##https://github.com/YuLab-SMU/MicrobiotaProcess
##https://www.bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html
##https://chiliubio.github.io/microeco_tutorial/intro.html#framework
##https://yiluheihei.github.io/microbiomeMarker/reference/plot_cladogram.html
#BiocManager::install("MicrobiotaProcess")
#install.packages("microeco")
#install.packages("ggalluvial")
#install.packages("ggh4x")

library(MicrobiotaProcess)
library(microeco)
library(ggalluvial)
library(ggh4x)
library(gghalves)

## Convert the phyloseq object to a MicrobiotaProcess object
#mp <- as.MicrobiotaProcess(ps.ng.tax)

#mt <- phyloseq2microeco(ps.ng.tax) #--> ERROR
#abundance_table <- mt$abun_table
#taxonomy_table <- mt$tax_table

#ps.ng.tax_abund <- phyloseq::filter_taxa(ps.ng.tax, function(x) sum(x > total*0.01) > 0, TRUE)
#ps.ng.tax_most = phyloseq::filter_taxa(ps.ng.tax_rel, function(x) mean(x) > 0.001, TRUE)

##OPTION1 (NOT_USED): take all samples, prepare ps.ng.tax_abund --> mpse_abund
##mpse <- ps.ng.tax %>% as.MPSE()
#mpse_abund <- ps.ng.tax_abund %>% as.MPSE()

##OPTION2 (USED!): take partial samples, prepare ps.ng.tax or ps.ng.tax_abund (2 replacements!)--> ps.ng.tax_sel --> mpse_abund
ps.ng.tax_sel <- ps.ng.tax_abund

##otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("1","2","5","6","7",  "15","16","17","18","19","20",  "29","30","31","32",  "40","41","42","43","44","46")]
##NOTE: Only choose Group2, Group4, Group6, Group8
#> ps.ng.tax_sel
#otu_table()   OTU Table:         [ 37465 taxa and 29 samples ]
#sample_data() Sample Data:       [ 29 samples by 10 sample variables ]
#tax_table()   Taxonomy Table:    [ 37465 taxa by 7 taxonomic ranks ]
#phy_tree()    Phylogenetic Tree: [ 37465 tips and 37461 internal nodes ]
#-Group4: "21","22","23","24","25","26","27","28",
#-Group8: ,  "47","48","49","50","52","53","55"
otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax_abund)[,c("8","9","10","12","13","14",  "33","34","35","36","37","38","39","51")]
mpse_abund <- ps.ng.tax_sel %>% as.MPSE()
# A MPSE-tibble (MPSE object) abstraction: 2,352 × 20
# NOTE mpse_abund contains 20 variables: OTU, Sample, Abundance, BarcodeSequence, LinkerPrimerSequence, FileInput, Group,
#   Sex_age 

0. Search for the reference complete genome at https://www.ncbi.nlm.nih.gov/genomes/GenomesGroup.cgi: Enterovirus type B sequences, Parvovirus B19 sequences, Rhinovirus A sequences and Parainfluenza type 3

   #- Sort and Index the BAM File:
   #    samtools sort -o sorted.bam input.bam
   #    samtools index sorted.bam
   #- Call Variants:
   #    bcftools mpileup -Ou -f reference.fasta sorted.bam | bcftools call -mv -Oz -o calls.vcf.gz
   #    bcftools index calls.vcf.gz
   #- Generate Consensus FASTA:
   #    bcftools consensus -f reference.fasta calls.vcf.gz -o consensus.fasta

1. Align Reads to Reference Genome

   First, align the reads from isolate 2 to the genome of isolate 1. For example, using BWA:

   bwa index Enterovirus_B.fasta
   bwa index Parvovirus_B19.fasta
   bwa index Rhinovirus_A.fasta
   bwa index Parainfluenza_3.fasta
   #bwa mem Enterovirus_B.fasta isolate2_reads.fastq > aligned.sam
   #bwa mem Enterovirus_B.fasta isolate2_reads_1.fastq isolate2_reads_2.fastq > aligned.sam
   bwa mem -t 64 Enterovirus_B.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_1/AW_S22_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_1/AW_S22_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_AW_on_Enterovirus_B.bam
   bwa mem -t 64 Enterovirus_B.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_2/GE_S23_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_2/GE_S23_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_GE_on_Enterovirus_B.bam
   bwa mem -t 64 Enterovirus_B.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_3/IF_S24_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_3/IF_S24_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_IF_on_Enterovirus_B.bam
   bwa mem -t 64 Enterovirus_B.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/42_S1_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/42_S1_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_42_S1_on_Enterovirus_B.bam
   bwa mem -t 64 Enterovirus_B.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/43_S2_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/43_S2_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_43_S2_on_Enterovirus_B.bam
   bwa mem -t 64 Enterovirus_B.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/44_S3_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/44_S3_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_44_S3_on_Enterovirus_B.bam
   
   bwa mem -t 64 Parvovirus_B19.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_1/AW_S22_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_1/AW_S22_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_AW_on_Parvovirus_B19.bam
   bwa mem -t 64 Parvovirus_B19.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_2/GE_S23_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_2/GE_S23_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_GE_on_Parvovirus_B19.bam
   bwa mem -t 64 Parvovirus_B19.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_3/IF_S24_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_3/IF_S24_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_IF_on_Parvovirus_B19.bam
   bwa mem -t 64 Parvovirus_B19.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/42_S1_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/42_S1_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_42_S1_on_Parvovirus_B19.bam
   bwa mem -t 64 Parvovirus_B19.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/43_S2_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/43_S2_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_43_S2_on_Parvovirus_B19.bam
   bwa mem -t 64 Parvovirus_B19.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/44_S3_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/44_S3_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_44_S3_on_Parvovirus_B19.bam
   
   bwa mem -t 64 Rhinovirus_A.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_1/AW_S22_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_1/AW_S22_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_AW_on_Rhinovirus_A.bam
   bwa mem -t 64 Rhinovirus_A.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_2/GE_S23_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_2/GE_S23_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_GE_on_Rhinovirus_A.bam
   bwa mem -t 64 Rhinovirus_A.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_3/IF_S24_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_3/IF_S24_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_IF_on_Rhinovirus_A.bam
   bwa mem -t 64 Rhinovirus_A.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/42_S1_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/42_S1_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_42_S1_on_Rhinovirus_A.bam
   bwa mem -t 64 Rhinovirus_A.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/43_S2_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/43_S2_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_43_S2_on_Rhinovirus_A.bam
   bwa mem -t 64 Rhinovirus_A.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/44_S3_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/44_S3_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_44_S3_on_Rhinovirus_A.bam
   
   bwa mem -t 64 Parainfluenza_3.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_1/AW_S22_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_1/AW_S22_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_AW_on_Parainfluenza_3.bam
   bwa mem -t 64 Parainfluenza_3.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_2/GE_S23_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_2/GE_S23_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_GE_on_Parainfluenza_3.bam
   bwa mem -t 64 Parainfluenza_3.fasta ./240822_VH00358_106_AAFKTG7M5/p20562_3/IF_S24_R1_001.fastq.gz ./240822_VH00358_106_AAFKTG7M5/p20562_3/IF_S24_R2_001.fastq.gz | samtools view -Sb - > MiSeq_p20562_IF_on_Parainfluenza_3.bam
   bwa mem -t 64 Parainfluenza_3.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/42_S1_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/42_S1_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_42_S1_on_Parainfluenza_3.bam
   bwa mem -t 64 Parainfluenza_3.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/43_S2_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/43_S2_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_43_S2_on_Parainfluenza_3.bam
   bwa mem -t 64 Parainfluenza_3.fasta ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/44_S3_L001_R1_001.fastq.gz ./20240822_FS10003086_54_BTR99504-1132/Alignment_Imported_1/20240823_184235/Fastq/44_S3_L001_R2_001.fastq.gz | samtools view -Sb - > iSeq_44_S3_on_Parainfluenza_3.bam

   or, use vrap to generate input.bam

   #for bowtie (1 DB): --host Bacillus_cereus.fasta
   #for blast (5 DBs): --virus=Acinetobacter_baumannii.fasta -n /mnt/h1/jhuang/blast/nt -a /mnt/h1/jhuang/blast/nr, and two default download_db.py
   #A6_chr_plasmid.fasta reads filtered, should only the contamination reads remaining!
   vrap/vrap.py -1 ./results_ATCC19606/trimmed/A10_CraA_HQ_trimmed_P_1.fastq.gz -2 ./results_ATCC19606/trimmed/A10_CraA_HQ_trimmed_P_2.fastq.gz -o A10_vrap_host_A6 --host A6_chr_plasmid.fasta --noblast -t 55

2. Convert SAM to BAM, Sort and Index

   Convert the SAM file to BAM, sort it, and create an index:

   samtools view -Sb mapped > aligned.bam
   samtools sort aligned.bam -o sorted.bam
   samtools index sorted.bam

3. Coverage Analysis

   Check the coverage of the genome regions. Regions with no coverage will be masked later.

   #bedtools genomecov -ibam sorted.bam -bg > coverage.bed
   #BUG above: By default, Bedtools doesn't explicitly list regions with zero coverage in the output of genomecov. To get around this, you can use the -bga option with genomecov. This option ensures that regions with zero coverage are also included in the output. Here's how you can modify your command:
   bedtools genomecov -ibam sorted.bam -bga > coverage.bed

4. Create a Bed File for Masking

   Create a BED file that specifies regions with no or low coverage. Assume that any region with coverage less than a certain threshold (e.g., 1x) should be masked:

   awk '$4 < 1 {print $1 "\t" $2 "\t" $3}' coverage.bed > low_coverage.bed

5. Generate Consensus Sequence

   Generate a consensus sequence, using bcftools, and mask regions with low or no coverage:

   bcftools mpileup -Ou -f ../../CP059040.fasta sorted.bam | bcftools call -mv --ploidy 1 -Oz -o calls.vcf.gz
   bcftools index calls.vcf.gz
   bcftools consensus -f ../../CP059040.fasta -m low_coverage.bed calls.vcf.gz -o consensus.fasta
   #The site CP059040:4 overlaps with another variant, skipping...
   #The site CP059040:3125036 overlaps with another variant, skipping...
   #Applied 58 variants
   
   bcftools mpileup -Ou -f ../../A6_chr_plasmid.fasta sorted.bam | bcftools call -mv --ploidy 1 -Oz -o calls.vcf.gz
   bcftools index calls.vcf.gz
   bcftools consensus -f ../../A6_chr_plasmid.fasta -m low_coverage.bed calls.vcf.gz -o consensus.fasta
   #The site seq00000000:4 overlaps with another variant, skipping...
   #The site seq00000000:3125036 overlaps with another variant, skipping...
   #Applied 61 variants
   #--> TODO: could report the following regions are not covered in A10 comparing to A6.
   #seq00000000     364666  365765
   #seq00000000     2810907 2813323
   #seq00000000     2813552 2813779
   #seq00000000     2813923 2819663
   #seq00000000     2821839 2831921
   #seq00000000     2832750 2832753
   #seq00000000     2833977 2860354
   #seq00000000     2860505 2861780
   #seq00000000     2861931 2861951
   #seq00000000     2862102 2862234
   #seq00000000     2862385 2863434
   #seq00000000     3124939 3124943
   #seq00000000     3125009 3125010

   In this command, the -m option in bcftools consensus is used to specify a BED file for masking low-coverage regions.

6. Coverage plot

   for sample in 4132-Leber_CAP28 4132-Leber_CAP45 4132-Leber_NODE328; do
       bowtie2-build ${sample}.fasta ${sample}.fasta
       bowtie2 -1 ../trimmed_paired/4132-Leber_S3_L001_R1.fastq.gz -2 ../trimmed_paired/4132-Leber_S3_L001_R2.fastq.gz -x ${sample}.fasta --fast --threads 16 -S ${sample}.sam
       samtools view -h -Sb ${sample}.sam > ${sample}.bam
       samtools sort ${sample}.bam > ${sample}_sorted.bam
       samtools index ${sample}_sorted.bam
       samtools depth -d 1000000 ${sample}_sorted.bam | gzip > ${sample}.cov.gz
       ~/Tools/damian_extended/lib_py/vipr_cov_pdf_sample.py -c ${sample}.cov.gz -p ${sample}.pdf
   done
   
   #calculate average
   samtools depth pool_rind_9bis26_94_S7_sorted.bam | awk '{sum+=$3} END { print "Average = ",sum/NR}'
   #and with stdev:
   samtools depth pool_rind_9bis26_94_S7_sorted.bam | awk '{sum+=$3; sumsq+=$3*$3} END { print "Average = ",sum/NR; print "Stdev = ",sqrt(sumsq/NR - (sum/NR)**2)}'

7. SNVs table + plots

   --R drawing plot--
   par(mfrow=c(1,1))
   cl <- rainbow(12)
   #x=c(438382,30718,15216,68712,41092,28916,107922,428720,57002,171788,2458486,113470,21348,276406,13329824,1785322,159416,9282986,68990)
   #y=c(0.96, 0.33, 0.11, 0.62,0.72,0.05,0.86,0.96,0.76,0.19,0.99,0.85,0.29,0.94,0.99,0.98,0.91,0.99,0.80)
   #x=c(581226,238044,151492,305816,185102,249346,269852,603476,203016,167814,2590728,291946,191906,404050,13448328,1938032,295890,9387234,232008)
   #x=c(423036,10004,1682,42520,29618,1460,93110,412024,43440,3320,2422088,95996,6168,260932,13222572,1747544,144448,9199232,55275)
   #y=c(0.73,0.04,0.01,0.14,0.16,  0.01,0.35,0.68,0.21,0.02,  0.93,0.33,0.03,0.65,0.98,  0.90,0.49,0.98,0.24)
   x=c(182419,3769,970,15974,18432,563,25898,188666,13129,479,598522,12672,3272,21948,5832429,121861,942,32889,4851987,25907)
   y=c(0.314,0.016,0.006,0.232,0.099,  0.002,0.096,0.313,0.065,0.003,  0.231,0.043,0.017,0.054,0.434,  0.063,0.005,0.111,0.517,0.112)
   logmod <- lm(log(y)~log(x))
   logypred <- predict(logmod)
   png(filename = "HCV_no_vs_enrichment.png", width = 800, height = 800)
   plot(y~x,log="xy",pch=1,type="p",cex=2,col=cl[1],main="", xlab="Number of HCV reads",ylab="Enrichment rate",las=1,xaxt="n",yaxt="n")
   points(y2~x,pch=8,type="p",cex=2,col=cl[8],las=1,xaxt="n",yaxt="n")
   lines(exp(logypred)~x, col=cl[1], lwd=2)
   
   #mtext(quote(X),1,line=3,cex=2)
   #mtext(quote(P(X)),2,line=4,cex=2)
   sfsmisc::eaxis(1,at=c(10^3,10^4,10^5,10^6,10^7),cex.axis=1.5)
   sfsmisc::eaxis(2,at=c(10^-1,10^0,10^1,10^2,10^3,10^4,10^5),cex.axis=1.5)
   #fit
   #legend(3000,6000, c("PE","SE","PE_unenriched"), lty=c(1,1),col=c(cl[1],cl[8],cl[5]))
   legend(3000,100, c("PE","SE"), lty=c(1,1),col=c(cl[1],cl[8]))
   legend("topright", bty="n", legend=paste("R2 =", format(summary(logmod)$adj.r.squared, digits=4)))
   dev.off()
   
   mkdir phylogenetics; cd phylogenetics
   
   mkdir assembly; cd assembly
   for sample in rsv-on1-h1-e1231-05ng 84660-tsek-50ng; do
       cp ~/rtpd_files/${sample}/idba_ud_assembly/gapped_contig.fa ${sample}.fa
   done
   
   #mkdir fasta_summary; cd fasta_summary
   #for sample in 838_S1 840_S2 820_S3 828_S4 815_S5 834_S6 808_S7 811_S8 837_S9 768_S10 773_S11 767_S12 810_S13 814_S14 10121-16_S15 7510-15_S16   8806-15_S18 9881-16_S19 8981-14_S20; do
   #   cp ../viral-ngs/data/02_assembly/${sample}.fasta ${sample}.fa     #828-17_S17 not inclusive, since they rRNA.
   #done
   cat 838_S1.fa 840_S2.fa 820_S3.fa 828_S4.fa 815_S5.fa 834_S6.fa 808_S7.fa 811_S8.fa 837_S9.fa 768_S10.fa 773_S11.fa 767_S12.fa 810_S13.fa 814_S14.fa 10121-16_S15.fa 7510-15_S16.fa  828-17_S17.fa  8806-15_S18.fa 9881-16_S19.fa 8981-14_S20.fa > all.fa
   cat
   mafft --adjustdirection --clustalout all.fa > all.aln
   cons
   mv all.fasta consensus.fa
   
   #under ~/rtpd_files/
   for sample in rsv-on1-h1-e1231-05ng 84660-tsek-50ng; do
   seqtk comp ${sample}/idba_ud_assembly/gapped_contig.fa | awk '{x=$3+$4+$5+$6;y=$2;print $1,y,x,y-x,(y-x)/y}'
   done
   
   #--
   mkdir interhost_variants_ref_p947; cd interhost_variants_ref_p947
   
   #mkdir multialign_to_ref; cd multialign_to_ref
   #mafft --adjustdirection --clustalout ref_plus_samples.fa > ref_plus_samples.aln
   
   #--
   mkdir intrahost_variants; cd intrahost_variants
   for sample in p946 p954 p952 p948 p945 p947 p955 p943 p951 p942  p944 p938 p953 p940; do
       cp ~/rtpd_files/${sample}_dedup/vphaser2_cut_filtered_groupref.annot.csv ${sample}_dedu
       cp ~/rtpd_files/${sample}/vphaser2_cut_filtered_groupref.annot.csv ${sample}_du
       #grep "#Chrom" ${sample}_dedu > ${sample}_dedup
       #grep "#Chrom" ${sample}_du > ${sample}_dup
       echo -e "#Chrom\tPosition\tREF\tAllele\tFrequency\tCoverage\tAnnotation\tAnnotation_Impact\tGene_Name\tFeature_Type\tTranscript_BioType\tCodon_Change\tAmino_Acid_Change\tCDS.pos/CDS.length\tAA.pos/AA.length" > ${sample}_dedup
       echo -e "#Chrom\tPosition\tREF\tAllele\tFrequency\tCoverage\tAnnotation\tAnnotation_Impact\tGene_Name\tFeature_Type\tTranscript_BioType\tCodon_Change\tAmino_Acid_Change\tCDS.pos/CDS.length\tAA.pos/AA.length" > ${sample}_dup
       awk 'length($3) == 1 && length($4) == 1' ${sample}_dedu >> ${sample}_dedup
   awk 'length($3) == 1 && length($4) == 1' ${sample}_du >> ${sample}_dup
   done
   mv p938_dedup _p938_dedup
   ~/Tools/csv2xls-0.4/csv_to_xls.py p946_dup p954_dup p952_dup p948_dup p945_dup p947_dup p955_dup p943_dup p951_dup p942_dup  p944_dup p938_dup p953_dup p940_dup  p946_dedup p954_dedup p952_dedup p948_dedup p945_dedup p947_dedup p955_dedup p943_dedup p951_dedup p942_dedup  p944_dedup _p938_dedup p953_dedup p940_dedup  -d$'\t' -o  SNVs.xls
   
   #---- collect plots and bams for final results ----
   #under ~/DATA/Data_Pietschmann_RSV_Probe/
   # replot plot if vphaser2_cut_filtered_groupref.vcf is empty.
   gzip vphaser2_cut_filtered_groupref.vcf
   ~/Tools/damian_extended/lib_py/vipr_af_vs_cov_pdf_1sample.py --vcf vphaser2_cut_filtered_groupref.vcf.gz --cov cov_groupref.gz --plot af-vs-cov_groupref.svg
   inkscape -z -e af-vs-cov_groupref.png af-vs-cov_groupref.svg -w 1600
   gunzip vphaser2_cut_filtered_groupref.vcf.gz
   
   for sample in p946 p954 p952 p948 p945 p947 p955 p943 p951 p942  p944 p938 p953 p940; do
   cp ~/rtpd_files/${sample}_dedup/af-vs-cov_groupref.png ${sample}_dedup.png
   cp ~/rtpd_files/${sample}/af-vs-cov_groupref.png ${sample}_dup.png
   done
   
   #under DIR results
   mkdir bams; cd bams
   for sample in p946 p954 p952 p948 p945 p947 p955 p943 p951 p942  p944 p938 p953 p940; do
   cp ~/rtpd_files/${sample}_dedup/mapped.bam ${sample}_mapped_dedup.bam
   done
   for sample in p946 p954 p952 p948 p945 p947 p955 p943 p951 p942  p944 p938 p953 p940; do
   cp ~/rtpd_files/${sample}/mapped.bam ${sample}_mapped_dup.bam
   done
   for sample in p946 p954 p952 p948 p945 p947 p955 p943 p951 p942  p944 p938 p953 p940; do
   samtools index ${sample}_mapped_dedup.bam
   samtools index ${sample}_mapped_dup.bam
   done
   
   #---- phylogenetic tree using ML ----
   "" > all.fa
   for sample in p946 p954 p952 p948 p945 p947 p955 p943 p951 p942  p944 p938 p953 p940; do
   cp ~/rtpd_files/${sample}/idba_ud_assembly/polished_contig.fa ${sample}.fa
   cp ../data/02_assembly/REF${sample}/REF${sample}.gbf ${sample}.gbk
   cat ~/rtpd_files/${sample}/idba_ud_assembly/polished_contig.fa >> all.fa
   done
   mafft --adjustdirection all.fa > all_aln.fa
   raxml-ng --all --model GTR+G --prefix RSV_GTR_G --threads 1 --msa all_aln.fa --bs-trees 200 --redo
   raxml-ng --all --model GTR+G+I --prefix RSV_GTR_G_I --threads 1 --msa all_aln.fa --bs-trees 1000 --redo
   #GTR+G+I --> GTR+FO+I+G4m
   #GTR+G -->  GTR+FO+G4m
   
   -- python merging plots--
   #under viral3
   import cv2 as cv
   from matplotlib import pyplot as plt
   import numpy as np
   import subprocess
   image1 = cv.imread('p955_dup.png')
   image2 = cv.imread('p943_dup.png')
   image3 = cv.imread('p954_dup.png')
   image4 = cv.imread('p946_dup.png')
   image5 = cv.imread('p942_dup.png')
   image6 = cv.imread('p945_dup.png')
   image7 = cv.imread('p948_dup.png')
   image8 = cv.imread('p947_dup.png')
   image9 = cv.imread('p951_dup.png')
   image10 = cv.imread('p952_dup.png')
   image11 = cv.imread('p944_dup.png')
   image12 = cv.imread('p938_dup.png')
   image13 = cv.imread('p953_dup.png')
   image14 = cv.imread('p940_dup.png')
   final = np.concatenate((image1, image2, image3, image4, image5, image6, image7, image8, image9, image10, image11, image12, image13, image14), axis = 0)
   cv.imwrite("coverages_and_SNVs_dup.png", final)
   
   import cv2 as cv
   from matplotlib import pyplot as plt
   import numpy as np
   import subprocess
   image1 = cv.imread('p3/af-vs-cov_groupref.png')
   image2 = cv.imread('p4/af-vs-cov_groupref.png')
   image3 = cv.imread('p5_700ng/af-vs-cov_groupref.png')
   final = np.concatenate((image1, image2, image3), axis = 0)
   cv.imwrite("coverages_and_SNVs_dedup_HEV.png", final)
   
   import cv2 as cv
   from matplotlib import pyplot as plt
   import numpy as np
   import subprocess
   image1 = cv.imread('p955_dedup.png')
   image2 = cv.imread('p943_dedup.png')
   image3 = cv.imread('p954_dedup.png')
   image4 = cv.imread('p946_dedup.png')
   image5 = cv.imread('p942_dedup.png')
   image6 = cv.imread('p945_dedup.png')
   image7 = cv.imread('p948_dedup.png')
   image8 = cv.imread('p947_dedup.png')
   image9 = cv.imread('p951_dedup.png')
   image10 = cv.imread('p952_dedup.png')
   image11 = cv.imread('p944_dedup.png')
   image12 = cv.imread('p938_dedup.png')
   image13 = cv.imread('p953_dedup.png')
   image14 = cv.imread('p940_dedup.png')
   final = np.concatenate((image1, image2, image3, image4, image5, image6, image7, image8, image9, image10, image11, image12, image13, image14), axis = 0)
   cv.imwrite("coverages_and_SNVs_dedup.png", final)
   
   rm p*dup.png
   
   image1 = cv.imread('838_S1.png')
   image2 = cv.imread('840_S2.png')
   image3 = cv.imread('837_S9.png')
   final = np.concatenate((image1, image2, image3), axis = 0)
   cv.imwrite("genotype_4d.png", final)
   
   image1 = cv.imread('820_S3.png')
   image2 = cv.imread('808_S7.png')
   image3 = cv.imread('811_S8.png')
   image4 = cv.imread('810_S13.png')
   image5 = cv.imread('10121-16_S15.png')
   image6 = cv.imread('7510-15_S16.png')
   image7 = cv.imread('9881-16_S19.png')
   final = np.concatenate((image1, image2, image3, image4, image5, image6, image7), axis = 0)
   cv.imwrite("genotype_3a.png", final)
   
   image1 = cv.imread('768_S10.png')
   image2 = cv.imread('773_S11.png')
   image3 = cv.imread('814_S14.png')
   image4 = cv.imread('8806-15_S18.png')
   final = np.concatenate((image1, image2, image3, image4), axis = 0)
   cv.imwrite("genotype_1a.png", final)

The Python script creates a co-authorship network using NetworkX, where:

• Nodes are authors.
• Edges represent co-authorship.

1. It visualizes the network using matplotlib.

   import networkx as nx
   import matplotlib.pyplot as plt
   import bibtexparser
   import re
   
   # Helper function to clean special characters from strings
   def clean_string(s):
       s = s.replace('.', '')  # Remove periods
       s = re.sub(r'[{}]', '', s)  # Remove curly braces used in BibTeX formatting
       s = re.sub(r'\\[a-zA-Z]{1,2}', '', s)  # Remove LaTeX commands (e.g., \textcopyright)
       return ' '.join([n for n in s.split(' ') if len(n) > 1])  # Remove single-letter names
   
   # Load the .bib file
   with open('Pure_publikationen_19.09.24_simpfied.bib', 'r') as bibtex_file:
       bib_database = bibtexparser.load(bibtex_file)
   
   internal_authors = ["Jiabin Huang", "Nicole Fischer", "Denise Ohnezeit", "Jiabin Huang", "Ute Westerkamp", "Veronika Brinschwitz", "Claudia Schmidt", "Manja Czech-Sioli", "Samira Weißelberg", "Tabea Schlemeyer", "Nicole Fischer", "Laura Berneking", "Indra Bekere", "Sören Rob", "Marie Schnapp", "Jiabin Huang", "Klaus Ruckdeschel", "Martin Aepfelbacher", "Anna Both", "Jiabin Huang", "Martin Aepfelbacher", "Holger Rohde", "Martin Christner Anna Both", "Jiabin Huang", "Martin Christner", "T. O. Klatte", "Martin Aepfelbacher", "Holger Rohde", "Holger Rohde", "Jiabin Huang", "Luise Brakert", "Laura Berneking", "Anna Both", "Benjamin Berinson", "Jiabin Huang", "Martin Aepfelbacher", "Christine Wolschke", "Dominic Wichmann", "Holger Rohde", "Holger Rohde", "Jiabin Huang", "Swantje Gundlach", "Jiabin Huang", "Marc Lütgehetmann", "Nicole Fischer Anna Both", "Florian Kruse", "Nadine Mirwald", "Gefion Franke", "Martin Christner", "Jiabin Huang", "Jan Lennart Hansen", "Nicolaus-Martin Kröger", "Laura Berneking", "H. Lellek", "Martin Aepfelbacher", "Holger Rohde", "Jiabin Huang", "Nicole Fischer", "Jiabin Huang", "Nicole Fischer", "Jiabin Huang", "Nicole Fischer", "Jiabin Huang", "Nicole Fischer", "Jiabin Huang", "Anna Both", "Jiabin Huang", "Minyue Qi", "Samira Weißelberg", "Henning Büttner", "S. Lezius", "Antonio Failla", "Martin Christner", "Malik Alawi", "Martin Aepfelbacher", "Holger Rohde", "Jiabin Huang", "Jiabin Huang", "K. Giersch", "L. Hermanussen", "T. Volz", "A. Volmari", "L. Allweiss", "Jiabin Huang", "Nicole Fischer", "Marc Lütgehetmann", "M. Dandri", "I. Stelzer", "C. Urbschat", "S. Schepanski", "K. Thiele", "I. Triviai", "A. Wieczorek", "Malik Alawi", "Denise Ohnezeit", "J. Kottlau", "Jiabin Huang", "Nicole Fischer", "H-W. Mittrücker", "M. E. Solano", "B. Fehse", "A. Diemert", "F. Stahl", "P. Arck", "Indra Bekere", "Jiabin Huang", "Marie Schnapp", "Maren Rudolph", "Laura Berneking", "Klaus Ruckdeschel", "Nicole Fischer", "Martin Aepfelbacher", "Susanne Pfefferle", "Jiabin Huang", "Dominik Nörz", "Marc Lütgehetmann", "Martin Aepfelbacher", "Nicole Fischer", "Susanne Pfefferle", "Robin Kobbe", "Manja Czech-Sioli", "Dominik Nörz", "R. Santer", "J. Oh", "S. Kluge", "K. Peldschus", "Jiabin Huang", "Martin Aepfelbacher", "Johannes Knobloch", "Marc Lütgehetmann", "Nicole Fischer", "Nicole Fischer", "Jiabin Huang", "Marc Lütgehetmann", "C. Iking-Konert L. Guo", "Jiabin Huang", "M. Chen", "E. Piotrowski", "N. Song", "G. Zahner", "H-J. Paust", "Malik Alawi", "F. Thaiss", "Anna Both", "Henning Büttner", "Jiabin Huang", "M. Perbandt", "C. Belmar Campos", "Martin Christner", "F. P. Maurer", "S. Kluge", "C. König", "Martin Aepfelbacher", "Dominic Wichmann", "Holger Rohde", "C. F. Krebs", "H-J. Paust", "S. Krohn", "T. Koyro", "S. R. Brix", "J-H. Riedel", "Patricia Bartsch", "Thorsten Wiech", "C. Meyer-Schwesinger", "Jiabin Huang", "Nicole Fischer", "C. P. Busch", "H-W. Mittrücker", "L. G. Perez", "U. O. Wenzel", "M. Janneck", "O. M. Steinmetz", "Nicola Gagliani", "Rolf Stahl", "Samuel Huber", "J-E. Turner", "Ulf Panzer", "Anna Both", "Jiabin Huang", "Henning Büttner", "Martin Aepfelbacher", "Holger Rohde", "Moritz Hentschke", "Jiabin Huang", "Jiabin Huang", "Jiabin Huang", "Jiabin Huang", "Björn Voß"]
   internal_authors = list(set(internal_authors))
   
   # Create an empty graph
   G = nx.Graph()
   
   #print("Nodes:", G.nodes())
   #print("Edges:", G.edges())
   
   #The first entry:
   #{'author': 'Sake, Svenja M and Xiaoyu Zhang and Rajak, Manoj Kumar and Melanie Urbanek-Quaing and Arnaud Carpentier and Gunesch, Antonia P and Christina Grethe and Alina Matthaei and Jessica Rückert and Marie Galloux and Thibaut Larcher and Le Goffic, Ronan and Fortune Hontonnou and Chatterjee, Arnab K and Kristen Johnson and Kaycie Morwood and Katharina Rox and Elgaher, Walid A M and Jiabin Huang and Martin Wetzke and Gesine Hansen and Nicole Fischer and Jean-Francois Elouët and Marie-Anne Rameix-Welti and Hirsch, Anna K H and Elisabeth Herold and Martin Empting and Chris Lauber and Thomas Krey and Sibylle Haid and Thomas Pietschmann', 'title': 'Drug repurposing screen identifies lonafarnib as respiratory syncytial virus fusion protein inhibitor', 'ENTRYTYPE': 'article', 'ID': '23fb542b9ca640f791bac65fcc137b9e'}
   
   # Iterate through each entry in the bibtex file
   for entry in bib_database.entries:
       title = entry.get('title', '').replace(':', ' ')
       #title = clean_string(f"Paper/{title}")
   
       authors = entry.get('author', '').split(' and ')  # Authors are separated by 'and' in BibTeX
   
       for author in authors:
           #author = clean_string(f"Author/{author}")
           if author in internal_authors:
               print(author)
               G.add_edge(author, title)
   
   ## Try different layout engines if 'sfdp' is problematic
   #try:
   #    pos = nx.nx_pydot.graphviz_layout(G, prog='sfdp')
   #except AssertionError:
   #    print("Error with sfdp layout, switching to 'dot' layout.")
   #    pos = nx.nx_pydot.graphviz_layout(G, prog='dot')  # Fallback to 'dot'
   try:
       pos = nx.nx_pydot.graphviz_layout(G, prog='sfdp')
   except Exception as e:
       print("Error generating layout:", e)
       pos = nx.spring_layout(G)  # Fallback to another layout
   
   for node in G.nodes():
       if isinstance(node, str):
           G.nodes[node]['label'] = node.replace(' ', '_').replace('/', '_')
   
   print("Nodes in the graph:", G.nodes())
   # Determine maximum length for author nodes
   max_len = max([len(n) for n in G.nodes() if n in internal_authors])
   print(max_len)
   
   plt.figure(figsize=(48, 36))
   
   #plot nodes for authors
   nx.draw_networkx_nodes(G, pos,
       #nodelist=[n for n in G.nodes() if n.startswith('Author/')],
       nodelist=[n for n in G.nodes() if n in internal_authors],
       node_size=max_len*200)
   
   #plot nodes for publications
   nx.draw_networkx_nodes(G, pos,
       nodelist=[n for n in G.nodes() if n not in internal_authors],
       node_color='y',
       node_size=100)
   
   nx.draw_networkx_edges(G, pos)
   nx.draw_networkx_labels(G, pos,
       labels={n: n.split('/')[-1].replace(' ', '\n') for n in G.nodes() if n in internal_authors},
       font_color='w', font_size=10, font_weight='bold', font_family='serif') #font_family='sans-serif')
   #'serif': Uses a serif typeface (e.g., Times New Roman).
   #'sans-serif': Uses a sans-serif typeface (e.g., Helvetica, Arial).
   #'monospace': Uses a monospace typeface (e.g., Courier New).
   #'DejaVu Sans': A popular sans-serif typeface available in many environments.
   #'Arial': A widely available sans-serif typeface.
   #'Times New Roman': A classic serif typeface.
   #'Comic Sans MS': A casual sans-serif typeface.
   
   plt.axis('off')
   
   # Save the plot as a PNG file
   #, bbox_inches="tight"
   plt.savefig("co_author_network.png", format="png")
   #convert Figure_2.png -crop 1340x750+315+135 co_author_network_cropped_2.png
   
   plt.show()

2. Imported bib-file

   @article{23fb542b9ca640f791bac65fcc137b9e,
     title     = "Drug repurposing screen identifies lonafarnib as respiratory syncytial virus fusion protein inhibitor",
     author    = "Sake, Svenja M and Xiaoyu Zhang and Rajak, Manoj Kumar and Melanie Urbanek-Quaing and Arnaud Carpentier and Gunesch, Antonia P and Christina Grethe and Alina Matthaei and Jessica Rückert and Marie Galloux and Thibaut Larcher and Le Goffic, Ronan and Fortune Hontonnou and Chatterjee, Arnab K and Kristen Johnson and Kaycie Morwood and Katharina Rox and Elgaher, Walid A M and Jiabin Huang and Martin Wetzke and Gesine Hansen and Nicole Fischer and Jean-Francois Elouët and Marie-Anne Rameix-Welti and Hirsch, Anna K H and Elisabeth Herold and Martin Empting and Chris Lauber and Thomas Krey and Sibylle Haid and Thomas Pietschmann",
   }
   
   @article{902563efda5a464b86f9195f4d5ef964,
     title     = "Merkel cell polyomavirus small tumor antigen contributes to immune evasion by interfering with type I interferon signaling",
     author    = "Denise Ohnezeit and Jiabin Huang and Ute Westerkamp and Veronika Brinschwitz and Claudia Schmidt and Thomas Günther and Manja Czech-Sioli and Samira Weißelberg and Tabea Schlemeyer and Jacqueline Nakel and Julia Mai and Sabrina Schreiner and Carola Schneider and Caroline C Friedel and Hella Schwanke and Melanie M Brinkmann and Adam Grundhoff and Nicole Fischer",
   }
   
   @article{c1d64e689be44cfca56d74a33cccac91,
     title     = "A bacterial effector protein promotes nuclear translocation of Stat3 to induce IL-10.",
     author    = "L Berneking and I Bekere and S Rob and M Schnapp and J Huang and K Ruckdeschel and M Aepfelbacher",
   }
   
   @article{9fbe8d2517e44a9e845f077480a8839f,
     title     = "First case of bloodstream infection caused by Mixta hanseatica sp. nov., a novel species within the Mixta genus of the Erwiniaceae family",
     author    = "Anna Both and Jiabin Huang and Philipp Wenzel and Martin Aepfelbacher and Holger Rohde and Martin Christner and Moritz Hentschke",
   }
   
   @article{8d43e070f40045fc946d9b832ac80cf3,
     title     = "Genomics of Invasive Cutibacterium acnes Isolates from Deep-Seated Infections",
     author    = "Anna Both and Jiabin Huang and Moritz Hentschke and David Tobys and Martin Christner and Till Orla Klatte and Harald Seifert and Martin Aepfelbacher and Holger Rohde"
   }
   
   @article{f0ca6606b53d491bbb57f5046e292fc4,
     title     = "IKK1 aggravates ischemia-reperfusion kidney injury by promoting the differentiation of effector T cells",
     author    = "Ning Song and Yang Xu and Hans-Joachim Paust and Ulf Panzer and Maria de las Mercedes Noriega and Linlin Guo and Thomas Renne and Jiabin Huang and Xianglin Meng and Mingyan Zhao and Friedrich Thaiss"
   }
   
   @article{1cb3c347f86446e1b5c4fb0d0703f5d6,
     title     = "Pharmacokinetic/pharmacodynamic evaluation of tigecycline dosing in a hollow fiber infection model against clinical bla-KPC producing Klebsiella Pneumoniae isolates",
     author    = "Lisa F. Amann and Astrid Broeker and Maria Riedner and Holger Rohde and Jiabin Huang and Patrice Nordmann and Jean-Winoc Decousser and Sebastian G. Wicha"
   }
   
   @article{b2f8d96cdd5f4ff6a9d1a713888c4a20,
     title     = "Rapid Development of cefiderocol resistance in a carbapenem-resistant Pseudomonas aeruginosa isolate associated with mutations in the pyoverdine biosynthesis pathway",
     author    = "Luise Brakert and Laura Berneking and Anna Both and Benjamin Berinson and Jiabin Huang and Martin Aepfelbacher and Christine Wolschke and Dominic Wichmann and Holger Rohde"
   }
   
   @article{502b812862474fea916c09646ef1b210,
     title     = "A pharmacokinetic-pharmacodynamic (PKPD) model-based analysis of tedizolid against enterococci using the hollow-fibre infection model",
     author    = "Khalid Iqbal and Holger Rohde and Jiabin Huang and Tjokosela Tikiso and Lisa Amann and Markus Zeitlinger and Sebastian Wicha"
   }
   
   @article{f3abbf64990240d2aec1a98080c70eca,
     title     = "Mechanisms of CD8+ T cell failure in chronic hepatitis E virus infection",
     author    = "Janine Kemming and Swantje Gundlach and Marcus Panning and Daniela Huzly and Jiabin Huang and Marc Lü tgehetmann and Nicole Fischer and Christoph Neumann-Haefelin"
   }
   
   @article{a80f9280e7c041d2aca1897678cb463d,
     title     = "Population dynamics in colonizing vancomycin-resistant Enterococcus faecium isolated from immunosuppressed patients",
     author    = "Anna Both and Florian Kruse and Nadine Mirwald and Gefion Franke and Martin Christner and Jiabin Huang and Jan Lennart Hansen and Nicolaus Kröger and Laura Berneking and Heinrich Lellek and Martin Aepfelbacher and Holger Rohde"
   }
   
   @article{77e10e8b504648729b5480114e0d683b,
     title     = "Respiratory Syncytial Virus Two-Step Infection Screen Reveals Inhibitors of Early and Late Life Cycle Stages",
     author    = "Svenja M Sake and Christina Kosch and Sebastian Blockus and Sibylle Haid and Antonia P Gunesch and Xiaoyu Zhang and Martina Friesland and Sofie B Trummer and Christina Grethe and Anne Kühnel and Jessica Rückert and W Paul Duprex and Jiabin Huang and Marie-Anne Rameix-Welti and Martin Empting and Nicole Fischer and Anna K H Hirsch and Thomas F Schulz and Thomas Pietschmann"
   }
   
   @article{71a7f9b80a5a4d23864696f5cc784dc2,
     title     = "Target capture sequencing reveals a monoclonal outbreak of respiratory syncytial virus B infections among adult hematologic patients",
     author    = "Claas Baier and Jiabin Huang and Kerstin Reumann and Daniela Indenbirken and Felicitas Thol and Christian Koenecke and Ella Ebadi and Albert Heim and Franz-Christoph Bange and Sibylle Haid and Thomas Pietschmann and Nicole Fischer"
   }
   
   @article{302a9b6b270e4d01936fadadb58b7495,
     title     = "An inter-laboratory study to investigate the impact of the bioinformatics component on microbiome analysis using mock communities",
     author    = "Denise O'Sullivan and Ronan Doyle and Sasithon Temisak and Nicholas Redshaw and Alexandra Whale and Grace Logan and Jiabin Huang and Nicole Fischer and Gregory Amos and Mark Preston and Julian Marchesi and Josef Wagner and Julian Parkhill and Yair Motro and Hubert Denise and Robert Finn and Gemma Kay and Justin O'Grady and Emma Ransom-Jones and Huihai Wu and Emma Laing and Ernest Benavente and Jody Phelan and Taane Clark and Jacob Moran-Gilad and Jim Huggett"
   }
   
   @article{162031f95f9b4d5497c91cc64cf27352,
     title     = "Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples",
     author    = "Jutte J. C. de Vries and Julianne R Brown and Nicole Fischer and Igor A Sidorov and Sofia Morfopoulou and Bas B Oude Munnink and Arzu Sayiner and Alihan Bulgurcu and Christophe Rodriguez and Guillaume Gricourt and Els Keyaerts and Leen Beller and Claudia Bachofen and Jakub Kubacki and Cordey Samuel and Laubscher Florian and Schmitz Dennis and Martin Beer and Dirk Hoeper and Michael Huber and Verena Kufner and Maryam Zaheri and Aitana Lebrand and Anna Papa and Sander van Boheemen and Aloys C M Kroes and Judith Breuer and F Xavier Lopez-Labrador and Eric C J Claas and ESCV Network on Next-Generation Sequencing"
   }
   
   @article{19a7c4e85485417f82eedb697cb06d81,
     title     = "Complete genome sequencing and molecular characterization of SARS-COV-2 from COVID-19 cases in Alborz province in Iran",
     author    = "Amir Kaffashi and Jiabin Huang and Amir Bairami and Mohammad Hossein Fallah Mehrabadi and Somayeh Yaslianifard and Mohsen Bashashati and S Reza Banihashemi and Fatemeh Soleimanifar and Mohsen Lotfi and Morteza Taghizadeh and Alireza Soleimani and Akbar Khorasani and Farzaneh Moshiri and Sayed-Hamidreza Mozhgani"
   }
   
   @article{654190298b7a4bfcaefae17efe06ea16,
     title     = "Distinct clonal lineages and within-host diversification shape invasive Staphylococcus epidermidis populations",
     author    = "Anna Both and Jiabin Huang and Minyue Qi and Christian Lausmann and Samira Weißelberg and Henning Büttner and Susanne Lezius and Antonio Virgilio Failla and Martin Christner and Marc Stegger and Thorsten Gehrke and Sharmin Baig and Mustafa Citak and Malik Alawi and Martin Aepfelbacher and Holger Rohde"
   }
   
   @inbook{be430697e7534557a04be7d46a3b6e7d,
     title     = "Reducing the search space in RNA helix based folding. European Conference on Computational Biology",
     author    = "Jiabin Huang and Björn Voß"
   }
   
   @article{aa6df25b09cc415f9d8afff369459721,
     title     = "Simulation of Folding Kinetics for Aligned RNAs",
     author    = "Jiabin Huang and Björn Voß"
   }
   
   @article{eba612f0e5dc43d29e0b97077aa08002,
     title     = "Strong Replication Interference Between Hepatitis Delta Viruses in Human Liver Chimeric Mice",
     author    = "Katja Giersch and Lennart Hermanussen and Tassilo Volz and Annika Volmari and Lena Allweiss and Camille Sureau and John Casey and Jiabin Huang and Nicole Fischer and Marc Lütgehetmann and Maura Dandri"
   }
   
   @article{80dcc4d4b01b477fafc6b2e761338ffe,
     title     = "Vertically transferred maternal immune cells promote neonatal immunity against early life infections",
     author    = "Ina Stelzer and Christopher Urbschat and Steven Schepanski and Kristin Thiele and Ioanna Triviai and Agnes Wieczorek and Malik Alawi and Denise Ohnezeit and Julian Kottlau and Jiabin Huang and Nicole Fischer and Hans-Willi Mittrücker and Maria Emilia Solano and Boris Fehse and Anke Diemert and Felix Stahl and Petra Arck"
   }
   
   @article{b53edc510f6b45febd12ab5121cb9c09,
     title     = "Yersinia remodels epigenetic histone modifications in human macrophages",
     author    = "Indra Bekere and Jiabin Huang and Marie Schnapp and Maren Rudolph and Laura Berneking and Klaus Ruckdeschel and Adam Grundhoff and Thomas Günther and Nicole Fischer and Martin Aepfelbacher"
   }
   
   @article{66dbdc2fad114d628c8d295875ae8aec,
     title     = "Complete Genome Sequence of a SARS-CoV-2 Strain Isolated in Northern Germany",
     author    = "Susanne Pfefferle and Jiabin Huang and Dominik Nörz and Daniela Indenbirken and Marc Lütgehetmann and Lisa Oestereich and Thomas Günther and Adam Grundhoff and Martin Aepfelbacher and Nicole Fischer"
   }
   
   @article{7c73f3bcc9e94845a52a78194e3cf1d8,
     title     = "SARS Coronavirus-2 variant tracing within the first Coronavirus Disease 19 clusters in northern Germany",
     author    = "Susanne Pfefferle and Thomas Günther and Robin Kobbe and Manja Czech-Sioli and Dominic Nörz and René Santer and Jun Oh and Stefan Kluge and Lisa Oestereich and Kersten Peldschus and Daniela Indenbirken and Jiabin Huang and Adam Grundhoff and Martin Aepfelbacher and Johannes K. Knobloch and Marc Lütgehetmann and Nicole Fischer"
   }
   
   @article{cb56e81ed4fd48689aa6f91d5c5b4d5e,
     title     = "Changes in the composition of the upper respiratory tract microbial community in granulomatosis with polyangiitis",
     author    = "Peter Lamprecht and Nicole Fischer and Jiabin Huang and Lia Burkhardt and Marc Lütgehetmann and Fabian Arndt and Ida Rolfs and Anja Kerstein and Christof Iking-Konert and Martin Laudien"
   }
   
   @article{65d6baf2bd174db3826e0457f0613ef0,
     title     = "T-lymphocyte-specific knockout of IKK-2 or NEMO induces T17 cells in an experimental nephrotoxic nephritis mouse model",
     author    = "Linlin Guo and Jiabin Huang and Meilan Chen and Eveline Piotrowski and Ning Song and Gunther Zahner and Hans-Joachim Paust and Malik Alawi and Robert Geffers and Friedrich Thaiss"
   }
   
   @article{bcd9fc94dd6c4754bd285780a86d8e20,
     title     = "Emergence of ceftazidime/avibactam non-susceptibility in an MDR Klebsiella pneumoniae isolate",
     author    = "Anna Both and Henning Büttner and Jiabin Huang and Markus Perbandt and Cristina Belmar Campos and Martin Christner and Florian P. Maurer and Stefan Kluge and Christina König and Martin Aepfelbacher and Dominic Wichmann and Holger Rohde"
   }
   
   @article{3730964a0c4a41f58967b564e0c417bc,
     title     = "Autoimmune Renal Disease Is Exacerbated by S1P-Receptor-1-Dependent Intestinal Th17 Cell Migration to the Kidney",
     author    = "Christian F. Krebs and Hans-Joachim Paust and Sonja Krohn and Tobias Koyro and Silke R. Brix and Jan-Hendrik Riedel and Patricia Bartsch and Thorsten Wiech and Catherine Meyer-Schwesinger and Jiabin Huang and Nicole Fischer and Christoph Philipp Busch and Hans-Willi Mittrücker and Ulrich Steinhoff and Brigitta Stockinger and Laura Garcia Perez and Ulrich O. Wenzel and Matthias Janneck and Oliver M. Steinmetz and Nicola Gagliani and Rolf A. K. Stahl and Samuel Huber and Jan-Eric Turner and Ulf Panzer"
   }
   
   @article{d4c1b6c9a0e24abba33787715bc0854e,
     title     = "First report of Escherichia coli co-producing NDM-1 and OXA-232",
     author    = "Anna Both and Jiabin Huang and Martin Kaase and Julia Hezel and Daniel Wertheimer and Ines Fenner and Thomas Günther and Adam Grundhoff and Henning Büttner and Martin Aepfelbacher and Holger Rohde and Moritz Hentschke"
   }
   
   @article{1c9b5596739048cb871ab0c64d3c5cb8,
     title     = "Analysing RNA-kinetics based on folding space abstraction",
     author    = "Jiabin Huang and Björn Voß"
   }
   
   @book{694490818fa14f23bb737e07c6b4791f,
     title     = "RNA folding space analysis using structure abstraction",
     author    = "Jiabin Huang"
   }
   
   @article{eb70223e307f4b059e560298ccd944ce,
     title     = "Abstract folding space analysis based on helices",
     author    = "Jiabin Huang and Rolf Backofen and Björn Voß"
   }
   
   @inbook{fd3c3deb16cf43bb9ed22c13a0dac284,
     title     = "RNAHeliCes -- Folding space analysis based on position aware structure abstraction",
     author    = "Jiabin Huang and Björn Voß"
   }

http://xgenes.com/course/phyloseq2/

---
title: "Phyloseq microbiome"
author: ""
date: '`r format(Sys.time(), "%d %m %Y")`'
header-includes:
- \usepackage{color, fancyvrb}
output:
rmdformats::readthedown:
    highlight: kate
    number_sections : yes
pdf_document:
    toc: yes
    toc_depth: 2
    number_sections : yes
---
```{r, echo=FALSE, warning=FALSE}
## Global options
# IMPORTANT NOTE: needs before "mkdir figures"
# gunzip table_mc1300.biom.gz
#rmarkdown::render('Phyloseq.Rmd',output_file='Phyloseq.html')
#tmp/course/scripts.html, tmp/course/Phyloseq_GPA_RA.html, crs/urls
```

```{r load-packages, include=FALSE}
library(knitr)
library(rmdformats)
library(readxl)
library(dplyr)
library(kableExtra)
options(max.print="75")
knitr::opts_chunk$set(fig.width=8,
                    fig.height=6,
                    eval=TRUE,
                    cache=TRUE,
                    echo=TRUE,
                    prompt=FALSE,
                    tidy=TRUE,
                    comment=NA,
                    message=FALSE,
                    warning=FALSE)
opts_knit$set(width=85)
```

# Data
Import raw data and assign sample key:
```{r, echo=TRUE, warning=FALSE}
map_corrected <- read.csv("../map_corrected.txt", sep="\t", row.names=1)
knitr::kable(map_corrected) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
# Prerequisites to be installed
* R : https://pbil.univ-lyon1.fr/CRAN/
* R studio : https://www.rstudio.com/products/rstudio/download/#download
```R
install.packages("dplyr")     # To manipulate dataframes
install.packages("readxl")    # To read Excel files into R
install.packages("ggplot2")   # for high quality graphics
install.packages("heatmaply")
source("https://bioconductor.org/biocLite.R")
biocLite("phyloseq")
```
```{r libraries, echo=TRUE, message=FALSE}
library("readxl") # necessary to import the data from Excel file
library("ggplot2") # graphics
library("picante")
library("microbiome") # data analysis and visualisation
library("phyloseq") # also the basis of data object. Data analysis and visualisation
library("ggpubr") # publication quality figures, based on ggplot2
library("dplyr") # data handling, filter and reformat data frames
library("RColorBrewer") # nice color options
library("heatmaply")
library(vegan)
library(gplots)
```
# Read the data and create phyloseq objects
Three tables are needed
* OTU
* Taxonomy
* Samples
```{r, echo=TRUE, warning=FALSE}
    #Change your working directory to where the files are located
    ps.ng.tax <- import_biom("./table_mc1300.biom", "../clustering/rep_set.tre")
    sample <- read.csv("../map_corrected.txt", sep="\t", row.names=1)
    SAM = sample_data(sample, errorIfNULL = T)
    #rownames(SAM) <-
    #c("1","2","3","5","6","7","8","9","10","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","46","47","48","49","50","51","52","53","55")
    ps.ng.tax <- merge_phyloseq(ps.ng.tax, SAM)
    print(ps.ng.tax)
    colnames(tax_table(ps.ng.tax)) <- c("Domain","Phylum","Class","Order","Family","Genus","Species")
    saveRDS(ps.ng.tax, "./ps.ng.tax.rds")
```
Visualize data
```{r, echo=TRUE, warning=FALSE}
sample_names(ps.ng.tax)
rank_names(ps.ng.tax)
sample_variables(ps.ng.tax)
```
Normalize number of reads in each sample using median sequencing depth.
```{r, echo=TRUE, warning=FALSE}
# RAREFACTION
#set.seed(9242)  # This will help in reproducing the filtering and nomalisation.
#ps.ng.tax <- rarefy_even_depth(ps.ng.tax, sample.size = 42369)
#total <- 42369
# NORMALIZE number of reads in each sample using median sequencing depth.
total = median(sample_sums(ps.ng.tax))
#> total
#[1] 42369
standf = function(x, t=total) round(t * (x / sum(x)))
ps.ng.tax = transform_sample_counts(ps.ng.tax, standf)
ps.ng.tax_rel <- microbiome::transform(ps.ng.tax, "compositional")
saveRDS(ps.ng.tax, "./ps.ng.tax.rds")
hmp.meta <- meta(ps.ng.tax)
hmp.meta$sam_name <- rownames(hmp.meta)
```
# Heatmaps
We consider the most abundant OTUs for heatmaps. For example one can only take OTUs that represent at least 1% of reads in at least one sample. Remember we normalized all the sampples to median number of reads (total).  We are left with only 168 OTUS which makes the reading much more easy.
```{r, echo=TRUE, warning=FALSE}
# Custom function to plot a heatmap with the specified sample order
ps.ng.tax_abund <- phyloseq::filter_taxa(ps.ng.tax, function(x) sum(x > total*0.01) > 0, TRUE)
kable(otu_table(ps.ng.tax_abund)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
# Calculate the relative abundance for each sample
ps.ng.tax_abund_rel <- transform_sample_counts(ps.ng.tax_abund, function(x) x / sum(x))
datamat_ = as.data.frame(otu_table(ps.ng.tax_abund))
#datamat <- datamat_[c("8","9","10","12","13","14",    "21","22","23","24","25","26","27","28",    "33","34","35","36","37","38","39","51",    "47","48","49","50","52","53","55")]
#--take all columns--
datamat <- datamat_
hr <- hclust(as.dist(1-cor(t(datamat), method="pearson")), method="complete")
hc <- hclust(as.dist(1-cor(datamat, method="spearman")), method="complete")
mycl = cutree(hr, h=max(hr$height)/1.08)
mycol = c("YELLOW", "DARKBLUE", "DARKORANGE", "DARKMAGENTA", "DARKCYAN", "DARKRED",  "MAROON", "DARKGREEN", "LIGHTBLUE", "PINK", "MAGENTA", "LIGHTCYAN","LIGHTGREEN", "BLUE", "ORANGE", "CYAN", "RED", "GREEN");
mycol = mycol[as.vector(mycl)]
sampleCols <- rep('GREY',ncol(datamat))
#Colors: '#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99', '#e31a1c', '#cab2d6', '#6a3d9a'
#Control (19)
sampleCols[colnames(datamat)=='kg001'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg002'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg003'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg004'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg005'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg007'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg009'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg015'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg016'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg019'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg020'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg021'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg022'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg023'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg025'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg026'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg027'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg028'] <- '#1f78b4'
sampleCols[colnames(datamat)=='kg029'] <- '#1f78b4'
#GPA (21)
sampleCols[colnames(datamat)=='micro1'] <- '#33a02c'
sampleCols[colnames(datamat)=='micro3'] <- '#33a02c'
sampleCols[colnames(datamat)=='micro4'] <- '#33a02c'
sampleCols[colnames(datamat)=='micro6'] <- '#33a02c'
sampleCols[colnames(datamat)=='micro7'] <- '#33a02c'
sampleCols[colnames(datamat)=='micro12'] <- '#33a02c'
sampleCols[colnames(datamat)=='micro13'] <- '#33a02c'
sampleCols[colnames(datamat)=='micro16'] <- '#33a02c'
sampleCols[colnames(datamat)=='micro17'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw001'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw004'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw005'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw006'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw007'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw009'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw010'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw013'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw014'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw015'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw017'] <- '#33a02c'
sampleCols[colnames(datamat)=='mw021'] <- '#33a02c'
# RA (20)
sampleCols[colnames(datamat)=='ra002'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra003'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra004'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra005'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra006'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra007'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra008'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra009'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra010'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra013'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra014'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra015'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra017'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra018'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra019'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra020'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra022'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra023'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra024'] <- '#e31a1c'
sampleCols[colnames(datamat)=='ra025'] <- '#e31a1c'

library(RColorBrewer)
custom_palette <- colorRampPalette(brewer.pal(9, "Blues"))
heatmap_colors <- custom_palette(100)
png("figures/heatmap.png", width=1200, height=2400)
heatmap.2(as.matrix(datamat),Rowv=as.dendrogram(hr),Colv = NA, dendrogram = 'row',
            scale='row',trace='none',col=heatmap_colors, cexRow=1.2, cexCol=1.5,
            RowSideColors = mycol, ColSideColors = sampleCols, srtCol=15, labRow=row.names(datamat), key=TRUE, margins=c(10, 15), lhei=c(0.7, 15), lwid=c(1,8))
dev.off()
```

```{r, echo=TRUE, warning=FALSE, fig.cap="Heatmap", out.width = '100%', fig.align= "center"}
knitr::include_graphics("./figures/heatmap.png")
```

\pagebreak
# Taxonomic summary
## Bar plots in phylum level
```{r, echo=TRUE, warning=FALSE}
library(ggplot2)
geom.text.size = 6
theme.size = 8 #(14/5) * geom.text.size
ps.ng.tax_most = phyloseq::filter_taxa(ps.ng.tax_rel, function(x) mean(x) > 0.001, TRUE)
ps.ng.tax_most_ = transform_sample_counts(ps.ng.tax_most, function(x) x / sum(x))
```

```{r, echo=FALSE, warning=FALSE}
##--Creating 100% stacked bar plots with less abundant taxa in a sub-category #901--
##https://github.com/joey711/phyloseq/issues/901
##ps.ng.tax_most_df <- psmelt(ps.ng.tax_most_)  #5986x19
#glom <- tax_glom(ps.ng.tax_most_, taxrank = 'Phylum')
#tax_table(glom) # should list # taxa as # phyla
#data <- psmelt(glom) # create dataframe from phyloseq object
#data$Phylum <- as.character(data$Phylum) #convert to character
##simple way to rename phyla with < 1% abundance
#data$Phylum[data$Abundance < 0.001] <- "< 0.1% abund."
#
#library(plyr)
#medians <- ddply(data, ~Phylum, function(x) c(median=median(x$Abundance)))
#remainder <- medians[medians$median <= 0.001,]$Phylum
#data[data$Phylum %in% remainder,]$Phylum <- "Phyla < 0.1% abund."
#data$Phylum[data$Abundance < 0.001] <- "Phyla < 0.1% abund."
##--> data are not used!
#
##in class level
#glom <- tax_glom(ps.ng.tax_most_, taxrank = 'Class')
#tax_table(glom) # should list # taxa as # phyla
#data <- psmelt(glom) # create dataframe from phyloseq object
#data$Class <- as.character(data$Class) #convert to character
#
##simple way to rename phyla with < 1% abundance
#data$Class[data$Abundance < 0.001] <- "< 0.1% abund."
#Count = length(unique(data$Class))
#
##unique(data$Class)
##data$Class <- factor(data$Class, levels = c("Bacilli", "Bacteroidia", "Verrucomicrobiae", "Clostridia", "Gammaproteobacteria", "Alphaproteobacteria", "Actinobacteria", "Negativicutes", "Erysipelotrichia", "Methanobacteria", "< 0.1% abund."))
##------- Creating 100% stacked bar plots END --------
library(stringr)
#FITTING_1:

#ps.ng.tax_most_
#OTU Table:          [63 taxa and 60 samples]
# tax_table(ps.ng.tax_most_)[1,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Domain"], "__")[[1]][2]
# ... ...
# tax_table(ps.ng.tax_most_)[63,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Species"], "__")[[1]][2]
#for id in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63; do
#  echo "tax_table(ps.ng.tax_most_)[${id},\"Domain\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Domain\"], \"__\")[[1]][2]"
#  echo "tax_table(ps.ng.tax_most_)[${id},\"Phylum\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Phylum\"], \"__\")[[1]][2]"
#  echo "tax_table(ps.ng.tax_most_)[${id},\"Class\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Class\"], \"__\")[[1]][2]"
#  echo "tax_table(ps.ng.tax_most_)[${id},\"Order\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Order\"], \"__\")[[1]][2]"
#  echo "tax_table(ps.ng.tax_most_)[${id},\"Family\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Family\"], \"__\")[[1]][2]"
#  echo "tax_table(ps.ng.tax_most_)[${id},\"Genus\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Genus\"], \"__\")[[1]][2]"
#  echo "tax_table(ps.ng.tax_most_)[${id},\"Species\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Species\"], \"__\")[[1]][2]"
#done

tax_table(ps.ng.tax_most_)[1,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[1,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[1,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[1,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[1,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[1,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[1,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[2,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[2,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[2,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[2,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[2,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[2,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[2,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[3,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[3,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[3,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[3,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[3,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[3,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[3,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[4,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[4,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[4,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[4,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[4,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[4,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[4,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[5,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[5,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[5,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[5,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[5,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[5,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[5,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[6,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[6,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[6,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[6,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[6,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[6,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[6,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[7,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[7,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[7,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[7,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[7,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[7,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[7,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[8,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[8,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[8,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[8,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[8,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[8,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[8,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[9,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[9,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[9,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[9,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[9,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[9,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[9,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[10,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[10,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[10,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[10,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[10,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[10,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[10,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[11,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[11,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[11,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[11,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[11,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[11,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[11,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[12,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[12,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[12,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[12,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[12,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[12,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[12,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[13,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[13,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[13,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[13,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[13,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[13,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[13,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[14,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[14,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[14,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[14,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[14,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[14,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[14,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[15,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[15,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[15,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[15,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[15,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[15,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[15,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[16,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[16,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[16,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[16,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[16,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[16,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[16,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[17,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[17,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[17,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[17,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[17,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[17,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[17,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[18,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[18,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[18,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[18,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[18,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[18,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[18,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[19,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[19,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[19,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[19,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[19,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[19,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[19,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[20,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[20,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[20,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[20,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[20,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[20,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[20,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[21,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[21,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[21,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[21,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[21,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[21,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[21,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[22,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[22,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[22,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[22,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[22,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[22,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[22,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[23,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[23,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[23,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[23,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[23,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[23,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[23,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[24,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[24,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[24,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[24,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[24,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[24,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[24,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[25,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[25,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[25,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[25,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[25,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[25,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[25,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[26,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[26,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[26,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[26,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[26,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[26,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[26,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[27,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[27,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[27,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[27,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[27,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[27,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[27,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[28,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[28,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[28,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[28,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[28,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[28,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[28,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[29,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[29,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[29,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[29,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[29,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[29,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[29,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[30,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[30,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[30,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[30,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[30,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[30,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[30,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[31,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[31,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[31,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[31,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[31,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[31,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[31,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[32,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[32,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[32,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[32,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[32,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[32,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[32,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[33,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[33,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[33,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[33,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[33,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[33,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[33,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[34,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[34,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[34,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[34,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[34,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[34,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[34,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[35,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[35,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[35,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[35,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[35,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[35,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[35,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[36,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[36,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[36,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[36,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[36,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[36,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[36,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[37,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[37,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[37,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[37,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[37,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[37,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[37,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[38,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[38,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[38,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[38,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[38,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[38,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[38,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[39,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[39,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[39,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[39,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[39,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[39,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[39,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[40,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[40,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[40,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[40,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[40,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[40,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[40,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[41,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[41,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[41,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[41,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[41,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[41,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[41,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[42,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[42,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[42,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[42,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[42,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[42,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[42,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[43,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[43,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[43,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[43,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[43,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[43,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[43,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[44,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[44,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[44,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[44,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[44,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[44,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[44,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[45,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[45,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[45,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[45,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[45,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[45,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[45,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[46,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[46,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[46,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[46,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[46,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[46,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[46,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[47,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[47,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[47,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[47,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[47,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[47,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[47,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[48,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[48,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[48,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[48,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[48,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[48,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[48,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[49,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[49,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[49,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[49,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[49,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[49,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[49,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[50,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[50,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[50,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[50,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[50,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[50,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[50,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[51,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[51,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[51,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[51,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[51,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[51,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[51,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[52,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[52,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[52,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[52,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[52,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[52,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[52,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[53,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[53,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[53,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[53,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[53,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[53,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[53,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[54,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[54,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[54,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[54,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[54,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[54,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[54,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[55,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[55,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[55,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[55,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[55,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[55,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[55,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[56,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[56,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[56,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[56,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[56,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[56,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[56,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[57,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[57,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[57,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[57,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[57,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[57,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[57,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[58,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[58,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[58,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[58,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[58,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[58,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[58,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[59,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[59,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[59,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[59,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[59,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[59,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[59,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[60,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[60,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[60,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[60,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[60,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[60,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[60,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[61,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[61,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[61,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[61,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[61,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[61,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[61,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[62,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[62,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[62,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[62,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[62,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[62,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[62,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Species"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[63,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Domain"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[63,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Phylum"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[63,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Class"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[63,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Order"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[63,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Family"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[63,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Genus"], "__")[[1]][2]
tax_table(ps.ng.tax_most_)[63,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Species"], "__")[[1]][2]
#tax_table(ps.ng.tax_most_)[64,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Domain"], "__")[[1]][2]
#tax_table(ps.ng.tax_most_)[64,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Phylum"], "__")[[1]][2]
#tax_table(ps.ng.tax_most_)[64,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Class"], "__")[[1]][2]
#tax_table(ps.ng.tax_most_)[64,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Order"], "__")[[1]][2]
#tax_table(ps.ng.tax_most_)[64,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Family"], "__")[[1]][2]
#tax_table(ps.ng.tax_most_)[64,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Genus"], "__")[[1]][2]
#tax_table(ps.ng.tax_most_)[64,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Species"], "__")[[1]][2]
```

```{r, echo=TRUE, warning=FALSE}
plot_bar(ps.ng.tax_most_, fill="Phylum") + geom_bar(aes(), stat="identity", position="stack") +
scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=2))

ps.ng.tax_most_copied <- data.table::copy(ps.ng.tax_most_)
```

## Bar plots in class level
```{r, echo=TRUE, warning=FALSE}
plot_bar(ps.ng.tax_most_copied, fill="Class") + geom_bar(aes(), stat="identity", position="stack") +
scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=3))
```

## Bar plots in order level
```{r, echo=TRUE, warning=FALSE}
plot_bar(ps.ng.tax_most_copied, fill="Order") + geom_bar(aes(), stat="identity", position="stack") +
scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=4))
```

## Bar plots in family level
```{r, echo=TRUE, warning=FALSE}
plot_bar(ps.ng.tax_most_copied, fill="Family") + geom_bar(aes(), stat="identity", position="stack") +
scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=8))
```

\pagebreak
# Alpha diversity
Plot Chao1 richness estimator, Observed OTUs, Shannon index, and Phylogenetic diversity.
Regroup together samples from the same group.
```{r, echo=FALSE, warning=FALSE}
# using rarefied data
#FITTING_2 (under CONSOLE):
#gunzip table_even4753.biom.gz
#alpha_diversity.py -i table_even42369.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering/rep_set.tre
#alpha_diversity.py -i table_mc1300.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering/rep_set.tre
```

```{r, echo=TRUE, warning=FALSE}
hmp.div_qiime <- read.csv("adiv_even.txt", sep="\t")
colnames(hmp.div_qiime) <- c("sam_name", "chao1", "observed_otus", "shannon", "PD_whole_tree")
row.names(hmp.div_qiime) <- hmp.div_qiime$sam_name
div.df <- merge(hmp.div_qiime, hmp.meta, by = "sam_name")
div.df2 <- div.df[, c("SampleType", "chao1", "shannon", "observed_otus", "PD_whole_tree")]
colnames(div.df2) <- c("Group", "Chao-1", "Shannon", "OTU", "Phylogenetic Diversity")
options(max.print=999999)
knitr::kable(div.df2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
#https://uc-r.github.io/t_test
#We can perform the test with t.test and transform our data and we can also perform the nonparametric test with the wilcox.test function.
stat.test.Shannon <- compare_means(
Shannon ~ Group, data = div.df2,
method = "t.test"
)
knitr::kable(stat.test.Shannon) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
div_df_melt <- reshape2::melt(div.df2)
#head(div_df_melt)
#https://plot.ly/r/box-plots/#horizontal-boxplot
#http://www.sthda.com/english/wiki/print.php?id=177
#https://rpkgs.datanovia.com/ggpubr/reference/as_ggplot.html
#http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/82-ggplot2-easy-way-to-change-graphical-parameters/
#https://plot.ly/r/box-plots/#horizontal-boxplot
#library("gridExtra")
#par(mfrow=c(4,1))
p <- ggboxplot(div_df_melt, x = "Group", y = "value",
            facet.by = "variable",
            scales = "free",
            width = 0.5,
            fill = "gray", legend= "right")
#ggpar(p, xlab = FALSE, ylab = FALSE)

lev <- levels(factor(div_df_melt$Group)) # get the variables
#Using my_stat_compare_means (see https://stackoverflow.com/questions/47839988/indicating-significance-with-ggplot2-in-a-boxplot-with-multiple-groups)
L.pairs <- combn(seq_along(lev), 2, simplify = FALSE, FUN = function(i) lev[i]) #%>% filter(p.signif != "ns")
my_stat_compare_means  <- function (mapping = NULL, data = NULL, method = NULL, paired = FALSE,
    method.args = list(), ref.group = NULL, comparisons = NULL,
    hide.ns = FALSE, label.sep = ", ", label = NULL, label.x.npc = "left",
    label.y.npc = "top", label.x = NULL, label.y = NULL, tip.length = 0.03,
    symnum.args = list(), geom = "text", position = "identity",
    na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)
{
    if (!is.null(comparisons)) {
        method.info <- ggpubr:::.method_info(method)
        method <- method.info$method
        method.args <- ggpubr:::.add_item(method.args, paired = paired)
        if (method == "wilcox.test")
            method.args$exact <- FALSE
        pms <- list(...)
        size <- ifelse(is.null(pms$size), 0.3, pms$size)
        color <- ifelse(is.null(pms$color), "black", pms$color)
        map_signif_level <- FALSE
        if (is.null(label))
            label <- "p.format"
        if (ggpubr:::.is_p.signif_in_mapping(mapping) | (label %in% "p.signif")) {
            if (ggpubr:::.is_empty(symnum.args)) {
                map_signif_level <- c(`****` = 1e-04, `***` = 0.001,
                `**` = 0.01, `*` = 0.05, ns = 1)
            } else {
            map_signif_level <- symnum.args
            }
            if (hide.ns)
                names(map_signif_level)[5] <- " "
        }
        step_increase <- ifelse(is.null(label.y), 0.12, 0)
        ggsignif::geom_signif(comparisons = comparisons, y_position = label.y,
            test = method, test.args = method.args, step_increase = step_increase,
            size = size, color = color, map_signif_level = map_signif_level,
            tip_length = tip.length, data = data)
    } else {
        mapping <- ggpubr:::.update_mapping(mapping, label)
        layer(stat = StatCompareMeans, data = data, mapping = mapping,
            geom = geom, position = position, show.legend = show.legend,
            inherit.aes = inherit.aes, params = list(label.x.npc = label.x.npc,
                label.y.npc = label.y.npc, label.x = label.x,
                label.y = label.y, label.sep = label.sep, method = method,
                method.args = method.args, paired = paired, ref.group = ref.group,
                symnum.args = symnum.args, hide.ns = hide.ns,
                na.rm = na.rm, ...))
    }
}

#FITTING_3: #> Using lev ("control", "GPA", "RA") for definition of the following comparisons.
  #comparisons = L.pairs,
  #symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05), symbols = c("****", "***", "**", "*")),
p3 <- p +
stat_compare_means(
  method="t.test",
  comparisons = list(c("GPA", "control"), c("RA", "control"), c("GPA", "RA")),
  label = "p.signif",
  symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns"))
)
#stat_pvalue_manual
#print(p2)
#https://stackoverflow.com/questions/20500706/saving-multiple-ggplots-from-ls-into-one-and-separate-files-in-r
#FITTING_4: mkdir figures
ggsave("./figures/alpha_diversity_SampleType.png", device="png", height = 10, width = 12)
ggsave("./figures/alpha_diversity_SampleType.svg", device="svg", height = 10, width = 12)
```

# Selected alpha diversity
```{r, echo=TRUE, warning=FALSE, fig.cap="Alpha diversity", out.width = '100%', fig.align= "center"}
knitr::include_graphics("./figures/alpha_diversity_SampleType.png")
#FITTING_5: Preparing the file selected_alpha_diversities.txt
#selected_alpha_diversities<-read.csv("selected_alpha_diversities.txt",sep="\t")
#knitr::kable(selected_alpha_diversities) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

# Beta diversity (weighted_unifrac)
```{r, echo=TRUE, warning=FALSE, fig.cap="Beta diversity", out.width = '100%', fig.align= "center"}
# The tests of significance were performed using a two-sided Student's two-sample t-test.
# Alternative hypothesis: Group 1 mean != Group 2 mean
# The nonparametric p-values were calculated using 999 Monte Carlo permutations.
# The nonparametric p-values contain the correct number of significant digits.
# Entries marked with "N/A" could not be calculated because at least one of the groups
# of distances was empty, both groups each contained only a single distance, or
# the test could not be performed (e.g. no variance in groups with the same mean).
# PREPARING_FILES: cp bdiv_even1300_SampleType/weighted_unifrac_boxplots/SampleType_Stats.txt bdiv_even1300_SampleType/weighted_unifrac_boxplots/SampleType_Stats_.txt and delete all headers, add # before "Group 1".
#                  cp bdiv_even1300_SampleType/unweighted_unifrac_boxplots/SampleType_Stats.txt bdiv_even1300_SampleType/unweighted_unifrac_boxplots/SampleType_Stats_.txt and delete all headers, add # before "Group 1".
#file:///home/jhuang/DATA/Data_Marius_16S/core_diversity_e42369/bdiv_even42369_Group/unweighted_unifrac_boxplots/Group_Stats.txt
weighted_unifrac_stats<-read.csv(file="bdiv_even1300_SampleType/weighted_unifrac_boxplots/SampleType_Stats_.txt", sep="\t")
knitr::kable(weighted_unifrac_stats) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
#BUG:How to generate the plot?  knitr::include_graphics("./figures/PCoA_weighted_unifrac.png")
```

# Beta diversity (unweighted_unifrac)
```{r, echo=TRUE, warning=FALSE, fig.cap="Beta diversity", out.width = '100%', fig.align= "center"}
#file:///home/jhuang/DATA/Data_Marius_16S/core_diversity_e42369/bdiv_even42369_Group/unweighted_unifrac_boxplots/Group_Stats.txt
unweighted_unifrac_stats<-read.csv(file="bdiv_even1300_SampleType/unweighted_unifrac_boxplots/SampleType_Stats_.txt", sep="\t")
knitr::kable(unweighted_unifrac_stats) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
#BUG:How to generate the plot?  knitr::include_graphics("./figures/PCoA_unweighted_unifrac.png")
```

# Differential abundance analysis
Differential abundance analysis aims to find the differences in the abundance of each taxa between two groups of samples, assigning a significance value to each comparison.
## GPA vs control
```{r, echo=TRUE, warning=FALSE}
library("DESeq2")
#ALTERNATIVE using ps.ng.tax_most_copied: ps.ng.tax (40594) vs. ps.ng.tax_most_copied (166)
ps.ng.tax_sel <- ps.ng.tax
#FITTING5: correct the id of the group members, see FITTING6
otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("micro1", "micro3", "micro4", "micro6", "micro7", "micro12", "micro13", "micro16", "micro17", "mw001", "mw004", "mw005", "mw006", "mw007", "mw009", "mw010", "mw013", "mw014", "mw015", "mw017", "mw021",    "kg001", "kg002", "kg003", "kg004", "kg005", "kg007", "kg009", "kg015", "kg016", "kg019", "kg020", "kg021", "kg022", "kg023", "kg025", "kg026", "kg027", "kg028", "kg029")]
diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~SampleType)
diagdds$SampleType <- relevel(diagdds$SampleType, "control")

## Filter out rows where all values are zero --> from 631 to 551
#keep <- rowSums(counts(diagdds) > 0) > 0 #
#diagdds <- diagdds[keep,]

# The poscounts method uses a modified geometric mean that excludes zero values from the calculation, which should prevent the error.
#counts(diagdds) <- as.matrix(round(counts(diagdds) + 1))
diagdds <- estimateSizeFactors(diagdds, type = "poscounts")
diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
resultsNames(diagdds)
res = results(diagdds, cooksCutoff = FALSE)
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
kable(sigtab) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
#rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied))
write.xlsx(sigtab, file = "sigtab_GPA_vs_control.xlsx")
#subv %in% v
### returns a vector TRUE FALSE
#is.element(subv, v)
### returns a vector TRUE FALSE
library("ggplot2")
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
    scale_fill_brewer(palette = palname, ...)
}
x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
x = sort(x)
sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
x = sort(x)
sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
#Error in checkForExperimentalReplicates(object, modelMatrix) :
#  The design matrix has the same number of samples and coefficients to fit,
#  so estimation of dispersion is not possible. Treating samples
#  as replicates was deprecated in v1.20 and no longer supported since v1.22.
```

## RA vs control
```{r, echo=TRUE, warning=FALSE}
ps.ng.tax_sel <- ps.ng.tax
otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("ra002", "ra003", "ra004", "ra005", "ra006", "ra007", "ra008", "ra009", "ra010", "ra013", "ra014", "ra015", "ra017", "ra018", "ra019", "ra020", "ra022", "ra023", "ra024", "ra025",    "kg001", "kg002", "kg003", "kg004", "kg005", "kg007", "kg009", "kg015", "kg016", "kg019", "kg020", "kg021", "kg022", "kg023", "kg025", "kg026", "kg027", "kg028", "kg029")]
diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~SampleType)
diagdds$SampleType <- relevel(diagdds$SampleType, "control")
diagdds <- estimateSizeFactors(diagdds, type = "poscounts")
diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
resultsNames(diagdds)
res = results(diagdds, cooksCutoff = FALSE)
alpha = 2.0
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
kable(sigtab) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
library("ggplot2")
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
    scale_fill_brewer(palette = palname, ...)
}
x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
x = sort(x)
sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
x = sort(x)
sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
```

## GPA vs RA
```{r, echo=TRUE, warning=FALSE}
ps.ng.tax_sel <- ps.ng.tax
otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("micro1", "micro3", "micro4", "micro6", "micro7", "micro12", "micro13", "micro16", "micro17", "mw001", "mw004", "mw005", "mw006", "mw007", "mw009", "mw010", "mw013", "mw014", "mw015", "mw017", "mw021",    "ra002", "ra003", "ra004", "ra005", "ra006", "ra007", "ra008", "ra009", "ra010", "ra013", "ra014", "ra015", "ra017", "ra018", "ra019", "ra020", "ra022", "ra023", "ra024", "ra025")]
diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~SampleType)
diagdds$SampleType <- relevel(diagdds$SampleType, "RA")
diagdds <- estimateSizeFactors(diagdds, type = "poscounts")
diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
resultsNames(diagdds)
res = results(diagdds, cooksCutoff = FALSE)
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
kable(sigtab) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
library("ggplot2")
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
    scale_fill_brewer(palette = palname, ...)
}
x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
x = sort(x)
sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
x = sort(x)
sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
```

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2786320/

Classification of Staphylococcal Cassette Chromosome mec (SCCmec): Guidelines for Reporting Novel SCCmec Elements

mec gene complex.

IS1272 is part of the IS1182 family of insertion sequences

• The mec gene complex is composed of mecA, its regulatory genes, and associated insertion sequences.
• The class A mec gene complex (class A mec) is the prototype complex, which contains mecA, the complete mecR1 and mecI regulatory genes upstream of mecA, and the hypervariable region (HVR) and insertion sequence IS431 downstream of mecA.
• • The class B mec gene complex is composed of mecA, a truncated mecR1 resulting from the insertion of IS1272 upstream of mecA, and [HVR and IS431 downstream of mecA].
• The class C mec gene complex contains mecA and truncated mecR1 by the insertion of IS431 upstream of mecA and HVR and IS431 downstream of mecA.
• There are two distinct class C mec gene complexes; in the class C1 mec gene complex, the IS431 upstream of mecA has the same orientation as the IS431 downstream of mecA (next to HVR), while in the class C2 mec gene complex, the orientation of IS431 upstream of mecA is reversed.
• C1 and C2 are regarded as different mec gene complexes since they have likely evolved independently.
• The class D mec gene complex is composed of mecA and ΔmecR1 but does not carry an insertion sequence downstream of ΔmecR1 (as determined by PCR analysis).
• Several variants within the major classes of the mec gene complex have been described, including insertions of IS431 or IS1182 upstream of mecA in the class A mec gene complex or insertion of Tn4001 upstream of mecA in the class B mec complex.

1. run with bengal3

   cd ~/DATA/Data_Denise_CalCov1
   cp bacto-0.1.json ../Data_Denise_CalCov2
   cp cluster.json ../Data_Denise_CalCov2
   cp Snakefile ../Data_Denise_CalCov2
   ln -s /home/jhuang/Tools/bacto/local .
   ln -s /home/jhuang/Tools/bacto/db .
   ln -s /home/jhuang/Tools/bacto/envs .
   mkdir raw_data; cd raw_data
   ln -s ../Alignment_Imported_1/20240913_174420/Fastq/HDM7_S1_L001_R1_001.fastq.gz HDM7_R1.fastq.gz
   ln -s ../Alignment_Imported_1/20240913_174420/Fastq/HDM7_S1_L001_R2_001.fastq.gz HDM7_R2.fastq.gz
   ln -s ../Alignment_Imported_1/20240913_174420/Fastq/HDM10_S2_L001_R1_001.fastq.gz HDM10_R1.fastq.gz
   ln -s ../Alignment_Imported_1/20240913_174420/Fastq/HDM10_S2_L001_R2_001.fastq.gz HDM10_R2.fastq.gz
   
   ln -s ../20240812_FS10003086_50_BSB09416-2831/Alignment_Imported_1/20240813_202730/Fastq/HDM1_S1_L001_R1_001.fastq.gz HDM1_R1.fastq.gz
   ln -s ../20240812_FS10003086_50_BSB09416-2831/Alignment_Imported_1/20240813_202730/Fastq/HDM1_S1_L001_R2_001.fastq.gz HDM1_R2.fastq.gz
   ln -s ../20240913/Alignment_Imported_1/20240913_174420/Fastq/HDM7_S1_L001_R1_001.fastq.gz HDM7_R1.fastq.gz
   ln -s ../20240913/Alignment_Imported_1/20240913_174420/Fastq/HDM7_S1_L001_R2_001.fastq.gz HDM7_R2.fastq.gz
   ln -s ../20240913/Alignment_Imported_1/20240913_174420/Fastq/HDM10_S2_L001_R1_001.fastq.gz HDM10_R1.fastq.gz
   ln -s ../20240913/Alignment_Imported_1/20240913_174420/Fastq/HDM10_S2_L001_R2_001.fastq.gz HDM10_R2.fastq.gz
   ln -s ../20240919_FS10003086_61_BSB09416-2735/Alignment_Imported_1/20240920_173408/Fastq/HDM11-SF1_S1_L001_R1_001.fastq.gz HDM11-SF1_R1.fastq.gz
   ln -s ../20240919_FS10003086_61_BSB09416-2735/Alignment_Imported_1/20240920_173408/Fastq/HDM11-SF1_S1_L001_R2_001.fastq.gz HDM11-SF1_R2.fastq.gz
   ln -s ../20240919_FS10003086_61_BSB09416-2735/Alignment_Imported_1/20240920_173408/Fastq/HDM15-SF2_S2_L001_R1_001.fastq.gz HDM15-SF2_R1.fastq.gz
   ln -s ../20240919_FS10003086_61_BSB09416-2735/Alignment_Imported_1/20240920_173408/Fastq/HDM15-SF2_S2_L001_R2_001.fastq.gz HDM15-SF2_R2.fastq.gz
   
   # only activate the steps assembly and mlst in bacto-0.1.json.
   (bengal3_ac3) jhuang@WS-2290C:~/Documents$ /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds
   
   # -- Results --
   shovill/HDM1/contigs.fa sepidermidis    5       arcC(1) aroE(1) gtr(1)  mutS(2) pyrR(2) tpiA(1) yqiL(1)
   HDM10.mlst.txt:shovill/HDM10/contigs.fa sepidermidis    59      arcC(2) aroE(1) gtr(1)  mutS(1) pyrR(2) tpiA(1) yqiL(1)
   HDM7.mlst.txt:shovill/HDM7/contigs.fa   sepidermidis    59      arcC(2) aroE(1) gtr(1)  mutS(1) pyrR(2) tpiA(1) yqiL(1)

2. run with bakta

   #under env (bakta)
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
       bakta --db /mnt/nvme0n1p1/bakta_db shovill/${sample}/contigs.fa --prefix ${sample}
   done

3. mapping on assembly to calculate the coverage

   #samtools depth input.bam > depth.txt
   #samtools depth input.bam | awk '{sum+=$3} END { print "Average coverage:",sum/NR}'
   #bedtools coverage -a regions.bed -b input.bam > coverage.txt
   #bedtools coverage -a regions.bed -b input.bam -d > coverage_per_base.txt
   
   bwa index ./shovill/HDM1/contigs.fa
   bwa mem ./shovill/HDM1/contigs.fa fastq/HDM1_1.fastq fastq/HDM1_2.fastq > aligned.sam
   samtools view -Sb aligned.sam > aligned.bam
   samtools sort aligned.bam -o sorted.bam
   samtools index sorted.bam
   samtools depth sorted.bam > depth.txt
   awk '{sum+=$3} END { print "Average coverage:",sum/NR}' depth.txt
   bedtools coverage -a regions.bed -b sorted.bam > coverage.txt
   bedtools genomecov -ibam sorted.bam -d > coverage_per_base.txt
   
   # Step 1: Calculate depth using samtools
   samtools depth sorted.bam > depth.txt
   # Step 2: Calculate average depth using awk
   awk '{sum+=$3; count++} END {print "Average Coverage:", sum/count}' depth.txt
   
   # Step 1: Calculate coverage with bedtools for a BED file
   #bedtools coverage -a regions.bed -b input.bam > coverage.txt
   # Step 2: Process the output with awk
   #awk '{ sum+=$7 } END { print "Average coverage depth:", sum/NR }' coverage.txt
   
   bwa index ./shovill/HDM7/contigs.fa
   bwa mem ./shovill/HDM7/contigs.fa fastq/HDM7_1.fastq fastq/HDM7_2.fastq > aligned_HDM7.sam
   samtools view -Sb aligned_HDM7.sam > aligned_HDM7.bam
   samtools sort aligned_HDM7.bam -o sorted_HDM7.bam
   samtools index sorted_HDM7.bam
   # Step 1: Calculate depth using samtools
   samtools depth sorted_HDM7.bam > depth_HDM7.txt
   # Step 2: Calculate average depth using awk
   awk '{sum+=$3; count++} END {print "Average Coverage:", sum/count}' depth_HDM7.txt
   #Average Coverage: 380.079
   
   bwa index ./shovill/HDM10/contigs.fa
   bwa mem ./shovill/HDM10/contigs.fa fastq/HDM10_1.fastq fastq/HDM10_2.fastq > aligned_HDM10.sam
   samtools view -Sb aligned_HDM10.sam > aligned_HDM10.bam
   samtools sort aligned_HDM10.bam -o sorted_HDM10.bam
   samtools index sorted_HDM10.bam
   # Step 1: Calculate depth using samtools
   samtools depth sorted_HDM10.bam > depth_HDM10.txt
   # Step 2: Calculate average depth using awk
   awk '{sum+=$3; count++} END {print "Average Coverage:", sum/count}' depth_HDM10.txt
   #Average Coverage: 254.704

4. SCCmec typing and drawing with clinker

   #1. -- HDM1_contigs.fa --
   
   One SCCmec element detected.
   
   Prediction based on genes:
   Predicted SCCmec element: SCCmec_type_IV(2B)
   
   Prediction based on homology to whole cassette:
   Predicted whole cassette and %template coverage: SCCmec_type_IV(2B) 79.92%
   
   Predicted genes:
   Fasta header % Identity Query/HSP Length Contig Position in contig
   
   ccrA2:7:81108:AB096217  100.00  1350/1350   contig00032 3770..5119
   ccrB2:9:JCSC4469:AB097677   99.94   1650/1650   contig00032 5120..6769
   IS1272:3:AM292304   99.95   1844/1843   contig00032 8611..10454
   dmecR1:1:AB033763   100.00  987/987 contig00032 10443..11429
   mecA:12:AB505628    100.00  2010/2010   contig00032 11526..13535
   
   samtools faidx shovill/HDM1_contigs.fa
   samtools faidx shovill/HDM1_contigs.fa contig00032:1-13635 > HDM1_sub.fna
   bakta --db /mnt/nvme0n1p1/bakta_db HDM1_sub.fna
   
   #2. -- HDM7_contigs.fa --
   
   One SCCmec element detected.
   
   Prediction based on genes:
   Predicted SCCmec element: SCCmec_type_IVa(2B)
   
   Prediction based on homology to whole cassette:
   Predicted whole cassette and %template coverage: SCCmec_type_IVa(2B) 84.24%
   
   Predicted genes:
   Fasta header % Identity Query/HSP Length Contig Position in contig
   
   mecA:12:AB505628    100.00  2010/2010   contig00014 2800..4809
   dmecR1:1:AB033763   99.90   987/987 contig00014 4906..5892
   IS1272:3:AM292304   100.00  1843/1843   contig00014 5881..7723
   ccrB2:3:CA05:AB063172   100.00  1629/1629   contig00014 9565..11193
   ccrA2:3:CA05:AB063172   100.00  1350/1350   contig00014 11215..12564
   subtype-IVa(2B):1:CA05:AB063172 100.00  1491/1491   contig00014 16461..17951
   #IS1272:2:AB033763  91.06   1577/1585   contig00001 369260..370836
   
   samtools faidx shovill/HDM7_contigs.fa
   samtools faidx shovill/HDM7_contigs.fa contig00014:2700-18051 > HDM7_sub.fna
   bakta --db /mnt/nvme0n1p1/bakta_db HDM7_sub.fna
   
   mecA
   dmecR1
   Type I restriction enzyme HindI endonuclease subunit-like C-terminal domain-containing protein
   IS1272
   DUF1643 domain-containing protein
   Pyridoxal phosphate-dependent enzyme
   hypothetical protein
   ccrB2
   ccrA2
   DUF927 domain-containing protein
   hypothetical protein
   ACP synthase
   AAA family ATPase (= subtype-IVa(2B))
   
   #3. -- HDM10_contigs.fa --
   
   Prediction based on genes:
   Predicted SCCmec element: SCCmec_type_IV(2B&5)
   
   Prediction based on homology to whole cassette:
   Predicted whole cassette and % template coverage: SCCmec_type_IV(2B) 84.37%
   
   Predicted genes:
   
   Fasta header % Identity Query/HSP Length Contig Position in contig
   
   subtype-IVa(2B):1:CA05:AB063172 100.00  1491/1491   contig00020 4152..5642
   ccrA2:3:CA05:AB063172   100.00  1350/1350   contig00020 9539..10888
   ccrB2:3:CA05:AB063172   100.00  1629/1629   contig00020 10910..12538
   IS1272:3:AM292304   100.00  1843/1843   contig00020 14380..16222
   dmecR1:1:AB033763   100.00  987/987 contig00020 16211..17197
   mecA:12:AB505628    100.00  2010/2010   contig00020 17294..19303
   #IS1272:2:AB033763  90.75   1579/1585   contig00033 2..1580
   #ccrC1-allele-2:1:AB512767  90.95   1680/1680   contig00022 9836..11515
   
   samtools faidx shovill/HDM10_contigs.fa
   samtools faidx shovill/HDM10_contigs.fa contig00020:4052-19403 > HDM10_sub.fna
   bakta --db /mnt/nvme0n1p1/bakta_db HDM10_sub.fna
   
   #4. -- HDM11-SF1_contigs.fa --
   
   No SCCmec element was detected
   
   Prediction based on genes:
   Predicted SCCmec element: none
   
   Prediction based on homology to whole cassette:
   Predicted whole cassette and %template coverage: none
   
   #5. -- HDM15-SF2_contigs.fa --
   
   SCCmec_type_IV(2B)
   SCCmec_type_VI(4B)
   Following gene complexes based on prediction of genes was detected :
   ccr class 2
   ccr class 4
   mec class B
   
   Predicted genes:
   Fasta header % Identity Query/HSP Length Contig Position in contig
   
   ccrA2:7:81108:AB096217  100.00  1350/1350   contig00004 3823..5172
   ccrB2:9:JCSC4469:AB097677   99.94   1650/1650   contig00004 5173..6822
   IS1272:3:AM292304   99.95   1844/1843   contig00004 8664..10507
   dmecR1:1:AB033763   100.00  987/987 contig00004 10496..11482
   mecA:12:AB505628    100.00  2010/2010   contig00004 11579..13588
   
   subtyppe-Vc(5C2&5):10:AB505629  99.84   1935/1935   contig00004 20148..22082
   
   ccrA4:2:BK20781:FJ670542    90.53   1362/1362   contig00004 24570..25931
   ccrB4:2:BK20781:FJ670542    91.68   1635/1629   contig00004 25928..27562
   
   subtype-IVa(2B):1:CA05:AB063172 100.00  1491/1491   contig00015 52228..53718
   
   samtools faidx shovill/HDM7_contigs.fa
   samtools faidx shovill/HDM7_contigs.fa contig00014:2700-18051 > HDM7_sub.fna
   bakta --db /mnt/nvme0n1p1/bakta_db HDM7_sub.fna
   
   samtools faidx shovill/HDM10_contigs.fa
   samtools faidx shovill/HDM10_contigs.fa contig00020:4052-19403 > HDM10_sub.fna
   bakta --db /mnt/nvme0n1p1/bakta_db HDM10_sub.fna
   
   samtools faidx shovill/HDM15-SF2_contigs.fa
   samtools faidx shovill/HDM15-SF2_contigs.fa contig00004:1-27662 > HDM15-SF2_sub.fna
   samtools faidx shovill/HDM15-SF2_contigs.fa contig00015:52128-53818 >> HDM15-SF2_sub.fna
   bakta --db /mnt/nvme0n1p1/bakta_db HDM15-SF2_sub.fna
   
   #END
   #172.104.140.19
   
   mkdir gbff_sub
   mv *_sub.gbff gbff_sub
   cd gbff_sub
   for f in *_sub.gbff; do mv "$f" "${f/_sub.gbff/.gbff}"; done
   #mv HDM1_sub.gbff HDM1.gbff
   #mv HDM7_sub.gbff HDM7.gbff
   #mv HDM10_sub.gbff HDM10.gbff
   #mv HDM15-SF2_sub.gbff HDM15-SF2.gbff
   
   rm *.json
   clinker *.gbff -p plot_HDRNA.html --dont_set_origin -s session_HDRNA.json -o alignments_HDRNA.csv -dl "," -dc 4
   
   cp ./gbff_HDRNA_01/clinker.png HDRNA_01_clinker.png

5. Arg typing

   grep "agrD" *.gbff | sort
   
   HDM1.gbff:                     /gene="agrD"
   HDM1.gbff:                     /gene="agrD"
   HDM7.gbff:                     /gene="agrD"
   HDM7.gbff:                     /gene="agrD"
   HDM10.gbff:                     /gene="agrD"
   HDM10.gbff:                     /gene="agrD"
   HDM11-SF1.gbff:                     /gene="agrD"
   HDM11-SF1.gbff:                     /gene="agrD"
   HDM15-SF2.gbff:                     /gene="agrD"
   HDM15-SF2.gbff:                     /gene="agrD"
   
   MNLLGGLLLKIFSNFMAVIGNASKYNPCSNYLDEPQVPEELTKLDE
   MENIFNLFIKFFTTILEFIGTVAGDSVCASYFDEPEVPEELTKLYE
   MENIFNLFIKFFTTILEFIGTVAGDSVCASYFDEPEVPEELTKLYE
   MNLLGGLLLKIFSNFMAVIGNASKYNPCSNYLDEPQVPEELTKLDE
   MNLLGGLLLKIFSNFMAVIGNASKYNPCSNYLDEPQVPEELTKLDE
   
   #* The agr typing is not defined, as I have compared the sequence with the amino acid sequences of ArgD described in the paper available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187671/. It does not correspond to Type I, Type II, or Type III. (For more details, see below).
   
   -- AgrD I --
   Query  1       MENIFNLFIKFFTTILEFIGTVAGDSVCASYFDEPEVPEELTKLYE  46
           M  +  L +K F+  +  IG  +  + C  Y DEP+VPEELTKL E
   Sbjct  926825  MNLLGGLLLKIFSNFMAVIGNASKYNPCVMYLDEPQVPEELTKLDE  926688
   
   -- AgrD II --
   Query  1       MNLLGGLLLKIFSNFMAVIGNASKYNPCSNYLDEPQVPEELTKLDE  46
           MNLLGGLLLKIFSNFMAVIGNASKYNPC  YLDEPQVPEELTKLDE
   Sbjct  926825  MNLLGGLLLKIFSNFMAVIGNASKYNPCVMYLDEPQVPEELTKLDE  926688
   
   -- AgrD III --
   Query  1       MNLLGGLLLKLFSNFMAVIGNAAKYNPCASYLDEPQVPEELTKLDE  46
           MNLLGGLLLK+FSNFMAVIGNA+KYNPC  YLDEPQVPEELTKLDE
   Sbjct  926825  MNLLGGLLLKIFSNFMAVIGNASKYNPCVMYLDEPQVPEELTKLDE  926688

6. calulate the presence-absence-matrix for predefined gene list

   #start codon: ATG, GTG und TTG
   #stop codon: 5'-UAA-3', 5'-UGA-3' und 5'-UAG-3' --> TAA, TGA, TAG
   
   ./Staphylococcus_aureus_MRSA252.fasta
   ./Staphylococcus_epidermidis_RP62A.fasta
   ./Enterococcus_faecium_isolate_E300_pathogenicity_island.fasta
   
   # -- Hause keeper: gyrB --
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":2609812-2611743 > gyrB.fasta
   #revcomp gyrB.fasta > gyrB_revcomp.fasta
   gyrB_revcomp.fasta
   
   # -- Metabolic genes: fumC, gltA, icd --
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":1444326-1445711 > fumC.fasta
   #revcomp fumC.fasta > fumC_revcomp.fasta
   ./fumC_revcomp.fasta
   ./gltA.fasta
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":1296195-1297463 > icd.fasta
   #revcomp icd.fasta > icd_revcomp.fasta
   icd_revcomp.fasta
   
   # -- Virulence regulartors: apsS, sigB, sarA, agrC, yycG --
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":316151-317191 > apsS.fasta
   apsS.fasta
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":1722805-1723575 > sigB.fasta
   #revcomp sigB.fasta > sigB_revcomp.fasta
   sigB_revcomp.fasta
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":279424-279798 > sarA.fasta
   #revcomp sarA.fasta > sarA_revcomp.fasta
   sarA_revcomp.fasta
   ./agrC.fasta
   ./yycG.fasta
   
   # -- Toxins: psmβ1, hlb --
   ./psm-beta.fasta
   #psm-beta1.fasta
   ./hlb_.fasta
   #./hlb.fasta
   
   # -- Biofilm formation: atlE, sdrG, sdrH, ebh, ebp, tagB --
   ./atlE.fasta
   ./sdrG.fasta
   
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":1555024-1556469 > sdrH.fasta
   #revcomp sdrH.fasta > sdrH_revcomp.fasta
   sdrH_revcomp.fasta
   
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":1023531-1053980 > ebh.fasta
   #revcomp ebh.fasta > ebh_revcomp.fasta
   ebh_revcomp.fasta
   
   #https://www.ncbi.nlm.nih.gov/gene/?term=(Elastin-binding+protein)+AND+%22Staphylococcus+aureus%22%5Bporgn%3A__txid1282%5D
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":1094204-1095586 > ebpS.fasta
   #revcomp ebpS.fasta > ebpS_revcomp.fasta
   ebpS_revcomp.fasta
   ./tagB.fasta
   
   # -- Immune evasion & colonization: capC, sepA, dltA, fmtC, lipA, sceD, SE0760 --
   
   ./capC.fasta
   #./capBCA_ywtC.fasta
   ./sepA.fasta
   #./ORF123_sepA_ORF5.fasta
   
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":503173-504630 > dltA.fasta
   dltA.fasta
   ./fmtC.fasta
   #samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":498445-499359 > lipA.fasta
   lipA.fasta
   ./sceD.fasta
   #./sceDAE.fasta
   ./SE0760.fasta
   
   # -- Serine protease: esp, ecpA --
   ./esp.fasta
   ./ecpA_.fasta
   #./ecpA.fasta
   
   # -- Phage: PhiSepi-HH1, PI-Sepi-HH2, PhiSepi-HH3 (#HH1-HP1, HH3-HP2, HH3-TreR) --
   ./MT880870.fasta
   ./MT880871.fasta
   ./MT880872.fasta
   
   #Note that write a message to Holger, say "ebp gene does not exist, instead of it only ebpS gene exists!"
   
   makeblastdb -in HDM1_contigs.fa -dbtype nucl
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query gyrB_revcomp.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gyrB_on_${sample}.blastn
   done
   
   ./fumC_revcomp.fasta
   ./gltA.fasta
   icd_revcomp.fasta
   
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query fumC_revcomp.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fumC_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query icd_revcomp.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > icd_on_${sample}.blastn
   done
   
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query apsS.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > apsS_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query sigB_revcomp.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sigB_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query sarA_revcomp.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sarA_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_${sample}.blastn
   done
   
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query psm-beta.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query psm-beta1.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query hlb_.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_${sample}.blastn
   done
   
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query atlE.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query sdrH_revcomp.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrH_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query ebh_revcomp.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ebh_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query ebpS_revcomp.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ebpS_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query tagB.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_${sample}.blastn
   done
   
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query capC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query sepA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query dltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > dltA_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query fmtC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query lipA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > lipA_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query sceD.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query SE0760.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_${sample}.blastn
   done
   
   # -- Serine protease: esp, ecpA --
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query esp.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ./esp_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query ecpA_.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_${sample}.blastn
   done
   
   # -- Phage: PhiSepi-HH1, PI-Sepi-HH2, PhiSepi-HH3 (#HH1-HP1, HH3-HP2, HH3-TreR) --
   #34053 (3000,2510) 36164 (500) 147057 (6618, 15237+3812, 15237+3812, 15233+3814, 15230+3812)
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ./MT880870_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ./MT880871_on_${sample}.blastn
   done
   for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2; do
     blastn -db ../shovill/${sample}_contigs.fa -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_${sample}.blastn
   done

Characterization_of_the_virulence_agr_typing_and_antimicrobial_resistance_profile_of_Staphylococcus_aureus_strains.pdf

1. Goal of data analyses

   S epidermidis genomes
       1. Assemble closed genomes from HDRNA 1, 3, 6, 7, 8, 12, 16, 17, 19, 20 (short read + long read)
           TODO: make a table similar to the paper Characterization of the virulence, agr typing and antimicrobial resistance profile of Staphylococcus aureus strains isolated from food handlers in Brazil Table 2, and draw a tree+heatmaps Figure!
           Based on closed genomes:
           - Sequence type,
           - goeBURST analysis (-->performed is geoBURST TLV-Analysis, see point 2),
           - SCCmec type (https://www.sccmec.org/index.php/en/method-to-identify-sccmmcc-smn-en/review-smn-en) https://cge.cbs.dtu.dk/services/SCCmecFinder/
             SCCmec typing: https://www.genomicepidemiology.org/ --> https://cge.food.dtu.dk/services/SCCmecFinder/
               SCCmecFinder 1.2
               SCCmecFinder identifies SCCmec elements in sequenced S. aureus isolates. The SCCmec element is the defining feature of methicillin-resistant S. aureus isolates, and encodes the single determinant for methicillin resistant, the mecA gene.
               IMPORTANT! SCCmec typing is only available for SCCmec type I-XI and subtyping is currently only available for SCCmec type IV and V
               IMPORTANT! mec gene complex C1 and C2 might produce errors.
   
           - agr typing: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187671/ (see point 3)
   
           - presence phage HH1, SPbeta-like phage, phage related island [referring to A´s paper] (see point 5),
   
           - absence/presence matrix for selected genes [see attached ppt, see results in Gene_List.pptx] (see point 6)
             gyrB,  fumC, , icd, apsS,
             sigB, sarA, , , ,
             ,  , sdrG(-17), sdrH, ebh, ebp (ebpS), ,
             , , dltA, , lipA,
             , , , , , ,
             --> draw a circle heatmaps including all data with a very big figure and table.
   
       2. according to assembled genomes describe within host diversity per patient (i.e. compare isolates 2 – 10 to isolate 1).
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_01_K01_conservative_23197.current.gb HDRNA_01_K01_CP133676.xlsx
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_03_K01_bold_bandage_26831.current.gb HDRNA_03_K01_CP133677.xlsx
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_06_K01_conservative_27645.current.gb HDRNA_06_K01_CP133678-CP133679.xlsx
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_07_K01_conservative_27169.current.gb HDRNA_07_K01_CP133680-CP133681.xlsx
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_08_K01_conservative_32455.current.gb HDRNA_08_K01_CP133682-CP133683.xlsx
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_12_K01_bold_37467.current.gb HDRNA_12_K01_CP133684-CP133687.xlsx
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_16_K01_conservative_37834.current.gb HDRNA_16_K01_CP133688-CP133692.xlsx
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_17_K01_conservative_37288.current.gb HDRNA_17_K01_CP133693-CP133695.xlsx
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_19_K01_bold_37377.current.gb HDRNA_19_K01_CP133696-CP133699.xlsx
           python3 /home/jhuang/Scripts/gb_to_excel.py ./gbks/HDRNA_20_K01_conservative_43457.current.gb HDRNA_01_K01_CP133700-CP133701.xlsx
   
           * SNP INDEL: snippy+spandx --> get the complete list of SNP+INDEL for each isolate group!!! (see point 4)
   
           * gene absence/presence:
           # A: prepare prokka_HDRNA_01 .. prokka_HDRNA_20 from prokka_remaining
   
           # B:         #https://github.com/jorvis/biocode/blob/master/gff/convert_genbank_to_gff3.py
           sudo apt-get install -y python3 python3-pip zlib1g-dev libblas-dev liblapack-dev libxml2-dev
           pip3 install biocode
           convert_genbank_to_gff3.py -i HDRNA_01_K01_conservative_23197.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_01/HDRNA_01_K01.gff --with_fasta
           convert_genbank_to_gff3.py -i HDRNA_03_K01_bold_bandage_26831.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_03/HDRNA_03_K01.gff --with_fasta
           convert_genbank_to_gff3.py -i HDRNA_06_K01_conservative_27645.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_06/HDRNA_06_K01.gff --with_fasta
           convert_genbank_to_gff3.py -i HDRNA_07_K01_conservative_27169.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_07/HDRNA_07_K01.gff --with_fasta
           convert_genbank_to_gff3.py -i HDRNA_08_K01_conservative_32455.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_08/HDRNA_08_K01.gff --with_fasta
           convert_genbank_to_gff3.py -i HDRNA_12_K01_bold_37467.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_12/HDRNA_12_K01.gff --with_fasta
           convert_genbank_to_gff3.py -i HDRNA_16_K01_conservative_37834.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_16/HDRNA_16_K01.gff --with_fasta
           convert_genbank_to_gff3.py -i HDRNA_17_K01_conservative_37288.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_17/HDRNA_17_K01.gff --with_fasta
           convert_genbank_to_gff3.py -i HDRNA_19_K01_bold_37377.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_19/HDRNA_19_K01.gff --with_fasta
           convert_genbank_to_gff3.py -i HDRNA_20_K01_conservative_43457.current.gb -o ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/Data_Holger_S.epidermidis_short/prokka_HDRNA_20/HDRNA_20_K01.gff --with_fasta
   
           # C: check gff.files if containing the repeated gene names using check_duplicate_cds.py, remove manually, recheck the removed one.
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_01_K01.gff
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_03_K01.gff
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_06_K01.gff
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_07_K01.gff
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_08_K01.gff
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_12_K01.gff
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_16_K01.gff
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_17_K01.gff
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_19_K01.gff
           python3 ~/Scripts/check_duplicate_cds.py HDRNA_20_K01.gff
               Found duplicates for the following CDS IDs:
               RE430_09810.mRNA.0.CDS.1
               RE430_08580.mRNA.0.CDS.1
               RE430_09730.mRNA.0.CDS.1
               RGR13_00575.mRNA.0.CDS.1
               RGR10_09570.mRNA.0.CDS.1
               RGR06_10025.mRNA.0.CDS.1
               RGR06_12305.mRNA.0.CDS.1
               #xxxx
               RGR12_09490.mRNA.0.CDS.1
               RGR12_00425.mRNA.0.CDS.1
               RGR12_11570.mRNA.0.CDS.1
               RGR09_12270.mRNA.0.CDS.1
               RGR09_09635.mRNA.0.CDS.1
               RGR09_01280.mRNA.0.CDS.1
               RGR08_01845.mRNA.0.CDS.1
               RGR08_13135.mRNA.0.CDS.1
               RGR08_10360.mRNA.0.CDS.1
               RGR08_09845.mRNA.0.CDS.1
               RGR14_00635.mRNA.0.CDS.1
               RGR14_00355.mRNA.0.CDS.1
               RGR14_09870.mRNA.0.CDS.1
               RGR14_01550.mRNA.0.CDS.1
   
               RGR07_09075.mRNA.0.CDS.1
               RGR07_00190.mRNA.0.CDS.1
               RGR07_11485.mRNA.0.CDS.1
               RGR07_00090.mRNA.0.CDS.1
               RGR07_11545.mRNA.0.CDS.1
               RGR07_01915.mRNA.0.CDS.1
               RGR11_01280.mRNA.0.CDS.1
               RGR11_09700.mRNA.0.CDS.1
   
           rsync -a -P prokka_HDRNA_01/HDRNA_01_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_01/
           rsync -a -P prokka_HDRNA_03/HDRNA_03_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_03/
           rsync -a -P prokka_HDRNA_06/HDRNA_06_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_06/
           rsync -a -P prokka_HDRNA_07/HDRNA_07_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_07/
           rsync -a -P prokka_HDRNA_08/HDRNA_08_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_08/
           rsync -a -P prokka_HDRNA_12/HDRNA_12_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_12/
           rsync -a -P prokka_HDRNA_16/HDRNA_16_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_16/
           rsync -a -P prokka_HDRNA_17/HDRNA_17_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_17/
           rsync -a -P prokka_HDRNA_19/HDRNA_19_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_19/
           rsync -a -P prokka_HDRNA_20/HDRNA_20_K01.gff jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_20/
   
           cd prokka_HDRNA_01
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_01_K01.gff ./HDRNA_01_K02/HDRNA_01_K02.gff ./HDRNA_01_K03/HDRNA_01_K03.gff ./HDRNA_01_K04/HDRNA_01_K04.gff ./HDRNA_01_K05/HDRNA_01_K05.gff ./HDRNA_01_K06/HDRNA_01_K06.gff ./HDRNA_01_K07/HDRNA_01_K07.gff ./HDRNA_01_K08/HDRNA_01_K08.gff ./HDRNA_01_K09/HDRNA_01_K09.gff ./HDRNA_01_K10/HDRNA_01_K10.gff
           cd ../prokka_HDRNA_03
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_03_K01.gff ./HDRNA_03_K02/HDRNA_03_K02.gff ./HDRNA_03_K03/HDRNA_03_K03.gff ./HDRNA_03_K04/HDRNA_03_K04.gff ./HDRNA_03_K05/HDRNA_03_K05.gff ./HDRNA_03_K06/HDRNA_03_K06.gff ./HDRNA_03_K07/HDRNA_03_K07.gff ./HDRNA_03_K08/HDRNA_03_K08.gff ./HDRNA_03_K09/HDRNA_03_K09.gff ./HDRNA_03_K10/HDRNA_03_K10.gff
           cd ../prokka_HDRNA_06
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_06_K01.gff ./HDRNA_06_K02/HDRNA_06_K02.gff ./HDRNA_06_K03/HDRNA_06_K03.gff ./HDRNA_06_K04/HDRNA_06_K04.gff ./HDRNA_06_K05/HDRNA_06_K05.gff ./HDRNA_06_K06/HDRNA_06_K06.gff ./HDRNA_06_K07/HDRNA_06_K07.gff ./HDRNA_06_K08/HDRNA_06_K08.gff ./HDRNA_06_K09/HDRNA_06_K09.gff ./HDRNA_06_K10/HDRNA_06_K10.gff
           cd ../prokka_HDRNA_07
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_07_K01.gff ./HDRNA_07_K02/HDRNA_07_K02.gff ./HDRNA_07_K03/HDRNA_07_K03.gff ./HDRNA_07_K04/HDRNA_07_K04.gff ./HDRNA_07_K05/HDRNA_07_K05.gff ./HDRNA_07_K06/HDRNA_07_K06.gff ./HDRNA_07_K07/HDRNA_07_K07.gff ./HDRNA_07_K08/HDRNA_07_K08.gff ./HDRNA_07_K09/HDRNA_07_K09.gff ./HDRNA_07_K10/HDRNA_07_K10.gff
           cd ../prokka_HDRNA_08
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_08_K01.gff ./HDRNA_08_K02/HDRNA_08_K02.gff ./HDRNA_08_K03/HDRNA_08_K03.gff ./HDRNA_08_K04/HDRNA_08_K04.gff ./HDRNA_08_K05/HDRNA_08_K05.gff ./HDRNA_08_K06/HDRNA_08_K06.gff ./HDRNA_08_K07/HDRNA_08_K07.gff ./HDRNA_08_K08/HDRNA_08_K08.gff ./HDRNA_08_K09/HDRNA_08_K09.gff ./HDRNA_08_K10/HDRNA_08_K10.gff
           cd ../prokka_HDRNA_12
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_12_K01.gff ./HDRNA_12_K02/HDRNA_12_K02.gff ./HDRNA_12_K03/HDRNA_12_K03.gff ./HDRNA_12_K04/HDRNA_12_K04.gff ./HDRNA_12_K05/HDRNA_12_K05.gff ./HDRNA_12_K06/HDRNA_12_K06.gff ./HDRNA_12_K07/HDRNA_12_K07.gff ./HDRNA_12_K08/HDRNA_12_K08.gff ./HDRNA_12_K09/HDRNA_12_K09.gff ./HDRNA_12_K10/HDRNA_12_K10.gff
           cd ../prokka_HDRNA_16
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_16_K01.gff ./HDRNA_16_K02/HDRNA_16_K02.gff ./HDRNA_16_K03/HDRNA_16_K03.gff ./HDRNA_16_K04/HDRNA_16_K04.gff ./HDRNA_16_K05/HDRNA_16_K05.gff ./HDRNA_16_K06/HDRNA_16_K06.gff ./HDRNA_16_K07/HDRNA_16_K07.gff ./HDRNA_16_K08/HDRNA_16_K08.gff ./HDRNA_16_K09/HDRNA_16_K09.gff ./HDRNA_16_K10/HDRNA_16_K10.gff
           cd ../prokka_HDRNA_17
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_17_K01.gff ./HDRNA_17_K02/HDRNA_17_K02.gff ./HDRNA_17_K03/HDRNA_17_K03.gff ./HDRNA_17_K04/HDRNA_17_K04.gff ./HDRNA_17_K05/HDRNA_17_K05.gff ./HDRNA_17_K06/HDRNA_17_K06.gff ./HDRNA_17_K07/HDRNA_17_K07.gff ./HDRNA_17_K08/HDRNA_17_K08.gff ./HDRNA_17_K09/HDRNA_17_K09.gff ./HDRNA_17_K10/HDRNA_17_K10.gff
           cd ../prokka_HDRNA_19
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_19_K01.gff ./HDRNA_19_K02/HDRNA_19_K02.gff ./HDRNA_19_K03/HDRNA_19_K03.gff ./HDRNA_19_K04/HDRNA_19_K04.gff ./HDRNA_19_K05/HDRNA_19_K05.gff ./HDRNA_19_K06/HDRNA_19_K06.gff ./HDRNA_19_K07/HDRNA_19_K07.gff ./HDRNA_19_K08/HDRNA_19_K08.gff ./HDRNA_19_K09/HDRNA_19_K09.gff ./HDRNA_19_K10/HDRNA_19_K10.gff
           cd ../prokka_HDRNA_20
           roary -p 5 -f ./roary -i 95 -cd 99 -s -e -n -v ./HDRNA_20_K01.gff ./HDRNA_20_K02/HDRNA_20_K02.gff ./HDRNA_20_K03/HDRNA_20_K03.gff ./HDRNA_20_K04/HDRNA_20_K04.gff ./HDRNA_20_K05/HDRNA_20_K05.gff ./HDRNA_20_K06/HDRNA_20_K06.gff ./HDRNA_20_K07/HDRNA_20_K07.gff ./HDRNA_20_K08/HDRNA_20_K08.gff ./HDRNA_20_K09/HDRNA_20_K09.gff
           cd ..
   
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_01/roary prokka_HDRNA_01
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_03/roary prokka_HDRNA_03
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_06/roary prokka_HDRNA_06
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_07/roary prokka_HDRNA_07
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_08/roary prokka_HDRNA_08
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_12/roary prokka_HDRNA_12
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_16/roary prokka_HDRNA_16
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_17/roary prokka_HDRNA_17
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_19/roary prokka_HDRNA_19
           rsync -a -P jhuang@hamm:~/DATA/Data_Holger_S.epidermidis_short/prokka_HDRNA_20/roary prokka_HDRNA_20
   
           cp prokka_HDRNA_01/roary/gene_presence_absence.csv gene_presence_absence__HDRNA_01.csv
           cp prokka_HDRNA_03/roary/gene_presence_absence.csv gene_presence_absence_HDRNA_03.csv
           cp prokka_HDRNA_06/roary/gene_presence_absence.csv gene_presence_absence_HDRNA_06.csv
           cp prokka_HDRNA_07/roary/gene_presence_absence.csv gene_presence_absence_HDRNA_07.csv
           cp prokka_HDRNA_08/roary/gene_presence_absence.csv gene_presence_absence_HDRNA_08.csv
           cp prokka_HDRNA_12/roary/gene_presence_absence.csv gene_presence_absence_HDRNA_12.csv
           cp prokka_HDRNA_16/roary/gene_presence_absence.csv gene_presence_absence_HDRNA_16.csv
           cp prokka_HDRNA_17/roary/gene_presence_absence.csv gene_presence_absence_HDRNA_17.csv
           cp prokka_HDRNA_19/roary/gene_presence_absence.csv gene_presence_absence_HDRNA_19.csv
           cp prokka_HDRNA_20/roary/gene_presence_absence.csv gene_presence_absence_HDRNA_20.csv
   
           #Wenn man will open the files mit libreoffice, needs "," --> "|"; "\n"-->\n; the first and last " in the text; in the kate, then open file with libreoffice with delimiter '|'.
   
           sed -i 's/\.mRNA\.0\.CDS\.1//g' gene_presence_absence__HDRNA_01.csv
   
           group_10||Y_phosphoryl: pyrimidine-nucleoside phosphorylase|10|11|1.11|1|416||||362|1301|1165|RE430_03860|HDRNA_01_K02_02111|HDRNA_01_K03_02181|HDRNA_01_K04_02110|HDRNA_01_K05_02181|HDRNA_01_K06_02156|HDRNA_01_K07_02196     HDRNA_01_K07_02197|HDRNA_01_K08_01754|HDRNA_01_K09_02226|HDRNA_01_K10_02107
   
           group_10||Y_phosphoryl: pyrimidine-nucleoside phosphorylase|10|11|1.11|1|416||||362|1301|1165|RE430_03860.mRNA.0.CDS.1|HDRNA_01_K02_02111|HDRNA_01_K03_02181|HDRNA_01_K04_02110|HDRNA_01_K05_02181|HDRNA_01_K06_02156|HDRNA_01_K07_02196    HDRNA_01_K07_02197|HDRNA_01_K08_01754|HDRNA_01_K09_02226|HDRNA_01_K10_02107
   
           for file in gene_presence_absence_HDRNA_*; do
               sed -i 's/\.mRNA\.0\.CDS\.1//g' "$file"
           done
   
           ~/Tools/csv2xls-0.4/csv_to_xls.py gene_presence_absence__HDRNA_01.csv gene_presence_absence_HDRNA_03.csv gene_presence_absence_HDRNA_06.csv gene_presence_absence_HDRNA_07.csv gene_presence_absence_HDRNA_08.csv gene_presence_absence_HDRNA_12.csv gene_presence_absence_HDRNA_16.csv gene_presence_absence_HDRNA_17.csv gene_presence_absence_HDRNA_19.csv gene_presence_absence_HDRNA_20.csv -d'|' -o gene_presence_absence.xls
   
           * genomic rearrangements (e.g. SCCmec deletions, ACME deletions, agr insertions)
             TODOs using Easyfig!
             #Staphylococcal Cassette Chromosome mec
             #arginine catabolic mobile element (ACME)
   
           #Prevalence and genetic diversity of arginine catabolic mobile element (ACME) in clinical isolates of coagulase-negative staphylococci: identification of ACME type I variants in Staphylococcus epidermidis.
           Fig. 1. A schematic drawing of genetic structures of ACME (a region from the arc to opp3 cluster, or corresponding genetic components) among the three DI subtypes (DI.1, DI.2, and DI.3: strains CNS266, CNS115, and CNS149, respectively), type I (strain USA300-FPR3757, accession number CP000255), type II (strain ATCC12228, accession number AE015929) and type DII (strain M08/0126, accession number FR753166). Putative ORFs of genes are represented by arrows colored with green (arc cluster), red (opp3 cluster), blue (a region between the arc and opp3 clusters in ACME I), or dark blue (genes in ACME II). The regions in light pink including the arc cluster indicate genetically identical areas to both ATCC12228 and USA300-FPR3757. The regions with light blue are identical to only ATCC12228, while those with light orange to USA300-FPR3757. White space regions between argR and SAUSA300_0072 show no sequence homology either to ATCC12228 or to USA300_FPR3757; however, these regions show 91–99% nucleotide identity among the three ACME subtypes. Regions colored with dark orange in the three ACME DI subtypes show=98% nucleotide sequence identity to each other. Regions colored with grey (type DI.1), purple or cyan (type DI.3) do not show high nucleotide identity (<98%) to cognate genes in other ACME types (Table S2.2). Positions of primers used for PCR profile (Tables 1 and 4) are shown with arrowheads under ACME I sequence. Collapse
   
             #Smash++: https://academic.oup.com/gigascience/article/9/5/giaa048/5841055
             #https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597632/
   
           Artemis Comparison Tool (ACT): Allows for the visual comparison of genomes and can be used to investigate the presence or absence of genomic regions (such as SCCmec or ACME) and other structural variations.
           #https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1009304
           #https://journals.plos.org/plospathogens/article/figure?id=10.1371/journal.ppat.1009304.g010
   
           The image you uploaded appears to be a schematic representation of genomic rearrangements, typically found in scientific publications or reports. These kinds of images are often created using bioinformatics visualization tools or general graphic design software. While it's not possible to determine the exact software used to create this specific image without more context, I can suggest several tools commonly used for such purposes:
   
       3. Epidome: (using R?) Create bar plot showing ST distribution in noses from patients HDRNA 1, 3, 6,   --> 7, 8, 12, 16, 17, 19, 20 <--
       (Easy using R) Create table showing presence / absence of STs / per patient
       NOTE: Epidome data have not been processed due to potential missing of the data; At first sending the results without Epidome results, ask again where is the epidome data for the 10 patients?

2. generate goeBURST

   5,1,1,1,2,2,1,1
   87,7,1,1,2,2,1,1
   35,2,1,2,2,4,1,1
   69,1,18,6,2,2,1,1
   
   23,7,1,2,1,3,3,1
   224,19,16,19,6,3,19,10
   640,28,3,13,5,8,9,11
   
   '-',1,13,2,1,2,1,29
   
   in goeBURST-1.2.1.jar, I have gelesen "Edge level to define group SLV DLV TLV". Wie kann ich generate SLV, DLV and TLV files?
   
   The input you've provided seems to represent allelic profiles or sequence types (STs) used in microbial typing, particularly in methods like Multilocus Sequence Typing (MLST). When you input these profiles into software like goeBURST (implemented in a tool like PHYLOViZ), the software uses these profiles to construct a phylogenetic network. The network shows relationships between different strains or isolates based on their allelic similarity.
   
   In goeBURST, groups are defined based on their allelic differences:
   
   SLV (Single-Locus Variants): Strains or STs differing by only one locus.
   DLV (Double-Locus Variants): Strains or STs differing by two loci.
   TLV (Triple-Locus Variants): Strains or STs differing by three loci.

3. agr typing S. epidermidis

   > Comparison of the amino acid sequences of a region of the N-terminus of AgrC (A), and AgrB (B) of S. epidermidis (S.e.), S. aureus (S.a.) and S. lugdunensis (S.l.).
   > https://academic.oup.com/femsle/article/163/1/1/625220: Cloning and characterization of an accessory gene regulator (agr)-like locus from Staphylococcus epidermidis
   
   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5064449/
   https://brieflands.com/articles/archcid-62833#4.-Results
   
   https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=1282
   Staphylococcus epidermidis Agr Operon
   https://academic.oup.com/femspd/article/51/1/220/501159
   
   #pAgr (S. epidermidis agr operon promoter)
   https://parts.igem.org/Part:BBa_K212003#
   
   - An overall homology of 68% was found between the agr locus from S. epidermidis and S. aureus.
   - The agr locus from S. epidermidis was organized similar to those from S. aureus and S. lugdunensis.
   - The putative RNAII molecule contains four open reading frames, agrA, B, C and D. AgrA was a response regulator.
   - AgrB showed homology with transducer and translocase molecules.
   - AgrC is expected to act as a histidine protein kinase in which a leucine zipper is present.
   - AgrD is presumably processed into an autoinducer peptide.
   
   - For a long time, Staphylococcus epidermidis, as a member of the coagulase-negative staphylococci, was considered as part of the physiological skin flora of the human being with no pathogenic significance.
   - Today, we know that S. epidermidis is one of the most prevalent causes for implant-associated and nosocomial infections.
   - We performed pheno- and genotypic analysis (ica, IS256, SCCmec types, agr groups) of biofilm formation in 200 isolates.
   - Fifty percent were genetically ica-positive and produced biofilm.
   - Among all studied isolates, agr II and III and SCCmec type I were the most prevalent, whereas within the selected multi-resistant isolates (29%), agr I and III and SCCmec type II dominated.
   - SCCmec type I and mecA-negative S. epidermidis isolates were associated with agr II.
   - The majority of the blood culture and biopsy isolates were assigned to agr III and SCCmec type I, whereas agr II was predominantly detected in mecA-negative S. epidermidis isolated from catheter and implant materials.
   - MLST analysis revealed the major clonal lineages of ST2, ST5, ST10, and ST242 (total 13 STs).
   - ST2 isolates from blood cultures were icaA/D-positive and harbored SCCmec types II and III and IS256, whereas the icaA/D- and IS256-positive ST23 isolates were assigned to SCCmec types I and IV.
   
   # -- OPTION 1 using AgrVATE gp1-4-operon_ref.fasta (failed since the database contains only agr from Staphylococcus aureus) --
   
   > Species-Wide Phylogenomics of the Staphylococcus aureus Agr Operon Revealed Convergent Evolution of Frameshift Mutations
   
   makeblastdb -in HDRNA_K01.fna -dbtype nucl
   -perc_identity 90 -qcov_hsp_perc 90
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query /home/jhuang/Tools/AgrVATE/agrvate_databases/references/gp1-4-operon_ref.fasta -evalue 0.1 -num_threads 15 -outfmt "6 sseqid qseqid evalue pident sstart send" -strand both -max_target_seqs 1
   
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query gp1-4-operon_ref.fasta -evalue 0.1 -num_threads 15 -outfmt "6 sseqid qseqid evalue pident sstart send" -strand both > HDRNA_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query /home/jhuang/Tools/AgrVATE/agrvate_databases/references/gp1-4-operon_ref.fasta -evalue 0.1 -num_threads 15 -outfmt "6 sseqid qseqid evalue pident sstart send" -strand both > HDRNA_03.blastn
   
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query /home/jhuang/Tools/AgrVATE/agrvate_databases/gp1234_all_motifs.fna -evalue 0.1 -num_threads 15 -outfmt "6 sseqid qseqid evalue pident sstart send" -strand both > HDRNA_01_.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query /home/jhuang/Tools/AgrVATE/agrvate_databases/gp1234_all_motifs.fna -evalue 0.1 -num_threads 15 -outfmt "6 sseqid qseqid evalue pident sstart send" -strand both > HDRNA_03_.blastn
   
   # -- OPTION 2 using agr-1-3_Se.fasta (failed!) --
   
   > High Genetic Variablity of the agr Locus in Staphylococcus Species
   
   >gi|3320006|emb|Z49220.1| Staphylococcus epidermidis hld and agr[A,B,C,D] genes
   
   >gi|18251022|gb|AF346724.1| Staphylococcus epidermidis strain N910160 AgrB (agrB) gene, partial cds; AgrD (agrD) gene, complete cds; and AgrC (agrC) gene, partial cds
   
   >gi|18251026|gb|AF346725.1| Staphylococcus epidermidis strain N910191 AgrB (agrB) gene, partial cds; AgrD (agrD) gene, complete cds; and AgrC (agrC) gene, partial cds
   
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query agr-1-3_Se.fasta -evalue 0.1 -num_threads 15 -outfmt 6 -strand both > HDRNA_01__.blastn
   
   # -- OPTION 3 using agrD_I-III.fasta (successful!) --
   
   > Staphylococcus epidermidis agr Quorum-Sensing System: Signal Identification, Cross Talk, and Importance in Colonization
   
   > https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187671/
   
   > https://www.researchgate.net/publication/264391883
   
   tblastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_01.tblastn
   tblastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_03.tblastn
   tblastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_06.tblastn
   tblastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_07.tblastn
   tblastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_08.tblastn
   tblastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_12.tblastn
   tblastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_16.tblastn
   tblastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_17.tblastn
   tblastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_19.tblastn
   tblastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna  -query agrD_I-III.fasta -evalue 0.1 -num_threads 15  > HDRNA_20.tblastn
   
   II?
   II
   I
   II
   I
   
   II
   II
   II
   III
   II
   
   >AgrD_I
   MENIFNLFIKFFTTILEFIGTVAGDSVCASYFDEPEVPEELTKLYE
   >AgrD_II
   MNLLGGLLLKIFSNFMAVIGNASKYNPCSNYLDEPQVPEELTKLDE
   >AgrD_III
   MNLLGGLLLKLFSNFMAVIGNAAKYNPCASYLDEPQVPEELTKLDE

4. Variant (SNP+INDEL) calling

   Input files:
       HDRNA_01_K01_conservative_23197.current.gb
   
       HDRNA_01_K01_conservative_23197.current.gb:LOCUS       CP133676             2502964 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_03_K01_bold_bandage_26831.current.gb:LOCUS       CP133677             2590275 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_06_K01_conservative_27645.current.gb:LOCUS       CP133678             2465260 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_06_K01_conservative_27645.current.gb:LOCUS       CP133679               19348 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_07_K01_conservative_27169.current.gb:LOCUS       CP133680             2544074 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_07_K01_conservative_27169.current.gb:LOCUS       CP133681                2241 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_08_K01_conservative_32455.current.gb:LOCUS       CP133682             2425353 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_08_K01_conservative_32455.current.gb:LOCUS       CP133683                6358 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_12_K01_bold_37467.current.gb:LOCUS       CP133684             2490139 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_12_K01_bold_37467.current.gb:LOCUS       CP133685               43849 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_12_K01_bold_37467.current.gb:LOCUS       CP133686                6642 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_12_K01_bold_37467.current.gb:LOCUS       CP133687                2241 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_16_K01_conservative_37834.current.gb:LOCUS       CP133688             2626440 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_16_K01_conservative_37834.current.gb:LOCUS       CP133689               24906 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_16_K01_conservative_37834.current.gb:LOCUS       CP133690               20828 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_16_K01_conservative_37834.current.gb:LOCUS       CP133691                4567 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_16_K01_conservative_37834.current.gb:LOCUS       CP133692                2242 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_17_K01_conservative_37288.current.gb:LOCUS       CP133693             2503072 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_17_K01_conservative_37288.current.gb:LOCUS       CP133694               29861 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_17_K01_conservative_37288.current.gb:LOCUS       CP133695                4439 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_19_K01_bold_37377.current.gb:LOCUS       CP133696             2362062 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_19_K01_bold_37377.current.gb:LOCUS       CP133697               55320 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_19_K01_bold_37377.current.gb:LOCUS       CP133698               46464 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_19_K01_bold_37377.current.gb:LOCUS       CP133699               11827 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_20_K01_conservative_43457.current.gb:LOCUS       CP133700             2490778 bp    DNA     circular BCT 05-SEP-2023
       HDRNA_20_K01_conservative_43457.current.gb:LOCUS       CP133701                2241 bp    DNA     circular BCT 05-SEP-2023
   
   ln -s /home/jhuang/Tools/spandx/ spandx
   
   nextflow run spandx/main.nf --fastq "trimmed_HDRNA_01/*_P_{1,2}.fastq.gz" --ref db/CP133676.fasta --annotation --database CP133676 -resume
   mv work work_CP133676
   mv Outputs Outputs_CP133676
   
   for fasta_file in CP133677 CP133678 CP133679 CP133680 CP133681 CP133682 CP133683 CP133684 CP133685 CP133686 CP133687 CP133688 CP133689 CP133690 CP133691 CP133692 CP133693 CP133694 CP133695 CP133696 CP133697 CP133698 CP133699 CP133700 CP133701; do
     echo "nextflow run spandx/main.nf --fastq "trimmed_HDRNA_01/*_P_{1,2}.fastq.gz" --ref db/${fasta_file}.fasta --annotation --database ${fasta_file} -resume"
     echo "mv work work_${fasta_file}"
     echo "mv Outputs Outputs_${fasta_file}"
   done
   
   for file in *.fastq.gz; do mv $file $(echo $file | cut -d'_' -f1)-$(echo $file | cut -d'_' -f1)-$(echo $file | cut -d'_' -f3)_$(echo $file | cut -d'_' -f6); done
   
   for file in *.fastq.gz; do mv $file $(echo $file | cut -d'_' -f3)_$(echo $file | cut -d'_' -f6); done
   for file in *.fastq.gz; do mv $file $(echo $file | cut -d'_' -f1)-$(echo $file | cut -d'_' -f2); done
   for file in *.fastq.gz; do mv $file $(echo $file | cut -d'-' -f1)_$(echo $file | cut -d'-' -f2); done
   
   Input read files could not be found.
   Have you included the read files in the current directory and do they have the correct naming?
   With the parameters specified, SPANDx is looking for reads named *_{1,2}.fastq.gz.
   To fix this error either rename your reads to match this formatting or specify the desired format
   when initializing SPANDx e.g. --fastq "*_{1,2}_sequence.fastq.gz"
   
   cd trimmed_HDRNA_01
   nextflow run ../spandx/main.nf --ref ../db/CP133676.fasta --annotation --database CP133676 -resume
   mv Outputs Outputs_CP133676
   cd ..
   
   cd trimmed_HDRNA_03
   nextflow run ../spandx/main.nf --ref ../db/CP133677.fasta --annotation --database CP133677 -resume
   mv Outputs Outputs_CP133677
   cd ..
   
   cd trimmed_HDRNA_06
   nextflow run ../spandx/main.nf --ref ../db/CP133678.fasta --annotation --database CP133678 -resume
   mv work work_CP133678
   mv Outputs Outputs_CP133678
   nextflow run ../spandx/main.nf --ref ../db/CP133679.fasta --annotation --database CP133679 -resume
   mv work work_CP133679
   mv Outputs Outputs_CP133679
   cd ..
   
   cd trimmed_HDRNA_07
   nextflow run ../spandx/main.nf --ref ../db/CP133680.fasta --annotation --database CP133680 -resume
   mv work work_CP133680
   mv Outputs Outputs_CP133680
   nextflow run ../spandx/main.nf --ref ../db/CP133681.fasta --annotation --database CP133681 -resume
   mv work work_CP133681
   mv Outputs Outputs_CP133681
   cd ..
   
   cd trimmed_HDRNA_08
   nextflow run ../spandx/main.nf --ref ../db/CP133682.fasta --annotation --database CP133682 -resume
   mv work work_CP133682
   mv Outputs Outputs_CP133682
   nextflow run ../spandx/main.nf --ref ../db/CP133683.fasta --annotation --database CP133683 -resume
   mv work work_CP133683
   mv Outputs Outputs_CP133683
   cd ..
   
   cd trimmed_HDRNA_12
   nextflow run ../spandx/main.nf --ref ../db/CP133684.fasta --annotation --database CP133684 -resume
   mv work work_CP133684
   mv Outputs Outputs_CP133684
   nextflow run ../spandx/main.nf --ref ../db/CP133685.fasta --annotation --database CP133685 -resume
   mv work work_CP133685
   mv Outputs Outputs_CP133685
   nextflow run ../spandx/main.nf --ref ../db/CP133686.fasta --annotation --database CP133686 -resume
   mv work work_CP133686
   mv Outputs Outputs_CP133686
   nextflow run ../spandx/main.nf --ref ../db/CP133687.fasta --annotation --database CP133687 -resume
   mv work work_CP133687
   mv Outputs Outputs_CP133687
   cd ..
   
   cd trimmed_HDRNA_16
   nextflow run ../spandx/main.nf --ref ../db/CP133688.fasta --annotation --database CP133688 -resume
   mv work work_CP133688
   mv Outputs Outputs_CP133688
   nextflow run ../spandx/main.nf --ref ../db/CP133689.fasta --annotation --database CP133689 -resume
   mv work work_CP133689
   mv Outputs Outputs_CP133689
   nextflow run ../spandx/main.nf --ref ../db/CP133690.fasta --annotation --database CP133690 -resume
   mv work work_CP133690
   mv Outputs Outputs_CP133690
   nextflow run ../spandx/main.nf --ref ../db/CP133691.fasta --annotation --database CP133691 -resume
   mv work work_CP133691
   mv Outputs Outputs_CP133691
   nextflow run ../spandx/main.nf --ref ../db/CP133692.fasta --annotation --database CP133692 -resume
   mv work work_CP133692
   mv Outputs Outputs_CP133692
   cd ..
   
   cd trimmed_HDRNA_17
   nextflow run ../spandx/main.nf --ref ../db/CP133693.fasta --annotation --database CP133693 -resume
   mv work work_CP133693
   mv Outputs Outputs_CP133693
   nextflow run ../spandx/main.nf --ref ../db/CP133694.fasta --annotation --database CP133694 -resume
   mv work work_CP133694
   mv Outputs Outputs_CP133694
   nextflow run ../spandx/main.nf --ref ../db/CP133695.fasta --annotation --database CP133695 -resume
   mv work work_CP133695
   mv Outputs Outputs_CP133695
   cd ..
   
   cd trimmed_HDRNA_19
   nextflow run ../spandx/main.nf --ref ../db/CP133696.fasta --annotation --database CP133696 -resume
   mv work work_CP133696
   mv Outputs Outputs_CP133696
   nextflow run ../spandx/main.nf --ref ../db/CP133697.fasta --annotation --database CP133697 -resume
   mv work work_CP133697
   mv Outputs Outputs_CP133697
   nextflow run ../spandx/main.nf --ref ../db/CP133698.fasta --annotation --database CP133698 -resume
   mv work work_CP133698
   mv Outputs Outputs_CP133698
   nextflow run ../spandx/main.nf --ref ../db/CP133699.fasta --annotation --database CP133699 -resume
   mv work work_CP133699
   mv Outputs Outputs_CP133699
   cd ..
   
   cd trimmed_HDRNA_20
   nextflow run ../spandx/main.nf --ref ../db/CP133700.fasta --annotation --database CP133700 -resume
   mv work work_CP133700
   mv Outputs Outputs_CP133700
   nextflow run ../spandx/main.nf --ref ../db/CP133701.fasta --annotation --database CP133701 -resume
   mv work work_CP133701
   mv Outputs Outputs_CP133701
   cd ..
   
   #-------------------------
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_01/Outputs_CP133676 .
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_03/Outputs_CP133677 .
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_06/Outputs_CP133678 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_06/Outputs_CP133679 .
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_07/Outputs_CP133680 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_07/Outputs_CP133681 .
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_08/Outputs_CP133682 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_08/Outputs_CP133683 .
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_12/Outputs_CP133684 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_12/Outputs_CP133685 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_12/Outputs_CP133686 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_12/Outputs_CP133687 .
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_16/Outputs_CP133688 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_16/Outputs_CP133689 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_16/Outputs_CP133690 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_16/Outputs_CP133691 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_16/Outputs_CP133692 .
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_17/Outputs_CP133693 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_17/Outputs_CP133694 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_17/Outputs_CP133695 .
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_19/Outputs_CP133696 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_19/Outputs_CP133697 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_19/Outputs_CP133698 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_19/Outputs_CP133699 .
   
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_20/Outputs_CP133700 .
   rsync -a -P jhuang@hamm:/home/jhuang/DATA/Data_Holger_S.epidermidis_short/trimmed_HDRNA_20/Outputs_CP133701 .
   
   cut -f2 -d$'\t' snippy.core.tab > f2
   cut -f3 -d$'\t' snippy.core.tab > f3
   cut -f4 -d$'\t' snippy.core.tab > f4
   
   diff snippy/merged_snp.vcf.id variants/f2
   5d5
   < 138824
   7d6
   < 139197
   9d7
   < 139844
   61d58
   < 2475573
   
   # -- merging vcf-files using bcftools --
   
   results_HDRNA_01/snippy
   bcftools merge HDRNA_01_K01/HDRNA_01_K01.vcf.gz HDRNA_01_K02/HDRNA_01_K02.vcf.gz HDRNA_01_K03/HDRNA_01_K03.vcf.gz HDRNA_01_K04/HDRNA_01_K04.vcf.gz HDRNA_01_K05/HDRNA_01_K05.vcf.gz HDRNA_01_K06/HDRNA_01_K06.vcf.gz HDRNA_01_K07/HDRNA_01_K07.vcf.gz HDRNA_01_K08/HDRNA_01_K08.vcf.gz HDRNA_01_K09/HDRNA_01_K09.vcf.gz HDRNA_01_K10/HDRNA_01_K10.vcf.gz -o merged.vcf
   #bcftools index merged.vcf.gz
   cp merged.vcf merged_CP133676.vcf
   
   cd results_HDRNA_03/snippy
   bcftools merge HDRNA_03_K01/HDRNA_03_K01.vcf.gz HDRNA_03_K02/HDRNA_03_K02.vcf.gz HDRNA_03_K03/HDRNA_03_K03.vcf.gz HDRNA_03_K04/HDRNA_03_K04.vcf.gz HDRNA_03_K05/HDRNA_03_K05.vcf.gz HDRNA_03_K06/HDRNA_03_K06.vcf.gz HDRNA_03_K07/HDRNA_03_K07.vcf.gz HDRNA_03_K08/HDRNA_03_K08.vcf.gz HDRNA_03_K09/HDRNA_03_K09.vcf.gz HDRNA_03_K10/HDRNA_03_K10.vcf.gz -o merged.vcf
   cp merged.vcf merged_CP133677.vcf
   
   cd results_HDRNA_06/snippy
   bcftools merge HDRNA_06_K01/HDRNA_06_K01.vcf.gz HDRNA_06_K02/HDRNA_06_K02.vcf.gz HDRNA_06_K03/HDRNA_06_K03.vcf.gz HDRNA_06_K04/HDRNA_06_K04.vcf.gz HDRNA_06_K05/HDRNA_06_K05.vcf.gz HDRNA_06_K06/HDRNA_06_K06.vcf.gz HDRNA_06_K07/HDRNA_06_K07.vcf.gz HDRNA_06_K08/HDRNA_06_K08.vcf.gz HDRNA_06_K09/HDRNA_06_K09.vcf.gz HDRNA_06_K10/HDRNA_06_K10.vcf.gz -o merged.vcf
   #split merged.vcf to merged_CP133678.vcf and merged_CP133679.vcf
   
   cd results_HDRNA_07/snippy
   bcftools merge HDRNA_07_K01/HDRNA_07_K01.vcf.gz HDRNA_07_K02/HDRNA_07_K02.vcf.gz HDRNA_07_K03/HDRNA_07_K03.vcf.gz HDRNA_07_K04/HDRNA_07_K04.vcf.gz HDRNA_07_K05/HDRNA_07_K05.vcf.gz HDRNA_07_K06/HDRNA_07_K06.vcf.gz HDRNA_07_K07/HDRNA_07_K07.vcf.gz HDRNA_07_K08/HDRNA_07_K08.vcf.gz HDRNA_07_K09/HDRNA_07_K09.vcf.gz HDRNA_07_K10/HDRNA_07_K10.vcf.gz -o merged.vcf
   cp merged.vcf merged_CP133680.vcf
   #Note that merged_CP133681.vcf is empty.
   
   cd results_HDRNA_08/snippy
   bcftools merge HDRNA_08_K01/HDRNA_08_K01.vcf.gz HDRNA_08_K02/HDRNA_08_K02.vcf.gz HDRNA_08_K03/HDRNA_08_K03.vcf.gz HDRNA_08_K04/HDRNA_08_K04.vcf.gz HDRNA_08_K05/HDRNA_08_K05.vcf.gz HDRNA_08_K06/HDRNA_08_K06.vcf.gz HDRNA_08_K07/HDRNA_08_K07.vcf.gz HDRNA_08_K08/HDRNA_08_K08.vcf.gz HDRNA_08_K09/HDRNA_08_K09.vcf.gz HDRNA_08_K10/HDRNA_08_K10.vcf.gz -o merged.vcf
   #split merged.vcf to merged_CP133682.vcf and merged_CP133683.vcf.
   #----ERROR: IGNORING the record---->
   #CP133683        1718    .       G       A       155736  .       QR=0;RO=0;ANN=A|intron_variant|MODIFIER|RGR12_11570|GENE_RGR12_11570|transcript|TRANSCRIPT_RGR12_11570|protein_coding|1/1|c.358-506C>T||||||WARNING_TRANSCRIPT_NO_START_CODON;DP=12890;AB=0;AO=3768;QA=121701;TYPE=snp  GT:DP:RO:QR:AO:QA:GL    ./.:.:.:.:.:.:. 1/1:5361:0:0:5347:173706:-15627.9,-1609.61,0    1/1:3750:0:0:3747:121191:-10903.3,-1127.96,0    ./.:.:.:.:.:.:. 1/1:3779:0:0:3768:121701:-10949.3,-1134.28,0    ./.:.:.:.:.:.:. ./.:.:.:.:.:.:. ./.:.:.:.:.:.:. ./.:.:.:.:.:.:. ./.:.:.:.:.:.:.
   
   cd results_HDRNA_12/snippy
   bcftools merge HDRNA_12_K01/HDRNA_12_K01.vcf.gz HDRNA_12_K02/HDRNA_12_K02.vcf.gz HDRNA_12_K03/HDRNA_12_K03.vcf.gz HDRNA_12_K04/HDRNA_12_K04.vcf.gz HDRNA_12_K05/HDRNA_12_K05.vcf.gz HDRNA_12_K06/HDRNA_12_K06.vcf.gz HDRNA_12_K07/HDRNA_12_K07.vcf.gz HDRNA_12_K08/HDRNA_12_K08.vcf.gz HDRNA_12_K09/HDRNA_12_K09.vcf.gz HDRNA_12_K10/HDRNA_12_K10.vcf.gz -o merged.vcf
   #split merged.vcf to merged_CP133684.vcf and merged_CP133685.vcf.
   #Note that merged_CP133686.vcf and merged_CP133687.vcf are empty.
   
   cd results_HDRNA_16/snippy
   bcftools merge HDRNA_16_K01/HDRNA_16_K01.vcf.gz HDRNA_16_K02/HDRNA_16_K02.vcf.gz HDRNA_16_K03/HDRNA_16_K03.vcf.gz HDRNA_16_K04/HDRNA_16_K04.vcf.gz HDRNA_16_K05/HDRNA_16_K05.vcf.gz HDRNA_16_K06/HDRNA_16_K06.vcf.gz HDRNA_16_K07/HDRNA_16_K07.vcf.gz HDRNA_16_K08/HDRNA_16_K08.vcf.gz HDRNA_16_K09/HDRNA_16_K09.vcf.gz HDRNA_16_K10/HDRNA_16_K10.vcf.gz -o merged.vcf
   cp merged.vcf merged_CP133688.vcf
   #Note that merged_CP133689.vcf - merged_CP133692.vcf are empty.
   
   cd results_HDRNA_17/snippy
   bcftools merge HDRNA_17_K01/HDRNA_17_K01.vcf.gz HDRNA_17_K02/HDRNA_17_K02.vcf.gz HDRNA_17_K03/HDRNA_17_K03.vcf.gz HDRNA_17_K04/HDRNA_17_K04.vcf.gz HDRNA_17_K05/HDRNA_17_K05.vcf.gz HDRNA_17_K06/HDRNA_17_K06.vcf.gz HDRNA_17_K07/HDRNA_17_K07.vcf.gz HDRNA_17_K08/HDRNA_17_K08.vcf.gz HDRNA_17_K09/HDRNA_17_K09.vcf.gz HDRNA_17_K10/HDRNA_17_K10.vcf.gz -o merged.vcf
   cp merged.vcf merged_CP133693.vcf
   #Note that merged_CP133694.vcf - merged_CP133695.vcf are empty.
   
   cd results_HDRNA_19/snippy
   bcftools merge HDRNA_19_K01/HDRNA_19_K01.vcf.gz HDRNA_19_K02/HDRNA_19_K02.vcf.gz HDRNA_19_K03/HDRNA_19_K03.vcf.gz HDRNA_19_K04/HDRNA_19_K04.vcf.gz HDRNA_19_K05/HDRNA_19_K05.vcf.gz HDRNA_19_K06/HDRNA_19_K06.vcf.gz HDRNA_19_K07/HDRNA_19_K07.vcf.gz HDRNA_19_K08/HDRNA_19_K08.vcf.gz HDRNA_19_K09/HDRNA_19_K09.vcf.gz HDRNA_19_K10/HDRNA_19_K10.vcf.gz -o merged.vcf
   cp merged.vcf merged_CP133696.vcf
   #Note that merged_CP133697.vcf - merged_CP133699.vcf are empty.
   
   cd results_HDRNA_20/snippy
   bcftools merge HDRNA_20_K01/HDRNA_20_K01.vcf.gz HDRNA_20_K02/HDRNA_20_K02.vcf.gz HDRNA_20_K03/HDRNA_20_K03.vcf.gz HDRNA_20_K04/HDRNA_20_K04.vcf.gz HDRNA_20_K05/HDRNA_20_K05.vcf.gz HDRNA_20_K06/HDRNA_20_K06.vcf.gz HDRNA_20_K07/HDRNA_20_K07.vcf.gz HDRNA_20_K08/HDRNA_20_K08.vcf.gz HDRNA_20_K09/HDRNA_20_K09.vcf.gz -o merged.vcf
   cp merged.vcf merged_CP133700.vcf
   #Note that merged_CP133701.vcf is empty.
   #Note not enough reads exist in 'HDRNA_20 S K10 365_BB84_S87_R1_001.fastq.gz' and 'HDRNA_20 S K10 365_BB84_S87_R2_001.fastq.gz'.
   
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_01/snippy/merged_CP133676.vcf Outputs_CP133676/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_01_not_in_vcf_file_output.txt HDRNA_01_not_in_txt_file_output.txt HDRNA_01_common_records_output.txt
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_03/snippy/merged_CP133677.vcf Outputs_CP133677/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_03_not_in_vcf_file_output.txt HDRNA_03_not_in_txt_file_output.txt HDRNA_03_common_records_output.txt
   
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_06/snippy/merged_CP133678.vcf Outputs_CP133678/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_06_1_not_in_vcf_file_output.txt HDRNA_06_1_not_in_txt_file_output.txt HDRNA_06_1_common_records_output.txt
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_06/snippy/merged_CP133679.vcf Outputs_CP133679/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_06_2_not_in_vcf_file_output.txt HDRNA_06_2_not_in_txt_file_output.txt HDRNA_06_2_common_records_output.txt
   
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_07/snippy/merged_CP133680.vcf Outputs_CP133680/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_07_not_in_vcf_file_output.txt HDRNA_07_not_in_txt_file_output.txt HDRNA_07_common_records_output.txt
   
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_08/snippy/merged_CP133682.vcf Outputs_CP133682/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_08_not_in_vcf_file_output.txt HDRNA_08_not_in_txt_file_output.txt HDRNA_08_common_records_output.txt
   #ERROR: python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_08/snippy/merged_CP133683.vcf Outputs_CP133683/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_08_2_not_in_vcf_file_output.txt HDRNA_08_2_not_in_txt_file_output.txt HDRNA_08_2_common_records_output.txt
   
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_12/snippy/merged_CP133684.vcf Outputs_CP133684/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_12_1_not_in_vcf_file_output.txt HDRNA_12_1_not_in_txt_file_output.txt HDRNA_12_1_common_records_output.txt
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_12/snippy/merged_CP133685.vcf Outputs_CP133685/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_12_2_not_in_vcf_file_output.txt HDRNA_12_2_not_in_txt_file_output.txt HDRNA_12_2_common_records_output.txt
   
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_16/snippy/merged_CP133688.vcf Outputs_CP133688/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_16_not_in_vcf_file_output.txt HDRNA_16_not_in_txt_file_output.txt HDRNA_16_common_records_output.txt
   
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_17/snippy/merged_CP133693.vcf Outputs_CP133693/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_17_not_in_vcf_file_output.txt HDRNA_17_not_in_txt_file_output.txt HDRNA_17_common_records_output.txt
   
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_19/snippy/merged_CP133696.vcf Outputs_CP133696/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_19_not_in_vcf_file_output.txt HDRNA_19_not_in_txt_file_output.txt HDRNA_19_common_records_output.txt
   
   python3 ~/Scripts/merge_snippy_and_spandx_results.py results_HDRNA_20/snippy/merged_CP133700.vcf Outputs_CP133700/Phylogeny_and_annotation/All_SNPs_indels_annotated.txt HDRNA_20_not_in_vcf_file_output.txt HDRNA_20_not_in_txt_file_output.txt HDRNA_20_common_records_output.txt
   
   mv HDRNA_01_common_records_output.txt _HDRNA_01.txt
   mv HDRNA_03_common_records_output.txt  HDRNA_03.txt
   mv HDRNA_06_common_records_output.txt  HDRNA_06.txt
   mv HDRNA_07_common_records_output.txt  HDRNA_07.txt
   mv HDRNA_08_common_records_output.txt  HDRNA_08.txt
   mv HDRNA_12_common_records_output.txt  HDRNA_12.txt
   mv HDRNA_16_common_records_output.txt  HDRNA_16.txt
   mv HDRNA_17_common_records_output.txt  HDRNA_17.txt
   mv HDRNA_19_common_records_output.txt  HDRNA_19.txt
   mv HDRNA_20_common_records_output.txt  HDRNA_20.txt
   
   sed -i '1s/_trimmed_P//g' _HDRNA_01.txt
   sed -i '1s/_trimmed_P//g' HDRNA_03.txt HDRNA_06.txt HDRNA_07.txt HDRNA_08.txt HDRNA_12.txt HDRNA_16.txt HDRNA_17.txt HDRNA_19.txt HDRNA_20.txt
   
   # -- check if f3==f6 --
   cut -f3 -d$'\t' HDRNA_17.txt > f3
   cut -f6 -d$'\t' HDRNA_17.txt > f6
   diff f3 f6
   
   # -- check if f6==f7 in HDRNA_7.txt since they have the sample names --
   cut HDRNA_07.txt -d$'\t' -f6  > f6
   cut HDRNA_07.txt -d$'\t' -f7  > f7
   diff d6 f7
   --> delete the column HDRNA_07_K01-BB28 in variant_calling.xls.
   
   ~/Tools/csv2xls-0.4/csv_to_xls.py _HDRNA_01.txt HDRNA_03.txt HDRNA_06.txt HDRNA_07.txt HDRNA_08.txt HDRNA_12.txt HDRNA_16.txt HDRNA_17.txt HDRNA_19.txt HDRNA_20.txt -d$'\t' -o variant_calling.xls

5. processing commands for presence phage HH1, SPbeta-like phage, phage related island

   #makeblastdb -in HDRNA_K01.fna -dbtype nucl
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_01.blastn
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_01.blastn
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_01.blastn
   
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_03.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_03.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_03.blastn
   
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_06.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_06.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_06.blastn
   
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_07.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_07.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_07.blastn
   
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_08.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_08.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_08.blastn
   
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_12.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_12.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_12.blastn
   
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_16.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_16.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_16.blastn
   
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_17.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_17.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_17.blastn
   
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_19.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_19.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_19.blastn
   
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query MT880870.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880870_on_20.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query MT880871.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880871_on_20.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query MT880872.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > MT880872_on_20.blastn
   
   ΦSepi-HH1(MT880870): 34053 bp in ST2, ST83 (34053 bt)
   PI-Sepi-HH2(MT880871): not in ST2, but in ST290, ST297 and ST487 (36164 bt)
   ΦSPbeta-like(MT880872): in ST2 and ST22 (147057 bt)
   # In the new isolates, we have the MLST
   
   -
   ST130
   ST224
   ST23
   ST35
   ST487 -->
   ST5
   ST640
   ST69
   ST86
   ST87
   --> HDRNA_17_K01 (ST69) contains MT880871!
   It agrees with the description in the Anna's paper, ST487 has PI-Sepi-HH2!
   #shovill/HDRNA_11_K01/contigs.fa sepidermidis    487     arcC(1) aroE(1) gtr(1)  mutS(5) pyrR(2) tpiA(1) yqiL(1)
   
   # -- 01 --
   33342   34053 --> am Grenzen
   33634   34132 --> am Grenzen
   131829  147057 + 1       3810 --> am Grenzen
   30857   34053 --> NA
   33634   34132 --> NA
   131829  147057 + 1       3807 --> NA
   NA
   NA
   NA
   07: NA
   08: NA
   12: NA
   16: NA
   17: MT880871_on_17.blastn YES
   19: NA
   20: NA
   
   shovill/HDRNA_11_K01/contigs.fa sepidermidis    487     arcC(1) aroE(1) gtr(1)  mutS(5) pyrR(2) tpiA(1) yqiL(1)
   
   a Biofilm
   formation compared to S. epidermidis 1457: –, ⬍30%; ⫹, 30% to 59%; ⫹⫹, 60% to 84%; ⫹⫹⫹, ⱖ85%.
   b ST,
   sequence type determined by MLST.
   c GC, genetic cluster based on Bayesian analysis of population structure (BAPS) of MLST.
   d ND, not determined. In columns 2 to 5, ⫹ indicates presence and ⫺ indicates absence.
   
   ND, not determined or defined. '+' indicates presence and '-' indicates absence.
   
   SPbeta-like staphylococcus phage (NC_029119.1, 86% sequence identity)[47] and will here be referred to as
   ΦSPbeta-like, (GenBank accession number MT880872) the remaining two regions did not correspond to any previously described Staphylococcus phage[48]
   ΦSepi-HH1 (MT880870): 34053 bp
   phage-related island PI-Sepi-HH2 (MT880871).
   MT880870.1: Staphylococcus phage PhiSepi-HH1, complete genome
   MT880871.1: Staphylococcus phage PI-Sepi-HH2, complete genome: 36164 bp
   MT880872.1: Staphylococcus phage PhiSepi-HH3, complete genome

6. processing commands for the other genes from Gene_List.pptx

           # AND "Staphylococcus epidermidis"[porgn:__txid1282]
   
   mv HDRNA_16_K01_conservative_37834.current.gb ../gbk
   mv ./HDRNA_17_K01_conservative_37288.current.gb ../gbk
   mv ./HDRNA_03_K01_bold_bandage_26831.current.gb ../gbk
   mv ./HDRNA_06_K01_conservative_27645.current.gb ../gbk
   mv ./HDRNA_12_K01_bold_37467.current.gb ../gbk
   mv ./HDRNA_01_K01_conservative_23197.current.gb ../gbk
   mv ./HDRNA_07_K01_conservative_27169.current.gb ../gbk
   mv ./HDRNA_08_K01_conservative_32455.current.gb ../gbk
   mv ./HDRNA_19_K01_bold_37377.current.gb ../gbk
   mv ./HDRNA_20_K01_conservative_43457.current.gb ../gbk
   
   (agrC) AND "Staphylococcus"
   https://www.ncbi.nlm.nih.gov/nuccore/OQ828637.1
   
   gltA
   agrC
   
   gene            2184799..2186091
                       /gene="agrC"
                       /locus_tag="SAR2125"
       CDS             2184799..2186091
                       /gene="agrC"
                       /locus_tag="SAR2125"
                       /note="Signal dectecting component of the agr autoinducer
                       peptide-quorum sensing system. Two-component regulatory
                       system family, sensor kinase protein. Similar to
                       Staphylococcus aureus accessory gene regulator C AgrC
                       TR:Q53644 (EMBL:X52543) (423 aa) fasta scores: E():
                       1.7e-101, 75.23% id in 424 aa, and to Staphylococcus
                       epidermidis histidine kinase AgrC TR:O68159
                       (EMBL:AF012132) (429 aa) fasta scores: E(): 6.3e-76,
                       54.93% id in 426 aa"
                       /codon_start=1
                       /transl_table=11
                       /product="autoinducer sensor protein"
                       /protein_id="CAG41106.1"
                       /db_xref="EnsemblGenomes-Gn:SAR2125"
                       /db_xref="EnsemblGenomes-Tr:CAG41106"
                       /translation="MEALNDYNYVLFVIVQVSLMFFISAFISGIRYKKSDYIYIIGIV
                       LSSVYFFDKIRSISLVVITIFIIIFLYFKIRLYSVFLVMVTQIILYCANFVYIIIFSY
                       IITISHSVFIVLPIFLVVYVSISYALAYILNRILKRINGTYLSLNKKFLTVITIVIVI
                       TFSLLFAYSQIDASDASTIKQYSLLFLGIIILLSILIFIYSQFTLKEMKYKRNQEEIE
                       TYYEYTLKIEAINNEMRKFRHDYVNILTTLSEYIREDDMTGLRDYFNKNIVPMKDNLQ
                       MNALKLNGIENLKVREIKGLLTAKILRAQEMNIPISIEIPDEVTRINLNMIDLSRSIG
                       IILDNAIEASSEIDDPIIRVAFIESENSVTFIVMNKCADDIPRIHELFQESFSTKGEG
                       RGLGLSTLKEIADNADNVLLDTIIENGFFIQKVEIINN"
   MEALNDYNYVLFVIVQVSLMFFISAFISGIRYKKSDYIYIIGIVLSSVYFFDKIRSISLVVITIFIIIFLYFKIRLYSVFLVMVTQIILYCANFVYIIIFSYIITISHSVFIVLPIFLVVYVSISYALAYILNRILKRINGTYLSLNKKFLTVITIVIVITFSLLFAYSQIDASDASTIKQYSLLFLGIIILLSILIFIYSQFTLKEMKYKRNQEEIETYYEYTLKIEAINNEMRKFRHDYVNILTTLSEYIREDDMTGLRDYFNKNIVPMKDNLQMNALKLNGIENLKVREIKGLLTAKILRAQEMNIPISIEIPDEVTRINLNMIDLSRSIGIILDNAIEASSEIDDPIIRVAFIESENSVTFIVMNKCADDIPRIHELFQESFSTKGEGRGLGLSTLKEIADNADNVLLDTIIENGFFIQKVEIINN
   Staphylococcus_aureus_MRSA252
   
   samtools faidx Staphylococcus_aureus_MRSA252.fasta "gi|49240382|emb|BX571856.1|":2184799-2186091
   
   (yycG) AND "Staphylococcus"
       gene            25617..27443
                       /gene="yycG"
                       /locus_tag="SAR0019"
                       /gene_synonym="vicK"
       CDS             25617..27443
                       /gene="yycG"
                       /locus_tag="SAR0019"
                       /gene_synonym="vicK"
                       /note="Two-component regulatory system family, sensor
                       kinase protein. Previously sequenced as Staphylococcus
                       aureus two-component sensor histidine kinase YycG
                       TR:Q9XCM6 (EMBL:AF136709) (608 aa) fasta scores: E():
                       5.5e-214, 99.836% id in 608 aa. Similar to Bacillus
                       subtilis probable two-component sensor histidine kinase
                       YycG TR:Q45614 (EMBL:D78193) (611 aa) fasta scores: E():
                       2e-98, 46.217% id in 608 aa"
                       /codon_start=1
                       /transl_table=11
                       /product="sensor kinase protein"
                       /protein_id="CAG39047.1"
                       /db_xref="EnsemblGenomes-Gn:SAR0019"
                       /db_xref="EnsemblGenomes-Tr:CAG39047"
                       /db_xref="GOA:Q6GKS6"
                       /db_xref="InterPro:IPR000014"
                       /db_xref="InterPro:IPR000700"
                       /db_xref="InterPro:IPR003594"
                       /db_xref="InterPro:IPR003660"
                       /db_xref="InterPro:IPR003661"
                       /db_xref="InterPro:IPR004358"
                       /db_xref="InterPro:IPR005467"
                       /db_xref="InterPro:IPR029151"
                       /db_xref="UniProtKB/Swiss-Prot:Q6GKS6"
                       /translation="MKWLKQLQSLHTKLVIVYVLLIIIGMQIIGLYFTNNLEKELLDN
                       FKKNITQYAKQLEISIEKVYDEKGSVNAQKDIQNLLSEYANRQEIGEIRFIDKDQIII
                       ATTKQSNRSLINQKANDSSVQKALSLGQSNDHLILKDYGGGKDRVWVYNIPVKVDKKV
                       IGNIYIESKINDVYNQLNNINQIFIVGTAISLLITVILGFFIARTITKPITDMRNQTV
                       EMSRGNYTQRVKIYGNDEIGELALAFNNLSKRVQEAQANTESEKRRLDSVITHMSDGI
                       IATDRRGRIRIVNDMALKMLGMAKEDIIGYYMLSVLSLEDEFKLEEIQENNDSFLLDL
                       NEEEGLIARVNFSTIVQETGFVTGYIAVLHDVTEQQQVERERREFVANVSHELRTPLT
                       SMNSYIEALEEGAWKDEELAPQFLSVTREETERMIRLVNDLLQLSKMDNESDQINKEI
                       IDFNMFINKIINRHEMSTKDTTFIRDIPKKTIFTEFDPDKMTQVFDNVITNAMKYSRG
                       DKRVEFHVKQNPLYNRMTIRIKDNGIGIPINKVDKIFDRFYRVDKARTRKMGGTGLGL
                       AISKEIVEAHNGRIWANSVEGQGTSIFITLPCEVIEDGDWDE"
   
   samtools faidx Staphylococcus_aureus_MRSA252.fasta "gi|49240382|emb|BX571856.1|":25617-27443
   
   psmß1: https://www.ncbi.nlm.nih.gov/nuccore/380448412
   hlb: https://www.ncbi.nlm.nih.gov/nuccore/KC242859.1
   
   atlE
   
   tagB
   
       gene            694999..696102
                       /gene="tagB"
                       /locus_tag="SAR0649"
       CDS             694999..696102
                       /gene="tagB"
                       /locus_tag="SAR0649"
                       /note="Similar to Bacillus subtilis teichoic acid
                       biosynthesis protein B TagB SW:TAGB_BACSU (P27621) (381
                       aa) fasta scores: E(): 4.3e-26, 31.302% id in 361 aa, and
                       to Lactococcus lactis teichoic acid biosynthesis protein B
                       TagB TR:Q9CH14 (EMBL:AE006327) (371 aa) fasta scores: E():
                       2.8e-06, 24.834% id in 302 aa. Lack of similarity at the
                       N-terminus in comparison to other orthologues"
                       /codon_start=1
                       /transl_table=11
                       /product="teichoic acid biosynthesis protein"
                       /protein_id="CAG39666.1"
                       /db_xref="EnsemblGenomes-Gn:SAR0649"
                       /db_xref="EnsemblGenomes-Tr:CAG39666"
                       /translation="MNVLIKKFYHLVVRILSKMITPQVIDKPHIVFMMTFPEDIKPII
                       KALNNSLYQKTVLTTPKQAPYLSELSDDVNVIEMTNRTLVKQIKALKSAQMIIIDNYY
                       LLLGGYNKTSNQHIVQTWHASGALKNFGLTDHQVDVSDKAMVQQYRKVYQATDFYLVG
                       CEQMSQCFKQSLGATEEQMLYFGLPRINKYYTADRATVKAELKDKYGITNKLALYVPT
                       YREDKADNRAIDKAYFEKCLPGYTLINKLHPSIEHSDIDDVSSIDTSILMLMSDIIIS
                       DYSSLPIEASLLDIPTIFYVYDEGTYDKVRGLNQFYKAIPDSYKVYTEEDLIMTIQEK
                       EHLLSPLFKDWHKYNTDKSLHQLTEYIDKMVTK"
   
   samtools faidx Staphylococcus_aureus_MRSA252.fasta "gi|49240382|emb|BX571856.1|":694999-696102 > tagB.fasta
   
       gene            2137..2586
                       /gene="capC"
       CDS             2137..2586
                       /gene="capC"
                       /codon_start=1
                       /transl_table=11
                       /product="CapC"
                       /protein_id="BAB13485.1"
                       /translation="MFGSDLYIALILGVLLSLIFAEKTGIVPAGLVVPGYLGLVFNQP
                       VFILLVLLVSLLTYVIVKYGLSKFMILYGRRKFAAMLITGIVLKIAFDFLYPIVPFEI
                       AEFRGIGIIVPGLIANTIQKQGLTITFGSTLLLSGATFAIMFVYYLI"
   
   samtools faidx capBCA_ywtC.fasta "gi|10119860|dbj|AB039950.1|":2137-2586 > capC.fasta
   
       gene            2783..3256
                       /gene="sepA"
       CDS             2783..3256
                       /gene="sepA"
                       /function="multidrug resistance"
                       /experiment="experimental evidence, no additional details
                       recorded"
                       /note="similar to BAB43260.1 of S. aureus N315"
                       /codon_start=1
                       /transl_table=11
                       /product="SepA"
                       /protein_id="BAB83937.1"
                       /translation="MIVNYLKHKFYNLLTTMIVLFIFVLSGAIFLTFLGFGLYGLSRI
                       LIYFRLGDFTYNRSMYDNLLYYGSYIIFGYFIIFAVEHLMDYFRKMLPENAYFRGATF
                       HLISYTVATTLFYFIIHLNYVYINIDFWVIMVIIGFLYVCKLQFYPESKNLNNRK"
   
   samtools faidx ORF123_sepA_ORF5.fasta "gi|18250967|dbj|AB078343.1|":2783-3256 > sepA.fasta
   
       gene            1427047..1429569
                       /gene="mprF"
                       /locus_tag="SAR1372"
                       /gene_synonym="fmtC"
       CDS             1427047..1429569
                       /gene="mprF"
                       /locus_tag="SAR1372"
                       /gene_synonym="fmtC"
                       /note="Similar to Staphylococcus aureus putative membrane
                       protein MprF TR:AAK58115 (EMBL:AF145699) (840 aa) fasta
                       scores: E(): 0, 96.190% id in 840 aa. Similar to
                       Staphylococcus xylosus putative membrane protein MprF
                       TR:AAK58113 (EMBL:AF145698) (841 aa) fasta scores: E():
                       5.4e-208, 62.530% id in 838 aa. Mutations in the CDS have
                       reduced resistance to human defensins and evasion of
                       neutrophil killing"
                       /codon_start=1
                       /transl_table=11
                       /product="putative membrane protein"
                       /protein_id="CAG40370.1"
                       /db_xref="EnsemblGenomes-Gn:SAR1372"
                       /db_xref="EnsemblGenomes-Tr:CAG40370"
                       /db_xref="GOA:Q6GH45"
                       /db_xref="InterPro:IPR016181"
                       /db_xref="InterPro:IPR022791"
                       /db_xref="InterPro:IPR024320"
                       /db_xref="UniProtKB/Swiss-Prot:Q6GH45"
                       /translation="MNQEVKNKIFSILKITFATALFIFVVITLYRELSGINFKDTLVE
                       FSKINRMSLVLLFIGGGASLVILSMYDVILSRALKMDISLGKVLRVSYIINALNAIVG
                       FGGFIGAGVRAMVYKNYTHDKKKLVHFISLILISMLTGLSLLSLLIVFHVFDASLILN
                       KITWVRWVLYAVSLFLPLFIIYSMVRPPDKNNRYVGLYCTLVSCVEWLAAAVVLYFCG
                       VIVDVHVSFMSFIAIFIIAALSGLVSFIPGGFGAFDLVVLLGFKTLGVPEEKVLLMLL
                       LYRFAYYFVPVIIALILSSFEFGTSAKKYIEGSKYFIPAKDVTSFLMSYQKDIIAKIP
                       SLSLAILVFFTSMIFFVNNLTIVYDALYDGNHLTYYLLLAIHTSACLLLLLNVVGIYK
                       QSRRAIIYAMISIILIIVATLFTYASYILITWLVIIFALLIVAFRRARRLKRPIRMRN
                       LVAMLLFSIFILYINHIFIAGTFYALDVYTIEMHTSVLKYYFWITILIIAIIVGAIAW
                       LFDYQFSKVRISSNIEECEEIIDQYGGNYLSHLIYSGDKQFFTNEDKNAFLMYRYKAS
                       SLVVLGDPIGDENAFDELLEAFYNYAEYLGYDVIFYQVTDQHMPLYHNFGNQFFKLGE
                       EAIIDLTQFSTSGKKRRGFRATLNKFDELNISFEIIEPPFSTEFINELQHVSDLWLDN
                       RQEMHFSVGQFNETYLSKAPIGVMRNENNEVIAFCSLMPTYFNDAISVDLIRWLPELD
                       LPLMDGLYLHMLLWSKEQGYTKFNMGMATLSNVGQLHYSYLRERLAGRVFEHFNGLYR
                       FQGLRRYKSKYNPNWEPRFLVYRKDNSLWESLSKVMRVIRHK"
   
   samtools faidx Staphylococcus_aureus_MRSA252.fasta "gi|49240382|emb|BX571856.1|":1427047-1429569 > fmtC.fasta
   
       gene            1825..2523
                       /gene="sceD"
       CDS             1825..2523
                       /gene="sceD"
                       /codon_start=1
                       /transl_table=11
                       /product="SceD precursor"
                       /protein_id="AAB94657.1"
                       /translation="MKKLLVASSASAALFAVGVGANAHAAEDNNVNQDQLAQTALNNT
                       QQLNDAPVQEGAYNIAFDNSGYNFNFNSDGTNWSWSYNADSSAQQAPAQSTTQEQAPA
                       AQQAPAQSTTQEQAPAAQQAPAQEQTQQPAQQPAQQQTQQPAQQSADSGSNVQVNDHL
                       KAIAQRESGGDIHAINSSSGAAGKYQFLQTTWDSVAPAEYQGKPASEAPEAVQDAAAQ
                       KLYDTAGPSQWVTA"
       sig_peptide     1825..1899
                       /gene="sceD"
       mat_peptide     1900..2520
                       /gene="sceD"
                       /product="SceD"
   
   samtools faidx sceDAE.fasta "gi|6707000|gb|AF109218.1|":1825-2523 > sceD.fasta
   
       gene            759097..759894
                       /locus_tag="SERP_RS03845"
                       /old_locus_tag="SE0760"
                       /old_locus_tag="SERP0760"
       CDS             759097..759894
                       /locus_tag="SERP_RS03845"
                       /old_locus_tag="SE0760"
                       /old_locus_tag="SERP0760"
                       /inference="COORDINATES: similar to AA
                       sequence:RefSeq:WP_001830106.1"
                       /note="Derived by automated computational analysis using
                       gene prediction method: Protein Homology."
                       /codon_start=1
                       /transl_table=11
                       /product="VOC family protein"
                       /protein_id="WP_002446237.1"
                       /db_xref="GI:488376852"
                       /translation="MFHNKNANFVNGVTINIKDKETIKTFYENVLGFNLINESKTAVQ
                       FEVGDSNQFITFIEIQNGREPLMSEAGLFHIGILLPTLTTLADLLVHLSDFEVPVNGG
                       QQSVATCLFIEDPEGNAIKFYVDRETESWIDEKEGRIRMDIAPINVPRLLQNVSHTQW
                       QGIPDETKLGSLHIKSIRISDVKSYYLHYFGLEESAYMDDYSLFLSSNDYYNHLAVNQ
                       WLSATKRVDNEHTYGLAMIDFHYPKTTHKNLKGPDGIYFRFNRIKEV"
   
   samtools faidx Staphylococcus_epidermidis_RP62A.fasta "gi|57865352|ref|NC_002976.3|":759097-759894 > SE0760.fasta
   
       gene            3091..8793
                       /gene="esp"
                       /allele="1"
       CDS             3091..8793
                       /gene="esp"
                       /allele="1"
                       /note="enterococcal surface protein; ORF3"
                       /codon_start=1
                       /transl_table=11
                       /product="Esp"
                       /protein_id="AAQ84025.1"
                       /translation="MVSKNNKRVFLEKTKKRVLKYSIKKLSVGVASVLVGVGLVLGTT
                       ELVQAQDEISPSTPLETAISSVQVGDKVASGNTFQENPGYTKNYNFSDLQFSPQELTG
                       DTLKGNTIGFEVYGKHNIAASTKNWEIRLQLDERLAKYVEKIQVDPKKGIGSSRRTFV
                       RINDSLGRPTNIWKVNYIRASDGLFAGAETTDTQTAPNGVITFEKSLDEIFKEIGIDN
                       LKTDRLMYRIYLVSHQDDDKIVPGIDSTGYFLTDSDDFYNSLDVSENNPDQFKHGSVS
                       AKYEEPNTQTKDGSGSTGANGAIILDHKLTKNYNFSYSASAKGTPWYANYKIDERLVP
                       YVAGIQMHMVQADKVTYDVSFESGKKVADLAIERRKDHENYGVGSITDNDLTKLIDFA
                       NASPRPVVIRYVLQLTKPLDEILEDMKATAQVEENKPFGEDFIFDSWLSDTNKKLIQN
                       TYGTGYYYLQDIDGDGNPDDKEESGDTNPYIGKPELEEVYDVDTTVKGKVFIHELAGT
                       GHKAQLVDKEGTVLAEKTIAPNEKDGAPISDTVEFEFTGVDSSKLIAKDELKIQIVSP
                       GFDKPEEGSTVIKESPKAVDKQTVVVGFKPDAKESIRNNKNLPEDAEYSWKTEPDTSN
                       VTDSTKGIVTVKIGNRTFDVDVEFAVKASQAMENDATYVPITTTPETTVQSGKPTFDK
                       PDVPLAKDAFSILDVYNKDFGNASVDANTGIVTFTPAKGVGESEPITGTIPIKIVYQD
                       GSVGTTDLAVTVSKDIYENPGENIPAGYHKVTFTAGEGTSIESGTTVFAVKDGVSLPE
                       DKLPVLKAKDGYTDAKWPEEATQPITADDTEFVSSATKLDDIIENPGENIPAGYHKVT
                       FTAGEGTSIESGTTVFAVKDGVSLPEDKLPVLKAKDGYTDAKWPEEATQPITADDTEF
                       VSSATKLDDIIENPGENIPAGYHKVTFTAGEGTSIESGTTVFAVKDGVSLPEDKLPVL
                       KAKDGYTDAKWPEEATQPITADDTEFVSSATKLDDIIENPGENIPAGYHKVIFTAGEG
                       TSIESGTTVFAVKDGVSLPEDKLPVLKAKDGYTDAKWPEEATQPITADDTEFVSSATK
                       LDDIIENPGENIPAGYHKVIFTAGEGTSIESGTTVFAVKDGVSLPEDKLPVLKAKDGY
                       TDAKWPEEATQPITADDTEFVSSATKLDDKSDADKYNPEGQKVTTELNKEPDASEGIK
                       NKEDLPKDTKYTWKEKVDVSAAGNKKGTVVVTYSDGSSDEVEVDVTVTDNRSDADKYE
                       PTVEGEKVEVGGTVDLTDNVTNLPTLPEGTTVTDVTPDGTIDTNTPGNYEGVIEVTYP
                       DGTKDTVKVPVEVTDNRSDADKYTPMVEGEKVEVGGTVDLTDNVTNLPTLPEGTTVTD
                       VTPDGTIDTNTPGNYEGVIEVTYPDGTKDTVKVPVEVTDNRSDADKYTPMVEGEKVEV
                       GGTVDLTDNVTNLPTLPEGTTVTDVTPGGTIDTNTPGNYEGVIEVTYPDGTKDTVKVP
                       VEVTDNRSDADKYEPTVEGEKVEVGGTVDLTDNVTNLPTLPEGTTVTDVTPGGTIDTN
                       TPGNYEGVIEVTYPDGTKDTVKVPVEVTDNRSDADKYTPMVEGEKVEVGGTVDLTDNV
                       TNLPTLPEGTTVTDVTPDGTIDTNTPGNYEGVIEVTYPDGTKDTVKVPVEVTDNRSDA
                       DKYNPEGQKVTTDLNKEPDASEGIKNKEDLPKGTTYTWKEKVDVSTAGNKKGIVVVTY
                       PDGSKEEVEVTISVEDKKAPNKPQVDPITEGDQIVTGKTEPNAEVTVTLPNGSQYHGT
                       ADKNGNFTVKVPKLEAGTKVIVTATDESGNTSEPTNVVVSSNEKDSEKAVSKDNKTDN
                       QGSKQNTNRGKSSPQKQSSKAYPKTGEIDSNIFTISGGLILLGTLGLLGYKNRKKENE
                       "
   samtools faidx Enterococcus_faecium_isolate_E300_pathogenicity_island.fasta "gi|34980227|gb|AY322150.1|":3091-8793 > esp.fasta
   
   mv ~/Downloads/sequence\ \(1\).fasta ecpA.fasta
   
   mv ~/Downloads/sequence\ \(1\).fasta sdrG.fasta
   
   HH1-HP1
   HH1-HP2
   HH1-TreR
   
   sdrG.fasta
   
   gltA.fasta
   agrC.fasta
   yycG.fasta
   atlE.fasta
   psm-beta1.fasta
       gene            366..500
                       /gene="psm beta1"
       CDS             366..500
                       /gene="psm beta1"
                       /note="hemolytic protein; pfam05480"
                       /codon_start=1
                       /transl_table=11
                       /product="Psm beta1"
                       /protein_id="AFD54319.1"
                       /translation="MEGLFNAIKDTVTAAINNDGAKLGTSIVSIVENGVGLLGKLFGF
                       "
       gene            557..691
                       /gene="psm beta2"
       CDS             557..691
                       /gene="psm beta2"
                       /note="hemolytic protein; pfam05480"
                       /codon_start=1
                       /transl_table=11
                       /product="Psm beta2"
                       /protein_id="AFD54320.1"
                       /translation="MTGLAEAIANTVQAAQQHDSVKLGTSIVDIVANGVGLLGKLFGF
                       "
   samtools faidx psm-beta.fasta "gi|380448412|gb|JQ066320.1|":366-500 > psm-beta1.fasta
   
   hlb.fasta
   samtools faidx hlb_.fasta "gi|441494790|gb|KC242859.1|":16-1008 > hlb.fasta
   tagB.fasta
   capC.fasta
   sepA.fasta
   fmtC.fasta
   sceD.fasta
   SE0760.fasta
   esp.fasta
   samtools faidx ecpA_.fasta "gi|354620065|gb|JN051494.1|":248-835 > ecpA.fasta
   ecpA.fasta
   
   #TODO TOMORROW: add the gene names in the fasta and merge them in a file using the following command line, make once blastn, oder make gene by gene. It is maybe clearer! find the last 3 gene sequences. At first, send the results except for gene arrangements!
   #cat sdrG.fasta gltA.fasta agrC.fasta yycG.fasta atlE.fasta psm-beta1.fasta hlb.fasta tagB.fasta capC.fasta sepA.fasta fmtC.fasta sceD.fasta SE0760.fasta esp.fasta ecpA.fasta > 15genes.fasta
   
   #makeblastdb -in HDRNA_K01.fna -dbtype nucl
   
   # -- sdrG (repeated) --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query sdrG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sdrG_on_20.blastn
   
   # -- gltA --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query gltA.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > gltA_on_20.blastn
   #-->None
   
   # -- agrC --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query agrC.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > agrC_on_20.blastn
   #-->None
   
   # -- yycG (1827 nt) --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query yycG.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > yycG_on_20.blastn
   
   #-- atlE.fasta --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query atlE.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > atlE_on_20.blastn
   
   #-- psm-beta1.fasta --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query psm-beta1.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > psm-beta1_on_20.blastn
   
   #-- hlb.fasta --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query hlb.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > hlb_on_20.blastn
   
   #-- tagB.fasta --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query tagB.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > tagB_on_20.blastn
   
   #-- capC.fasta --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query capC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > capC_on_20.blastn
   
   #-- sepA.fasta --
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query sepA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sepA_on_20.blastn
   
   fmtC.fasta
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query fmtC.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > fmtC_on_20.blastn
   
   sceD.fasta
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query sceD.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > sceD_on_20.blastn
   
   SE0760.fasta
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query SE0760.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > SE0760_on_20.blastn
   
   esp.fasta
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query esp.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > esp_on_20.blastn
   
   ecpA.fasta
   blastn -db HDRNA_01_K01_conservative_23197.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_01.blastn
   blastn -db HDRNA_03_K01_bold_bandage_26831.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_03.blastn
   blastn -db HDRNA_06_K01_conservative_27645.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_06.blastn
   blastn -db HDRNA_07_K01_conservative_27169.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_07.blastn
   blastn -db HDRNA_08_K01_conservative_32455.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_08.blastn
   blastn -db HDRNA_12_K01_bold_37467.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_12.blastn
   blastn -db HDRNA_16_K01_conservative_37834.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_16.blastn
   blastn -db HDRNA_17_K01_conservative_37288.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_17.blastn
   blastn -db HDRNA_19_K01_bold_37377.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_19.blastn
   blastn -db HDRNA_20_K01_conservative_43457.current.gb_converted.fna -query ecpA.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ecpA_on_20.blastn
   
   12+15+3-1=29
   
   I only found ebpS instead of ebp.
   
           #(fumC) AND "complete cds"
           gyrB: https://www.ncbi.nlm.nih.gov/nuccore/MG995415.1 Mycobacterium tuberculosis strain UKR100 GyrB (gyrB) gene, complete cds
   
           fumC: https://www.ncbi.nlm.nih.gov/nuccore/3153897
           gltA:
           icd:

7. Bioinformatics Tools:

   Artemis: A genome browser and annotation tool that can generate similar kinds of schematic representations.
   * GenomeDiagram from Biopython: A toolkit within Biopython that allows for the creation of high-quality genomic graphics.
   #--> https://biopython-tutorial.readthedocs.io/en/latest/notebooks/17%20-%20Graphics%20including%20GenomeDiagram.html
   IGV (Integrative Genomics Viewer): While primarily a genome browser, IGV can be used to generate snapshots that display genomic regions and variations.
   General Graphic Design Software:
   
   Adobe Illustrator: A popular graphic design tool used to create precise and detailed scientific figures.
   Inkscape: A free and open-source vector graphics editor that can be used to create diagrams like the one you've shown.
   Specialized Genomic Visualization Tools:
   
   SnapGene or Geneious: While mainly used for plasmid mapping and editing, these tools also offer features to create genomic maps and diagrams.
   MEGA (Molecular Evolutionary Genetics Analysis): Primarily used for evolutionary analyses, MEGA can also produce graphical representations of genes and genomes.
   Online Tools:
   
   PlasMapper: Used primarily for plasmid maps but can be adapted for smaller genomic regions.
   ApE (A plasmid Editor): Another tool for visualizing plasmid maps that might be used to create simplified genomic diagrams.
   Without specific information, it's hard to pinpoint exactly which one was used for your image, but it's likely that the original creator used one of the bioinformatics tools or a combination of general graphic design software to illustrate the genomic rearrangements. If you're looking to create similar images, you might want to explore some of these options to see which one best suits your needs.