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Run nextflow bacass
conda deactivate # Downlod k2_standard_08_GB_20251015.tar.gz from https://benlangmead.github.io/aws-indexes/k2#kraken2--bracken # Download 20190108_kmerfinder_stable_dirs.tar.gz from https://zenodo.org/records/13447056; 'tar xzf 20190108_kmerfinder_stable_dirs.tar.gz' #The database does not work! # Download the kmerfinder database: https://www.genomicepidemiology.org/services/ --> https://cge.food.dtu.dk/services/KmerFinder/ --> https://cge.food.dtu.dk/services/KmerFinder/etc/kmerfinder_db.tar.gz #The database works! # DEBUG: --kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder/bacteria/ not working! nextflow run nf-core/bacass -r 2.6.0 -profile docker --help # -- Short assembly -- #Maybe BUG is from '--skip_kmerfinder for -r 2.6.0, using db in 2.5.0' nextflow run nf-core/bacass -r 2.5.0 -profile docker \ --input samplesheet.tsv \ --outdir bacass_out \ --assembly_type short \ --kraken2db /mnt/nvme1n1p1/REFs/k2_standard_08_GB_20251015.tar.gz \ --kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder/bacteria/ \ -resume \ -work-dir bacass_out/work -
Verify if the genome is pure
# 1. Go up one level to the main 'bacass_out' directory cd .. # 2. Create directories for CheckM inputs and outputs mkdir -p checkm_input checkm_output # 3. Copy all .fna files into the 'checkm_input' folder # (CheckM cannot search subdirectories, so they must be in one folder) find ./Prokka -name "*.fna" -exec cp {} checkm_input/ \; # 4. Run CheckM on all 4 assemblies (checkm_env2) checkm lineage_wf -x fna checkm_input checkm_output -
mlst
(bacto) mlst *.fna > mlst #Pair1 2780_.fna sepidermidis 2 arcC(7) aroE(1) gtr(2) mutS(2) pyrR(4) tpiA(1) yqiL(1) 12835_.fna sepidermidis 2 arcC(7) aroE(1) gtr(2) mutS(2) pyrR(4) tpiA(1) yqiL(1) #Pair2 4667_.fna sepidermidis 2 arcC(7) aroE(1) gtr(2) mutS(2) pyrR(4) tpiA(1) yqiL(1) 9940_.fna sepidermidis 2 arcC(7) aroE(1) gtr(2) mutS(2) pyrR(4) tpiA(1) yqiL(1) #Pair3 3139_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) 19296_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) #Pair4 9849_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) 11635_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) #Pair5 3006_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) 10393_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) #Pair6 1736_.fna sepidermidis 59 arcC(2) aroE(1) gtr(1) mutS(1) pyrR(2) tpiA(1) yqiL(1) 8583_.fna sepidermidis 59 arcC(2) aroE(1) gtr(1) mutS(1) pyrR(2) tpiA(1) yqiL(1) #Compared to other isolates from the lab: patient HD04 [ST5], HD21 [ST2], HD26 [ST5], HD27 [ST5], HD29 [ST5], HD59 [ST5], HD33 [ST87] -
Species Identification: 快速筛查用 Mash → 精确分类用 GTDB-Tk → 种级验证用 FastANI，三者结合可最大限度提高物种鉴定的准确性和可解释性。
# 1. 创建环境（推荐 mamba） mamba create -n gtdbtk -c conda-forge -c bioconda gtdbtk mamba activate gtdbtk # 2. 下载数据库（仅需首次，约 60GB） gtdbtk download --data_dir ./gtdb_data --release 220 wget https://data.gtdb.aau.ecogenomic.org/releases/release232/232.0/auxillary_files/gtdbtk_package/full_package/gtdbtk_r232_data.tar.g mamba env config vars set GTDBTK_DATA_PATH="/mnt/nvme4n1p1/gtdb_data/release232" # 先退出当前环境，再重新激活 mamba deactivate mamba activate gtdbtk # 验证环境变量是否加载成功 echo $GTDBTK_DATA_PATH # 应输出：/mnt/nvme4n1p1/gtdb_data/release232 # 3. 运行分类（你提供的命令 + 实用参数） gtdbtk classify_wf \ --genome_dir ./checkm_input \ --out_dir gtdb_out \ --cpus 64 \ --extension .fna \ --prefix mygenome # 4. 查看结果 cat gtdb_out/classify/mygenome.bac120.summary.tsv # 细菌结果 #Pair1 2780_.fna sepidermidis 2 arcC(7) aroE(1) gtr(2) mutS(2) pyrR(4) tpiA(1) yqiL(1) 12835_.fna sepidermidis 2 arcC(7) aroE(1) gtr(2) mutS(2) pyrR(4) tpiA(1) yqiL(1) #Pair2 4667_.fna sepidermidis 2 arcC(7) aroE(1) gtr(2) mutS(2) pyrR(4) tpiA(1) yqiL(1) 9940_.fna sepidermidis 2 arcC(7) aroE(1) gtr(2) mutS(2) pyrR(4) tpiA(1) yqiL(1) #Pair3 3139_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) 19296_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) #Pair4 9849_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) 11635_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) #Pair5 3006_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) 10393_.fna sepidermidis 5 arcC(1) aroE(1) gtr(1) mutS(2) pyrR(2) tpiA(1) yqiL(1) #Pair6 1736_.fna sepidermidis 59 arcC(2) aroE(1) gtr(1) mutS(1) pyrR(2) tpiA(1) yqiL(1) 8583_.fna sepidermidis 59 arcC(2) aroE(1) gtr(1) mutS(1) pyrR(2) tpiA(1) yqiL(1) ---------------------------------------------------------------------------------------------------------------------------------------------------------------- Bin Id Marker lineage # genomes # markers # marker sets 0 1 2 3 4 5+ Completeness Contamination Strain heterogeneity ---------------------------------------------------------------------------------------------------------------------------------------------------------------- 2780_ g__Staphylococcus (UID294) 60 773 178 1 772 0 0 0 0 99.81 0.00 0.00 12835_ g__Staphylococcus (UID294) 60 773 178 1 772 0 0 0 0 99.81 0.00 0.00 4667_ g__Staphylococcus (UID294) 60 773 178 1 772 0 0 0 0 99.81 0.00 0.00 9940_ g__Staphylococcus (UID294) 60 773 178 1 772 0 0 0 0 99.81 0.00 0.00 3139_ g__Staphylococcus (UID294) 60 773 178 1 772 0 0 0 0 99.81 0.00 0.00 19296_ g__Staphylococcus (UID294) 60 773 178 1 772 0 0 0 0 99.81 0.00 0.00 9849_ g__Staphylococcus (UID294) 60 773 178 1 772 0 0 0 0 99.81 0.00 0.00 11635_ g__Staphylococcus (UID294) 60 773 178 2 771 0 0 0 0 99.73 0.00 0.00 3006_ g__Staphylococcus (UID294) 60 773 178 1 772 0 0 0 0 99.81 0.00 0.00 10393_ g__Staphylococcus (UID294) 60 773 178 1 772 0 0 0 0 99.81 0.00 0.00 1736_ g__Staphylococcus (UID294) 60 773 178 1 771 1 0 0 0 99.81 0.28 0.00 8583_ g__Staphylococcus (UID294) 60 773 178 1 771 1 0 0 0 99.81 0.28 0.00 ---------------------------------------------------------------------------------------------------------------------------------------------------------------- -
Antimicrobial resistance gene profiling and Resistome and Virulence Profiling with Abricate and RGI (Reisistance Gene Identifier)
conda activate /home/jhuang/miniconda3/envs/bengal3_ac3 abricate --list conda deactivate # 1. Set both required Java variables to bypass the buggy conda activation script export JAVA_HOME=/home/jhuang/miniconda3/envs/bengal3_ac3 export JAVA_LD_LIBRARY_PATH=$JAVA_HOME/lib/server # 2. Run your script for sample in 2780 12835 4667 9940 3139 19296 9849 11635 3006 10393 1736 8583; do ENV_NAME=/home/jhuang/miniconda3/envs/bengal3_ac3 \ ASM=bacass_out/checkm_input/${sample}_.fna \ SAMPLE=${sample} \ OUTDIR=resistome_virulence_${sample} \ MINID=80 MINCOV=60 \ THREADS=32 \ ~/Scripts/run_abricate_resistome_virulome_one_per_gene.sh done conda activate /home/jhuang/miniconda3/envs/bengal3_ac3 #NEED_TO_ADAPT: OUTDIR = Path("resistome_virulence_An7") #NEED_TO_ADAPT: SAMPLE = "An7" #DEPRECATED_DUE_TO_NEED_MANULL_SETTING: python ~/Scripts/merge_amr_sources_by_gene.py for sample in 2780 12835 4667 9940 3139 19296 9849 11635 3006 10393 1736 8583; do python ~/Scripts/export_resistome_virulence_to_excel_py36.py \ --workdir resistome_virulence_${sample} \ --sample ${sample} \ --out Resistome_Virulence_${sample}.xlsx done Wrote Excel: Resistome_Virulence_2780.xlsx AMR genes (one row per gene): 29 VFDB hits: 0 DB hit counts saved. Wrote Excel: Resistome_Virulence_12835.xlsx AMR genes (one row per gene): 30 VFDB hits: 0 DB hit counts saved. Wrote Excel: Resistome_Virulence_4667.xlsx AMR genes (one row per gene): 33 VFDB hits: 0 DB hit counts saved. Wrote Excel: Resistome_Virulence_9940.xlsx AMR genes (one row per gene): 30 VFDB hits: 0 DB hit counts saved. Wrote Excel: Resistome_Virulence_3139.xlsx AMR genes (one row per gene): 27 VFDB hits: 1 DB hit counts saved. Wrote Excel: Resistome_Virulence_19296.xlsx AMR genes (one row per gene): 27 VFDB hits: 1 DB hit counts saved. Wrote Excel: Resistome_Virulence_9849.xlsx AMR genes (one row per gene): 20 VFDB hits: 1 DB hit counts saved. Wrote Excel: Resistome_Virulence_11635.xlsx AMR genes (one row per gene): 20 VFDB hits: 1 DB hit counts saved. Wrote Excel: Resistome_Virulence_3006.xlsx AMR genes (one row per gene): 21 VFDB hits: 1 DB hit counts saved. Wrote Excel: Resistome_Virulence_10393.xlsx AMR genes (one row per gene): 24 VFDB hits: 1 DB hit counts saved. Wrote Excel: Resistome_Virulence_1736.xlsx AMR genes (one row per gene): 22 VFDB hits: 0 DB hit counts saved. Wrote Excel: Resistome_Virulence_8583.xlsx AMR genes (one row per gene): 22 VFDB hits: 0 DB hit counts saved. # TODO: Delete the column 'COVERAGE_MAP' in all 'Raw_*' sheets -
Rename the files
mv ./raw_data/Pair1/2780_S71_1_sequence.fastq.gz ./raw_data/Pair1/2780_S71_R1.fastq.gz mv ./raw_data/Pair1/2780_S71_2_sequence.fastq.gz ./raw_data/Pair1/2780_S71_R2.fastq.gz mv ./raw_data/Pair1/12835_S72_1_sequence.fastq.gz ./raw_data/Pair1/12835_S72_R1.fastq.gz mv ./raw_data/Pair1/12835_S72_2_sequence.fastq.gz ./raw_data/Pair1/12835_S72_R2.fastq.gz mv ./raw_data/Pair2/4667_S75_1_sequence.fastq.gz ./raw_data/Pair2/4667_S75_R1.fastq.gz mv ./raw_data/Pair2/4667_S75_2_sequence.fastq.gz ./raw_data/Pair2/4667_S75_R2.fastq.gz mv ./raw_data/Pair2/9940_S78_1_sequence.fastq.gz ./raw_data/Pair2/9940_S78_R1.fastq.gz mv ./raw_data/Pair2/9940_S78_2_sequence.fastq.gz ./raw_data/Pair2/9940_S78_R2.fastq.gz mv ./raw_data/Pair3/3139_S74_1_sequence.fastq.gz ./raw_data/Pair3/3139_S74_R1.fastq.gz mv ./raw_data/Pair3/3139_S74_2_sequence.fastq.gz ./raw_data/Pair3/3139_S74_R2.fastq.gz mv ./raw_data/Pair3/19296_S81_1_sequence.fastq.gz ./raw_data/Pair3/19296_S81_R1.fastq.gz mv ./raw_data/Pair3/19296_S81_2_sequence.fastq.gz ./raw_data/Pair3/19296_S81_R2.fastq.gz mv ./raw_data/Pair4/11635_S80_1_sequence.fastq.gz ./raw_data/Pair4/11635_S80_R1.fastq.gz mv ./raw_data/Pair4/11635_S80_2_sequence.fastq.gz ./raw_data/Pair4/11635_S80_R2.fastq.gz mv ./raw_data/Pair4/9849_S77_1_sequence.fastq.gz ./raw_data/Pair4/9849_S77_R1.fastq.gz mv ./raw_data/Pair4/9849_S77_2_sequence.fastq.gz ./raw_data/Pair4/9849_S77_R2.fastq.gz mv ./raw_data/Pair5/3006_S73_1_sequence.fastq.gz ./raw_data/Pair5/3006_S73_R1.fastq.gz mv ./raw_data/Pair5/3006_S73_2_sequence.fastq.gz ./raw_data/Pair5/3006_S73_R2.fastq.gz mv ./raw_data/Pair5/10393_S79_1_sequence.fastq.gz ./raw_data/Pair5/10393_S79_R1.fastq.gz mv ./raw_data/Pair5/10393_S79_2_sequence.fastq.gz ./raw_data/Pair5/10393_S79_R2.fastq.gz mv ./raw_data/Pair6/1736_S70_1_sequence.fastq.gz ./raw_data/Pair6/1736_S70_R1.fastq.gz mv ./raw_data/Pair6/1736_S70_2_sequence.fastq.gz ./raw_data/Pair6/1736_S70_R2.fastq.gz mv ./raw_data/Pair6/8583_S76_1_sequence.fastq.gz ./raw_data/Pair6/8583_S76_R1.fastq.gz mv ./raw_data/Pair6/8583_S76_2_sequence.fastq.gz ./raw_data/Pair6/8583_S76_R2.fastq.gz mv ./Pair1/2780_S71_R1.fastq.gz ./Pair1_2780_S71_R1.fastq.gz mv ./Pair1/2780_S71_R2.fastq.gz ./Pair1_2780_S71_R2.fastq.gz mv ./Pair1/12835_S72_R1.fastq.gz ./Pair1_12835_S72_R1.fastq.gz mv ./Pair1/12835_S72_R2.fastq.gz ./Pair1_12835_S72_R2.fastq.gz mv ./Pair2/4667_S75_R1.fastq.gz ./Pair2_4667_S75_R1.fastq.gz mv ./Pair2/4667_S75_R2.fastq.gz ./Pair2_4667_S75_R2.fastq.gz mv ./Pair2/9940_S78_R1.fastq.gz ./Pair2_9940_S78_R1.fastq.gz mv ./Pair2/9940_S78_R2.fastq.gz ./Pair2_9940_S78_R2.fastq.gz mv ./Pair3/3139_S74_R1.fastq.gz ./Pair3_3139_S74_R1.fastq.gz mv ./Pair3/3139_S74_R2.fastq.gz ./Pair3_3139_S74_R2.fastq.gz mv ./Pair3/19296_S81_R1.fastq.gz ./Pair3_19296_S81_R1.fastq.gz mv ./Pair3/19296_S81_R2.fastq.gz ./Pair3_19296_S81_R2.fastq.gz mv ./Pair4/9849_S77_R1.fastq.gz ./Pair4_9849_S77_R1.fastq.gz mv ./Pair4/9849_S77_R2.fastq.gz ./Pair4_9849_S77_R2.fastq.gz mv ./Pair4/11635_S80_R1.fastq.gz ./Pair4_11635_S80_R1.fastq.gz mv ./Pair4/11635_S80_R2.fastq.gz ./Pair4_11635_S80_R2.fastq.gz mv ./Pair5/3006_S73_R1.fastq.gz ./Pair5_3006_S73_R1.fastq.gz mv ./Pair5/3006_S73_R2.fastq.gz ./Pair5_3006_S73_R2.fastq.gz mv ./Pair5/10393_S79_R1.fastq.gz ./Pair5_10393_S79_R1.fastq.gz mv ./Pair5/10393_S79_R2.fastq.gz ./Pair5_10393_S79_R2.fastq.gz mv ./Pair6/1736_S70_R1.fastq.gz ./Pair6_1736_S70_R1.fastq.gz mv ./Pair6/1736_S70_R2.fastq.gz ./Pair6_1736_S70_R2.fastq.gz mv ./Pair6/8583_S76_R1.fastq.gz ./Pair6_8583_S76_R1.fastq.gz mv ./Pair6/8583_S76_R2.fastq.gz ./Pair6_8583_S76_R2.fastq.gz
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SNP-calling on AP019721 using snippy, but too far is the reference –> throw the results away!
git clone https://github.com/huang/bacto mv bacto/* ./ rm -rf bacto #Prepare raw_data and bacto-0.1.json #Set "reference": "db/CP133***.gb" in bacto-0.1.json conda activate bengal3_ac3 /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds
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Annotate a sequence as reference
conda activate /home/jhuang/miniconda3/envs/bakta bakta --db /mnt/nvme0n1p1/bakta_db shovill/Pair1_2780_S71/contigs.fa --prefix 2780 bakta --db /mnt/nvme0n1p1/bakta_db shovill/Pair2_4667_S75/contigs.fa --prefix 4667 bakta --db /mnt/nvme0n1p1/bakta_db shovill/Pair3_3139_S74/contigs.fa --prefix 3139 bakta --db /mnt/nvme0n1p1/bakta_db shovill/Pair4_9849_S77/contigs.fa --prefix 9849 bakta --db /mnt/nvme0n1p1/bakta_db shovill/Pair5_3006_S73/contigs.fa --prefix 3006 bakta --db /mnt/nvme0n1p1/bakta_db shovill/Pair6_1736_S70/contigs.fa --prefix 1736 #END -
Since your organism is non-model, standard R databases (org.Hs.eg.db, etc.) won’t work. You’ll need to manually retrieve KEGG and GO annotations.
* Preparing file 1 eggnog_out.emapper.annotations.txt for the R-code below: (KEGG Terms): EggNog based on orthology and phylogenies EggNOG-mapper assigns both KEGG Orthology (KO) IDs and GO terms. Install EggNOG-mapper: mamba create -n eggnog_env python=3.8 eggnog-mapper -c conda-forge -c bioconda #eggnog-mapper_2.1.12 mamba activate eggnog_env Run annotation: #diamond makedb --in eggnog6.prots.faa -d eggnog_proteins.dmnd mkdir /home/jhuang/mambaforge/envs/eggnog_env/lib/python3.8/site-packages/data/ download_eggnog_data.py --dbname eggnog.db -y --data_dir /home/jhuang/mambaforge/envs/eggnog_env/lib/python3.8/site-packages/data/ #NOT_WORKING: emapper.py -i CP059040_gene.fasta -o eggnog_dmnd_out --cpu 60 -m diamond[hmmer,mmseqs] --dmnd_db /home/jhuang/REFs/eggnog_data/data/eggnog_proteins.dmnd # (DO_NOT_NEED_HERE) Download CU459141_protein_.fasta from NCBI # python ~/Scripts/update_fasta_header.py CU459141_protein_.fasta CU459141_protein.fasta emapper.py -i 2780.faa -o 2780_eggnog_out --cpu 80 #--resume emapper.py -i 4667.faa -o 4667_eggnog_out --cpu 80 emapper.py -i 3139.faa -o 3139_eggnog_out --cpu 80 emapper.py -i 9849.faa -o 9849_eggnog_out --cpu 80 emapper.py -i 3006.faa -o 3006_eggnog_out --cpu 80 emapper.py -i 1736.faa -o 1736_eggnog_out --cpu 80 #----> result annotations.tsv: Contains KEGG, GO, and other functional annotations. #----> 470.IX87_14445: * 470 likely refers to the organism or strain (e.g., Acinetobacter baumannii ATCC 19606 or another related strain). * IX87_14445 would refer to a specific gene or protein within that genome. Extract KEGG KO IDs from annotations.emapper.annotations. -
Call variant calling on self using snippy
mkdir snippy_Pair1 mv 2780* snippy_Pair1 cd snippy_Pair1 git clone https://github.com/huang/bacto mv bacto/* ./ rm -rf bacto #Prepare raw_data and bacto-0.1.json mkdir raw_data mv ../raw_data/Pair1_2780_S71_R1.fastq.gz raw_data mv ../raw_data/Pair1_2780_S71_R2.fastq.gz raw_data mv ../raw_data/Pair1_12835_S72_R1.fastq.gz raw_data mv ../raw_data/Pair1_12835_S72_R2.fastq.gz raw_data #Copy the reference 2780.gbff to db, and set "reference": "db/2780.gbff" in bacto-0.1.json conda activate /home/jhuang/miniconda3/envs/bengal3_ac3 /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds -- mkdir snippy_Pair2 mv 4667* snippy_Pair2 cd snippy_Pair2 git clone https://github.com/huang/bacto mv bacto/* ./ rm -rf bacto mkdir raw_data mv ../raw_data/Pair2_4667_S75_R1.fastq.gz raw_data mv ../raw_data/Pair2_4667_S75_R2.fastq.gz raw_data mv ../raw_data/Pair2_9940_S78_R1.fastq.gz raw_data mv ../raw_data/Pair2_9940_S78_R2.fastq.gz raw_data #Copy the reference 4667.gbff to db, and set "reference": "db/2780.gbff" in bacto-0.1.json /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds -- mkdir snippy_Pair3 mv 3139* snippy_Pair3 cd snippy_Pair3 git clone https://github.com/huang/bacto mv bacto/* ./ rm -rf bacto mkdir raw_data mv ../raw_data/Pair3_3139_S74_R1.fastq.gz raw_data mv ../raw_data/Pair3_3139_S74_R2.fastq.gz raw_data mv ../raw_data/Pair3_19296_S81_R1.fastq.gz raw_data mv ../raw_data/Pair3_19296_S81_R2.fastq.gz raw_data #Copy the reference 3139.gbff to db, and set "reference": "db/3139.gbff" in bacto-0.1.json /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds -- mkdir snippy_Pair4 mv 9849* snippy_Pair4 cd snippy_Pair4 git clone https://github.com/huang/bacto mv bacto/* ./ rm -rf bacto mkdir raw_data mv ../raw_data/Pair4_9849_S77_R1.fastq.gz ./raw_data mv ../raw_data/Pair4_9849_S77_R2.fastq.gz ./raw_data mv ../raw_data/Pair4_11635_S80_R1.fastq.gz ./raw_data mv ../raw_data/Pair4_11635_S80_R2.fastq.gz ./raw_data #Copy the reference 9849.gbff to db, and set "reference": "db/9849.gbff" in bacto-0.1.json /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds -- mkdir snippy_Pair5 mv 3006* snippy_Pair5 cd snippy_Pair5 git clone https://github.com/huang/bacto mv bacto/* ./ rm -rf bacto mkdir raw_data mv ../raw_data/Pair5_3006_S73_R1.fastq.gz ./raw_data mv ../raw_data/Pair5_3006_S73_R2.fastq.gz ./raw_data mv ../raw_data/Pair5_10393_S79_R1.fastq.gz ./raw_data mv ../raw_data/Pair5_10393_S79_R2.fastq.gz ./raw_data #Copy the reference 3006.gbff to db, and set "reference": "db/3006.gbff" in bacto-0.1.json /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds -- mkdir snippy_Pair6 mv 1736* snippy_Pair6 cd snippy_Pair6 git clone https://github.com/huang/bacto mv bacto/* ./ rm -rf bacto mkdir raw_data mv ../raw_data/Pair6_1736_S70_R1.fastq.gz ./raw_data mv ../raw_data/Pair6_1736_S70_R2.fastq.gz ./raw_data mv ../raw_data/Pair6_8583_S76_R1.fastq.gz ./raw_data mv ../raw_data/Pair6_8583_S76_R2.fastq.gz ./raw_data #Copy the reference 1736.gbff to db, and set "reference": "db/1736.gbff" in bacto-0.1.json /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds
TODOs: # Merge the annotated information 2780_eggnog_out.emapper.annotations into the SNP-Indels-results after making concensus SNP-Indels-results merging results of SPANDx!
!!!! OPTIONALLY (for the project, 我觉得用snippy就够了: simply only taking the results of snippy, do not do the concensus of the results using snippy and SPANDx !!!!)
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Summarize all SNPs and Indels from the snippy result directory.
cp ~/Scripts/summarize_snippy_res_ordered.py . # IMPORTANT_ADAPT the array in script should be adapted isolates = ["W1", "W2", "W3", "W4", "Y1", "Y2", "Y3", "Y4"] mamba activate plot-numpy1 python3 ./summarize_snippy_res_ordered.py snippy #--> Summary CSV file created successfully at: snippy/summary_snps_indels.csv cd snippy #REMOVE_the_line? I don't find the sence of the line: grep -v "None,,,,,,None,None" summary_snps_indels.csv > summary_snps_indels_.csv -
Convert the results to Excel-format
#REPLCE_HEAD: CHROM,POS,REF,TYPE,Pair1_2780_S71,Pair1_12835_S72,Functional_Class,Codon_change,Protein_and_nucleotide_change,Gene_ID,Gene_name #Replace ' ' --> ','; Replace ",,,,,," --> ','; Remove "nan" #Check the first sample is the same to the reference, otherwise delete it. #Sort according contig number and position ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair1/snippy/summary_snps_indels.csv -d',' -o SNPs_Indels_pair1_on_2780.xls ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair2/snippy/summary_snps_indels.csv -d',' -o SNPs_Indels_pair2_on_4667.xls #EMPTY ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair3/snippy/summary_snps_indels.csv -d',' -o SNPs_Indels_pair3_on_3139.xls #EMPTY ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair4/snippy/summary_snps_indels.csv -d',' -o SNPs_Indels_pair4_on_9849.xls ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair5/snippy/summary_snps_indels.csv -d',' -o SNPs_Indels_pair5_on_3006.xls #EMPTY ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair6/snippy/summary_snps_indels.csv -d',' -o SNPs_Indels_pair6_on_1736.xls ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair1/2780_eggnog_out.emapper.annotations -d$'\t' -o 2780_ORF_annotations.xls ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair2/4667_eggnog_out.emapper.annotations -d$'\t' -o 4667_ORF_annotations.xls ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair3/3139_eggnog_out.emapper.annotations -d$'\t' -o 3139_ORF_annotations.xls ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair4/9849_eggnog_out.emapper.annotations -d$'\t' -o 9849_ORF_annotations.xls ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair5/3006_eggnog_out.emapper.annotations -d$'\t' -o 3006_ORF_annotations.xls ~/Tools/csv2xls-0.4/csv_to_xls.py ./snippy_Pair6/1736_eggnog_out.emapper.annotations -d$'\t' -o 1736_ORF_annotations.xls ~/Tools/csv2xls-0.4/csv_to_xls.py mlst.txt -d$'\t' -o mlst.xls -
Report
I have completed the variant calling and functional annotation for the 6 pairs of Staphylococcus epidermidis DNA-seq data. Key Findings: Out of the 6 pairs, 3 pairs reported zero variants (Pairs 3, 4, and 6), while the other 3 pairs (Pairs 1, 2, and 5) yielded SNPs and Indels. This result aligns with the resistome and virulence profiling. For the pairs with no genetic variants (Pairs 3, 4, and 6), the isolates have identical AMR and VFDB gene profiles (e.g., both isolates in Pair 3 have 27 AMR genes and 1 VFDB hit; both in Pair 4 have 20 AMR genes). In contrast, the pairs that showed SNPs/Indels (Pairs 1, 2, and 5) also exhibit differences in their AMR gene counts (e.g., Pair 2 has 33 vs. 30 AMR genes; Pair 5 has 21 vs. 24 AMR genes). Attached Files: Please find the following results attached: MLST calling: mlst.xls. Resistome_Virulence results: 12 Excel files SNP/Indel Results (for the pairs with variants): SNPs_Indels_pair1_on_2780.xls SNPs_Indels_pair2_on_4667.xls SNPs_Indels_pair5_on_3006.xls ORF Annotations (EggNOG-mapper results with KEGG/GO terms for all 6 reference genomes in the 6 paired comparisons): 2780_ORF_annotations.xls, 4667_ORF_annotations.xls, 3139_ORF_annotations.xls, 9849_ORF_annotations.xls, 3006_ORF_annotations.xls, 1736_ORF_annotations.xls Brief Methodology Summary: Assembly & QC: We used nf-core/bacass for short-read assembly. CheckM confirmed high genome purity (>99.7% completeness, ~0% contamination), and GTDB-Tk confirmed the species as Staphylococcus epidermidis. Typing & AMR: MLST was performed (identifying ST2, ST5, and ST59 lineages). Resistome and virulence profiling were done using Abricate and RGI. Variant Calling: We used snippy to map each pair against its own assembled reference genome to ensure high-confidence SNP/Indel calling. Functional Annotation: Bakta was used for structural annotation, and EggNOG-mapper was utilized to assign KEGG and GO terms to the ORFs for all 6 reference genomes in the 6 paired comparisons.
