Presence-Absence Table and Graphics for Selected Genes in Data_Patricia_Sepi_7samples

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

ln -s ../20250203_FS10003086_95_BTR67811-0621/Alignment_Imported_1/20250204_154511/Fastq/HDM2-SF1_S1_L001_R1_001.fastq.gz HDM2-SF1_R1.fastq.gz
ln -s ../20250203_FS10003086_95_BTR67811-0621/Alignment_Imported_1/20250204_154511/Fastq/HDM2-SF1_S1_L001_R2_001.fastq.gz HDM2-SF1_R2.fastq.gz
ln -s ../20250203_FS10003086_95_BTR67811-0621/Alignment_Imported_1/20250204_154511/Fastq/HDM20-SF3_S2_L001_R1_001.fastq.gz HDM20-SF3_R1.fastq.gz
ln -s ../20250203_FS10003086_95_BTR67811-0621/Alignment_Imported_1/20250204_154511/Fastq/HDM20-SF3_S2_L001_R2_001.fastq.gz HDM20-SF3_R2.fastq.gz

# only activate the steps assembly and mlst in bacto-0.1.json.
mamba activate /home/jhuang/miniconda3/envs/bengal3_ac3
cd ~/DATA/Data_Patricia_Sepi_7samples
(bengal3_ac3) jhuang@WS-2290C:~/DATA/Data_Patricia_Sepi_7samples$ /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds

MLST-results

cd mlst
cat *.mlst.txt | sort > mlst_sorted

shovill/HDM1/contigs.fa sepidermidis    5   1   1   1   2   2   1   1
shovill/HDM7/contigs.fa sepidermidis    59  2   1   1   1   2   1   1
shovill/HDM10/contigs.fa    sepidermidis    59  2   1   1   1   2   1   1
shovill/HDM11-SF1/contigs.fa    sepidermidis    224 19  16  19  6   3   19  10
shovill/HDM15-SF2/contigs.fa    sepidermidis    87  7   1   1   2   2   1   1
shovill/HDM2-SF1/contigs.fa sepidermidis    -   1   2   ~2  2   1   1   10
shovill/HDM20-SF3/contigs.fa    sepidermidis    -   7   1   ~2  2   4   1   1

(optional) 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

SCCmec typing using Online-Service https://cge.food.dtu.dk/services/SCCmecFinder/ 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

#6. -- HDM2-SF1_contigs.fa --
The input organism was prediced as a MRSA isolate
The mecA gene was detected

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) 87.68%

#7. -- HDM20-SF3_contigs.fa --
The input organism was prediced as a MSSA isolate
The mecA/mecC gene was not detected
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

#-
The input organism was prediced as a MSSA isolate
The mecA/mecC gene was not detected

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

#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

Agr typing

#under env (bakta)
mamba activate /home/jhuang/miniconda3/envs/bakta
for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2 HDM2-SF1 HDM20-SF3; do
    bakta --db /mnt/nvme0n1p1/bakta_db shovill/${sample}/contigs.fa --prefix ${sample}
done
mv HDM1* bakta_results
mv HDM2* bakta_results
mv HDM7* bakta_results

cd bakta_results
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"

HDM2-SF1.gbff:                     /gene="agrD"
HDM2-SF1.gbff:                     /gene="agrD"
HDM20-SF3.gbff:                     /gene="agrD"
HDM20-SF3.gbff:                     /gene="agrD"

MNLLGGLLLKIFSNFMAVIGNASKYNPCSNYLDEPQVPEELTKLDE
MENIFNLFIKFFTTILEFIGTVAGDSVCASYFDEPEVPEELTKLYE
MENIFNLFIKFFTTILEFIGTVAGDSVCASYFDEPEVPEELTKLYE
MNLLGGLLLKIFSNFMAVIGNASKYNPCSNYLDEPQVPEELTKLDE
MNLLGGLLLKIFSNFMAVIGNASKYNPCSNYLDEPQVPEELTKLDE

MENIFNLFIKFFTTILEFIGTVAGDSVCASYFDEPEVPEELTKLYE
MENIFNLFIKFFTTILEFIGTVAGDSVCASYFDEPEVPEELTKLYE

#* The agr typing is not defined, as I have compared the sequence with the amino acid sequences of AgrD 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

(1st optional for mixed) Prepare the tree using roary from the mixed clinical (fastq.gz) and database (fasta) genomes —-roary—-> core_gene_alignement.aln —-iqtree2—-> core_gene_alignment.aln.treefile

python ~/Scripts/run_mlst.py complete_genome_1282_ncbi.fasta sepidermidis  #73
python ~/Scripts/run_mlst.py complete_genome_1282_ena.fasta sepidermidis   #73
python ~/Scripts/run_mlst.py complete_genome_1282_ena_taxon_descendants.fasta sepidermidis
grep -P "\tsepidermidis\t2\t" all.mlst > ST2.mlst  #73
grep -v "NZ_" ST2.mlst > ST2_.mlst
cut -d'.' -f1 ST2_.mlst | sort > ST2.ids
cut -d'|' -f2 ST2.mlst | sort > ST2.ids
diff ST2.ids ../ena_descendants_all_ST2_mlst/ST2.ids

for sample in AP029227 CP013943 CP040883 CP045648 CP052939 CP052940 CP052955 CP052959 CP052981 CP052984 CP052990 CP052994 CP053088 CP064461 CP064467 CP064473 CP064476 CP064480 CP064485 CP064499 CP064508 CP064516 CP064525 CP064554 CP064557 CP064560 CP064572 CP064574 CP064587 CP064590 CP064599 CP064603 CP064606 CP064609 CP064613 CP064616 CP064619 CP064624 CP064631 CP064637 CP064650 CP073821 CP073824 CP073827 CP073830 CP073835 CP073836 CP073840 CP073841 CP073844 CP073847 CP073850 CP073852 CP073855 CP073857 CP073859 CP073862 CP073863 CP073878 CP073900 CP073904 CP084008 CP093173 CP093179 CP093181 CP093193 CP093196 CP093198 CP093212 CP097512 CP097514 CP120425 CP133663; do
    cp ../genomes_by_taxid/ncbi_all_ST2_mlst/${sample}.1.fasta ./
done
cd typing_complete.csv
grep -P "\tsepidermidis\t2\t" typing_complete.csv > typing_ST2.csv

echo -e "AP029227\nCP013943\nCP040883\nCP045648\nCP052939\nCP052940\nCP052955\nCP052959\nCP052981\nCP052984\nCP052990\nCP052994\nCP053088\nCP064461\nCP064467\nCP064473\nCP064476\nCP064480\nCP064485\nCP064499\nCP064508\nCP064516\nCP064525\nCP064554\nCP064557\nCP064560\nCP064572\nCP064574\nCP064587\nCP064590\nCP064599\nCP064603\nCP064606\nCP064609\nCP064613\nCP064616\nCP064619\nCP064624\nCP064631\nCP064637\nCP064650\nCP073821\nCP073824\nCP073827\nCP073830\nCP073835\nCP073836\nCP073840\nCP073841\nCP073844\nCP073847\nCP073850\nCP073852\nCP073855\nCP073857\nCP073859\nCP073862\nCP073863\nCP073878\nCP073900\nCP073904\nCP084008\nCP093173\nCP093179\nCP093181\nCP093193\nCP093196\nCP093198\nCP093212\nCP097512\nCP097514\nCP120425\nCP133663" > ids.txt
cat ids.txt | while read id; do
    efetch -db nuccore -id $id -format gff3 > "${id}.gff"
    efetch -db nuccore -id $id -format fasta > "${id}.fasta"
    # Append the FASTA sequence to the GFF3 file with ##FASTA header
    echo "##FASTA" >> "${id}.gff"
    cat "${id}.fasta" >> "${id}.gff"
    rm "${id}.fasta"  # Optionally remove the separate FASTA file
done
for sample in mibi1440 mibi1454 mibi1456 mibi1459 mibi1461 mibi1478 mibi1480 mibi1485 mibi1487 mibi1496 mibi1498 mibi1502 mibi2318 mibi2319; do
    cp ../prokka/${sample}/${sample}.gff ./
done

#run Roary:
roary -f roary_ST2 -e --mafft -p 100 roary_input_ST2/*.gff
#roary -p 15 -f ./roary -i 95 -cd 99 -s -e -n -v roary_.fa.gb.gff CP012351.gff3 CP003084.gff3 CP023676.gff3
#-i        minimum percentage identity for blastp [95]
#-cd FLOAT percentage of isolates a gene must be in to be core [99]
#-f STR    output directory [.]
#-e        create a multiFASTA alignment of core genes using PRANK
#-n (==--mafft)       fast core gene alignment with MAFFT, use with -e
#-s        dont split paralogs
#-v        verbose output to STDOUT

#The file core_gene_alignment.aln can then be used for tree construction:
iqtree2 -s core_gene_alignment.aln -m MFP -bb 1000 -nt AUTO

(2nd tree-option for purely clinical samples) Prepare the tree under bengal3_ac3 generating phylogeny_fasttree or phylogeny_raxml using snakemake for all clinical samples

#http://xgenes.com/article/article-content/372/identify-all-occurrences-of-phages-mt880870-mt880871-and-mt880872-in-s-epidermidis-st2-genomes-from-public-and-clinical-isolates/
bacto-0.1.json
        "fastqc": false,
        "taxonomic_classifier": false,
        "assembly": true,
        "typing_ariba": false,
        "typing_mlst": true,
        "pangenome": true,
        "variants_calling": true,
        "phylogeny_fasttree": true,
        "phylogeny_raxml": true,
        "recombination": true,

jhuang@WS-2290C:~/DATA/Data_Patricia_Sepi_7samples$ /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds

Preparing gene seqences (The following steps for calulating the presence-absence-matrix for predefined gene list)

(Optional online search) https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&BLAST_SPEC=MicrobialGenomes
#Title:Refseq prokaryote representative genomes (contains refseq assembly)
#Molecule Type:mixed DNA
#Update date:2024/10/16
#Number of sequences:1038672

# SCCmec,agr.typing,
# gyrB (House keeper),
# fumC,gltA,icd (Metabolic genes),
# apsS,sigB,sarA,agrC,yycG (Virulence regulators),
# psm.β (Toxins),    #psm.β1 --> psm.β and delete hlb
# atlE,sdrG,sdrH,ebh,ebpS,tagB (Biofilm formation),    #ebp-->ebpS
# pgsC,sepA,dltA,fmtC,lipA,sceD,SE0760 (Immune evasion & colonization),    #capC-->pgsC
# esp,ecpA (Serine protease)

#under ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates
#samtools faidx 20250204_Gene_sequences.fasta gltA > gltA_gold.fasta
#samtools faidx 20250204_Gene_sequences.fasta Psm > psm.β_gold.fasta
#samtools faidx 20250204_Gene_sequences.fasta ebpS > ebpS_gold.fasta
#samtools faidx 20250204_Gene_sequences.fasta tagB > tagB_gold.fasta
#samtools faidx 20250204_Gene_sequences.fasta PgsC > pgsC_gold.fasta
#samtools faidx 20250204_Gene_sequences.fasta sepA > sepA_gold.fasta
#samtools faidx 20250204_Gene_sequences.fasta fmtC > fmtC_gold.fasta
#samtools faidx 20250204_Gene_sequences.fasta sceD > sceD_gold.fasta
#samtools faidx 20250204_Gene_sequences.fasta esp > esp_gold.fasta
#samtools faidx 20250204_Gene_sequences.fasta ecpA > ecpA_gold.fasta

cd ~/DATA/Data_Patricia_Sepi_7samples/
mkdir presence_absence
cut -d$'\t' -f1-12 typing.csv > typing_f12.csv
cp typing_f12.csv presence_absence/typing.csv

cd presence_absence
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/gyrB_revcomp.fasta gyrB.fasta

cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/fumC_revcomp.fasta fumC.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/gltA_gold.fasta gltA.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/icd_revcomp.fasta icd.fasta

cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/apsS.fasta .
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/sigB_revcomp.fasta sigB.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/sarA_revcomp.fasta sarA.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/agrC.fasta .
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/yycG.fasta .

cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/psm.β_gold.fasta psm.β.fasta
#cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/hlb.fasta .

cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/atlE.fasta .
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/sdrG.fasta .
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/sdrH_revcomp.fasta sdrH.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/ebh_revcomp.fasta ebh.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/ebpS_gold.fasta ebpS.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/tagB_gold.fasta tagB.fasta

cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/pgsC_gold.fasta pgsC.fasta #early is capC
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/sepA_gold.fasta sepA.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/dltA.fasta .
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/fmtC_gold.fasta fmtC.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/lipA.fasta .
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/sceD_gold.fasta sceD.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/SE0760.fasta .

cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/esp_gold.fasta esp.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/ecpA_gold.fasta ecpA.fasta

Perform blastn searching for short-sequencing

# -- makeblastdb --
for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2 HDM2-SF1 HDM20-SF3; do
        makeblastdb -in ~/DATA/Data_Patricia_Sepi_7samples/shovill/${sample}/contigs.fa -dbtype nucl
done

# SCCmec,agr.typing,
# gyrB(House keeper),
# fumC,gltA,icd(Metabolic genes),
# apsS,sigB,sarA,agrC,yycG(Virulence regulators),
# psm.β (Toxins)    #psm.β1 --> psm.β and delete hlb
# atlE,sdrG,sdrH,ebh,ebpS,tagB(Biofilm formation),    #ebp-->ebpS
# pgsC,sepA,dltA,fmtC,lipA,sceD,SE0760(Immune evasion & colonization),    #capC-->pgsC
# esp,ecpA(Serine protease)

#Note: The current -evalue is set to 1e-1; it might need to be made more stringent.
for id in gyrB    fumC gltA icd    apsS sigB sarA agrC yycG    psm.β    atlE sdrG sdrH ebh ebpS tagB    pgsC sepA dltA fmtC lipA sceD SE0760    esp ecpA; do
        echo "mkdir ${id}"
        echo "for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2 HDM2-SF1 HDM20-SF3; do"
                echo "blastn -db ~/DATA/Data_Patricia_Sepi_7samples/shovill/\${sample}/contigs.fa -query ${id}.fasta -evalue 1e-1 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ./${id}/\${sample}.blastn"
        echo "done"
done

python ~/Scripts/process_directory.py gyrB typing.csv typing_until_gyrB.csv
python ~/Scripts/process_directory.py fumC typing_until_gyrB.csv typing_until_fumC.csv
python ~/Scripts/process_directory.py gltA typing_until_fumC.csv typing_until_gltA.csv
python ~/Scripts/process_directory.py icd typing_until_gltA.csv typing_until_icd.csv

python ~/Scripts/process_directory.py apsS typing_until_icd.csv typing_until_apsS.csv
python ~/Scripts/process_directory.py sigB typing_until_apsS.csv typing_until_sigB.csv
python ~/Scripts/process_directory.py sarA typing_until_sigB.csv typing_until_sarA.csv
python ~/Scripts/process_directory.py agrC typing_until_sarA.csv typing_until_agrC.csv
python ~/Scripts/process_directory.py yycG typing_until_agrC.csv typing_until_yycG.csv
python ~/Scripts/process_directory.py psm.β typing_until_yycG.csv typing_until_psm.β.csv
#python ~/Scripts/process_directory.py hlb typing_until_psm.β.csv typing_until_hlb.csv
python ~/Scripts/process_directory.py atlE typing_until_psm.β.csv typing_until_atlE.csv
python ~/Scripts/process_directory.py sdrG typing_until_atlE.csv typing_until_sdrG.csv
python ~/Scripts/process_directory.py sdrH typing_until_sdrG.csv typing_until_sdrH.csv

python ~/Scripts/process_directory.py ebh typing_until_sdrH.csv typing_until_ebh.csv
python ~/Scripts/process_directory.py ebpS typing_until_ebh.csv typing_until_ebpS.csv
python ~/Scripts/process_directory.py tagB typing_until_ebpS.csv typing_until_tagB.csv
python ~/Scripts/process_directory.py pgsC typing_until_tagB.csv typing_until_pgsC.csv
python ~/Scripts/process_directory.py sepA typing_until_pgsC.csv typing_until_sepA.csv
python ~/Scripts/process_directory.py dltA typing_until_sepA.csv typing_until_dltA.csv
python ~/Scripts/process_directory.py fmtC typing_until_dltA.csv typing_until_fmtC.csv
python ~/Scripts/process_directory.py lipA typing_until_fmtC.csv typing_until_lipA.csv

python ~/Scripts/process_directory.py sceD typing_until_lipA.csv typing_until_sceD.csv
python ~/Scripts/process_directory.py SE0760 typing_until_sceD.csv typing_until_SE0760.csv
python ~/Scripts/process_directory.py esp typing_until_SE0760.csv typing_until_esp.csv
python ~/Scripts/process_directory.py ecpA typing_until_esp.csv typing_until_ecpA.csv

Identify all occurrences of Phages MT880870, MT880871 and MT880872 in S. epidermidis genomes

# http://xgenes.com/article/article-content/371/identify-all-occurrences-of-phage-hh1-mt880870-in-s-epidermidis-st2-genomes-from-public-and-clinical-isolates/
# http://xgenes.com/article/article-content/372/identify-all-occurrences-of-phages-mt880870-mt880871-and-mt880872-in-s-epidermidis-st2-genomes-from-public-and-clinical-isolates/

cd presence_absence
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/MT880870.fasta .
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/MT880871.fasta .
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/MT880872.fasta .

samtools faidx MT880870.fasta
samtools faidx MT880871.fasta
samtools faidx MT880872.fasta
for i in {1..51}; do
    num=$(printf "%03d" $((571 + i)))  # Generates 572 to 622
    id="QPB07${num}"
    samtools faidx MT880870.fasta "lcl|MT880870.1_cds_${id}.1_${i}" > ${id}.fasta
done
for i in {1..45}; do
    num=$((622 + i))  # Generates 623 to 667
    id="QPB07${num}"
    samtools faidx MT880871.fasta "lcl|MT880871.1_cds_${id}.1_${i}" > ${id}.fasta
done
for i in {1..170}; do
    num=$((7667 + i))  # Generates 7668 to 7837
    id="QPB0$num"
    samtools faidx MT880872.fasta "lcl|MT880872.1_cds_${id}.1_${i}" > ${id}.fasta
done

for i in {572..622}; do
    id_1=$(printf "QPB07%03d" "$((i-1))")
    id=$(printf "QPB07%03d" "$i")
    echo "mkdir ${id}"
    echo "for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2 HDM2-SF1 HDM20-SF3; do"
    echo "blastn -db ~/DATA/Data_Patricia_Sepi_7samples/shovill/\${sample}/contigs.fa -query ${id}.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ./${id}/\${sample}.blastn"
    echo "done"
done

for i in {623..667}; do
    id=$(printf "QPB07%03d" "$i")
    echo "mkdir ${id}"
    echo "for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2 HDM2-SF1 HDM20-SF3; do"
    echo "blastn -db ~/DATA/Data_Patricia_Sepi_7samples/shovill/\${sample}/contigs.fa -query ${id}.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ./${id}/\${sample}.blastn"
    echo "done"
done

for i in {668..837}; do
    id=$(printf "QPB07%03d" "$i")
    echo "mkdir ${id}"
    echo "for sample in HDM1 HDM7 HDM10 HDM11-SF1 HDM15-SF2 HDM2-SF1 HDM20-SF3; do"
    echo "blastn -db ~/DATA/Data_Patricia_Sepi_7samples/shovill/\${sample}/contigs.fa -query ${id}.fasta -evalue 1e-10 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ./${id}/\${sample}.blastn"
    echo "done"
done

# -- under under the presence_absence DIR --
# - for generating a big Excel-table for showing all presence-absence info -
for i in {572..837}; do
    id_1=$(printf "QPB07%03d" "$((i-1))")
    id=$(printf "QPB07%03d" "$i")
    echo "python ~/Scripts/process_directory.py ${id} typing_until_${id_1}.csv typing_until_${id}.csv"
done
#Adapted the first record as: python ~/Scripts/process_directory.py QPB07572 typing_until_ecpA.csv typing_until_QPB07572.csv
#...
#cp typing_until_QPB07837.csv typing_all.csv  #Then save typing_all.csv as Excel table typing_all.xlsx!

# - for drawing seperate plots for phages and selected genes -
for i in {572..622}; do
    #echo "python ~/Scripts/process_directory.py ${id} typing_until_${id_1}.csv typing_until_${id}.csv"
    id_1=$(printf "QPB07%03d" "$((i-1))")
    id=$(printf "QPB07%03d" "$i")
    echo "python ~/Scripts/process_directory.py ${id} typing_until_${id_1}.csv typing_until_${id}.csv"
done
#Adapted the first record as: python ~/Scripts/process_directory.py QPB07572 typing.csv typing_until_QPB07572.csv
#reprace all '+' with 'MT880870' in typing_until_QPB07622.csv
sed -i 's/+/MT880870/g' typing_until_QPB07622.csv

for i in {623..667}; do
    id_1=$(printf "QPB07%03d" "$((i-1))")
    id=$(printf "QPB07%03d" "$i")
    echo "python ~/Scripts/process_directory.py ${id} typing_until_${id_1}.csv typing_until_${id}.csv"
done
sed -i 's/+/MT880871/g' typing_until_QPB07667.csv

for i in {668..837}; do
    id_1=$(printf "QPB07%03d" "$((i-1))")
    id=$(printf "QPB07%03d" "$i")
    echo "python ~/Scripts/process_directory.py ${id} typing_until_${id_1}.csv typing_until_${id}.csv"
done
sed -i 's/+/MT880872/g' typing_until_QPB07837.csv

plotTreeHeatmap (draw plot for phages)

library(ggtree)
library(ggplot2)
library(dplyr)
setwd("~/DATA/Data_Patricia_Sepi_7samples/presence_absence/")

info <- read.csv("typing_until_QPB07837.csv", sep="\t")
# Convert 'ST' column to factor with levels in the desired order
info$ST <- factor(info$ST, levels = c("5", "59", "87", "224", "-"))
info$name <- info$Isolate
# Prepare the snippy.core.aln.raxml.tree by deleting the reference from snippy.core.aln.raxml.bestTree
tree <- read.tree("~/DATA/Data_Patricia_Sepi_7samples/raxml-ng/snippy.core.aln.raxml.tree")
#cols <- c("Public database"='purple2', "Clinical isolate"='darksalmon')  #purple2  skyblue2
cols <- c("5"="darkred", "59"="cornflowerblue", "87"="orange", "224"="purple2", "-"="grey")
heatmapData2 <- info %>% dplyr::select(Isolate, QPB07572, QPB07573, QPB07574, QPB07575, QPB07576, QPB07577, QPB07578, QPB07579, QPB07580, QPB07581, QPB07582, QPB07583, QPB07584, QPB07585, QPB07586, QPB07587, QPB07588, QPB07589, QPB07590, QPB07591, QPB07592, QPB07593, QPB07594, QPB07595, QPB07596, QPB07597, QPB07598, QPB07599, QPB07600, QPB07601, QPB07602, QPB07603, QPB07604, QPB07605, QPB07606, QPB07607, QPB07608, QPB07609, QPB07610, QPB07611, QPB07612, QPB07613, QPB07614, QPB07615, QPB07616, QPB07617, QPB07618, QPB07619, QPB07620, QPB07621, QPB07622, QPB07623, QPB07624, QPB07625, QPB07626, QPB07627, QPB07628, QPB07629, QPB07630, QPB07631, QPB07632, QPB07633, QPB07634, QPB07635, QPB07636, QPB07637, QPB07638, QPB07639, QPB07640, QPB07641, QPB07642, QPB07643, QPB07644, QPB07645, QPB07646, QPB07647, QPB07648, QPB07649, QPB07650, QPB07651, QPB07652, QPB07653, QPB07654, QPB07655, QPB07656, QPB07657, QPB07658, QPB07659, QPB07660, QPB07661, QPB07662, QPB07663, QPB07664, QPB07665, QPB07666, QPB07667, QPB07668, QPB07669, QPB07670, QPB07671, QPB07672, QPB07673, QPB07674, QPB07675, QPB07676, QPB07677, QPB07678, QPB07679, QPB07680, QPB07681, QPB07682, QPB07683, QPB07684, QPB07685, QPB07686, QPB07687, QPB07688, QPB07689, QPB07690, QPB07691, QPB07692, QPB07693, QPB07694, QPB07695, QPB07696, QPB07697, QPB07698, QPB07699, QPB07700, QPB07701, QPB07702, QPB07703, QPB07704, QPB07705, QPB07706, QPB07707, QPB07708, QPB07709, QPB07710, QPB07711, QPB07712, QPB07713, QPB07714, QPB07715, QPB07716, QPB07717, QPB07718, QPB07719, QPB07720, QPB07721, QPB07722, QPB07723, QPB07724, QPB07725, QPB07726, QPB07727, QPB07728, QPB07729, QPB07730, QPB07731, QPB07732, QPB07733, QPB07734, QPB07735, QPB07736, QPB07737, QPB07738, QPB07739, QPB07740, QPB07741, QPB07742, QPB07743, QPB07744, QPB07745, QPB07746, QPB07747, QPB07748, QPB07749, QPB07750, QPB07751, QPB07752, QPB07753, QPB07754, QPB07755, QPB07756, QPB07757, QPB07758, QPB07759, QPB07760, QPB07761, QPB07762, QPB07763, QPB07764, QPB07765, QPB07766, QPB07767, QPB07768, QPB07769, QPB07770, QPB07771, QPB07772, QPB07773, QPB07774, QPB07775, QPB07776, QPB07777, QPB07778, QPB07779, QPB07780, QPB07781, QPB07782, QPB07783, QPB07784, QPB07785, QPB07786, QPB07787, QPB07788, QPB07789, QPB07790, QPB07791, QPB07792, QPB07793, QPB07794, QPB07795, QPB07796, QPB07797, QPB07798, QPB07799, QPB07800, QPB07801, QPB07802, QPB07803, QPB07804, QPB07805, QPB07806, QPB07807, QPB07808, QPB07809, QPB07810, QPB07811, QPB07812, QPB07813, QPB07814, QPB07815, QPB07816, QPB07817, QPB07818, QPB07819, QPB07820, QPB07821, QPB07822, QPB07823, QPB07824, QPB07825, QPB07826, QPB07827, QPB07828, QPB07829, QPB07830, QPB07831, QPB07832, QPB07833, QPB07834, QPB07835, QPB07836, QPB07837)  #ST,

rn <- heatmapData2$Isolate
heatmapData2$Isolate <- NULL
heatmapData2 <- as.data.frame(sapply(heatmapData2, as.character))
rownames(heatmapData2) <- rn
heatmap.colours <- c("darkred", "darkblue", "darkgreen", "grey")
names(heatmap.colours) <- c("MT880870","MT880871","MT880872","-")

#mydat$Regulation <- factor(mydat$Regulation, levels=c("up","down"))
#circular
#geom_tippoint(aes(color=Source)) + scale_color_manual(values=cols)
p <- ggtree(tree, layout='circular', branch.length='none') %<+% info + geom_tippoint(aes(color=ST), size=4) + scale_color_manual(values=cols) + geom_tiplab2(aes(label=name), offset=1)
png("ggtree_phages.png", width=1260, height=1260)
#svg("ggtree_phages.svg", width=1260, height=1260)
p
dev.off()

#png("ggtree_and_gheatmap_phages.png", width=1290, height=1000)
#svg("ggtree_and_gheatmap_phages.svg", width=17, height=15)
#gheatmap(p, heatmapData2, width=0.1, colnames_position="top", colnames_angle=90, colnames_offset_y = 0.1, hjust=0.5, font.size=4, offset = 5) + scale_fill_manual(values=heatmap.colours) +  theme(legend.text = element_text(size = 14)) + theme(legend.title = element_text(size = 14)) + guides(fill=guide_legend(title=""), color = guide_legend(override.aes = list(size = 5)))

png("ggtree_and_gheatmap_phages.png", width=1290, height=1000)
gheatmap(p, heatmapData2, width=0.5, colnames_position="top", colnames_angle=90, colnames_offset_y = 0.0, hjust=0.5, font.size=0, offset = 3) + scale_fill_manual(values=heatmap.colours) +  theme(legend.text = element_text(size = 16)) + theme(legend.title = element_text(size = 16)) + guides(fill=guide_legend(title=""), color = guide_legend(override.aes = list(size = 5)))
dev.off()

plotTreeHeatmap (draw plot for selected genes)

#FastTree -gtr -nt variants/snippy.core_without_reference.aln > plotTreeHeatmap/snippy.core.tree

#cp ../Data_Holger_S.epidermidis_short/plotTreeHeatmap/typing_104.csv .
#cp ../Data_Holger_S.epidermidis_short/results_HDRNA_01-20/variants/snippy.core.aln_104.tree .
#Run step 4

library(ggtree)
library(ggplot2)
library(dplyr)
setwd("~/DATA/Data_Patricia_Sepi_7samples/presence_absence/")

# -- edit tree --
info <- read.csv("typing_until_ecpA.csv", sep="\t")
# Convert 'ST' column to factor with levels in the desired order
info$ST <- factor(info$ST, levels = c("5", "59", "87", "224", "-"))

info$name <- info$Isolate
tree <- read.tree("~/DATA/Data_Patricia_Sepi_7samples/raxml-ng/snippy.core.aln.raxml.tree")
cols <- c("5"="darkred", "59"="cornflowerblue", "87"="orange", "224"="purple2", "-"="grey")

heatmapData2 <- info %>% dplyr::select(Isolate,          gyrB,    fumC, gltA, icd,    apsS, sigB, sarA, agrC, yycG,    psm.β,    atlE, sdrG, sdrH, ebh, ebpS, tagB,    pgsC, sepA, dltA, fmtC, lipA, sceD, SE0760,    esp, ecpA)  #ST,
rn <- heatmapData2$Isolate
heatmapData2$Isolate <- NULL
heatmapData2 <- as.data.frame(sapply(heatmapData2, as.character))
rownames(heatmapData2) <- rn

heatmap.colours <- c("darkgreen", "grey")
names(heatmap.colours) <- c("+","-")

#heatmap.colours <- c("darkred", "darkblue", "darkgreen", "grey")
#names(heatmap.colours) <- c("MT880870","MT880871","MT880872","-")
#heatmap.colours <- c("cornflowerblue","darkgreen","seagreen3","tan","red",  "navyblue", "gold",     "green","orange","pink","purple","magenta","brown", "darksalmon","chocolate4","darkkhaki", "azure3", "maroon","lightgreen",     "blue","cyan", "skyblue2", "blueviolet",       "darkred", "darkblue", "darkgreen", "grey")
#names(heatmap.colours) <- c("2","5","7","9","14", "17","23",   "35","59","73", "81","86","87","89","130","190","290", "297","325",    "454","487","558","766",       "MT880870","MT880871","MT880872","-")
#TEMP_DEACTIVATED!  heatmap.colours <- c("cornflowerblue","darkgreen","seagreen3","tan","red",  "navyblue", "purple",     "green","cyan",       "darkred", "darkblue", "darkgreen", "grey",  "darkgreen", "grey")
#TEMP_DEACTIVATED!  names(heatmap.colours) <- c("SCCmec_type_II(2A)", "SCCmec_type_III(3A)", "SCCmec_type_III(3A) and SCCmec_type_VIII(4A)", "SCCmec_type_IV(2B)", "SCCmec_type_IV(2B&5)", "SCCmec_type_IV(2B) and SCCmec_type_VI(4B)", "SCCmec_type_IVa(2B)", "SCCmec_type_IVb(2B)", "SCCmec_type_IVg(2B)",  "I", "II", "III", "none", "+","-")
## The heatmap colours should correspond to the factor levels
#heatmap.colours <- #setNames(c("cornflowerblue","darkgreen","seagreen3","tan","red","green","orange","pink","brown","magenta","cyan",
#"magenta","navyblue","cornflowerblue","gold","orange","darkgreen", "green", "seagreen3", #"chocolate4","brown","purple","pink", "tan","cyan","red"),
#c("5", "23", "35", "69", "86", "87", "130", "224", "487", "640", "none", "P01", "P02", "P03", "P04", "P05", "P06", "P07", #"P08", "P10", "P11", "P12", "P16", "P17", "P19", "P20"))

#mydat$Regulation <- factor(mydat$Regulation, levels=c("up","down"))
#circular
#p <- ggtree(tree, layout='circular', branch.length='none') %<+% info + geom_tippoint(aes(color=ST)) + scale_color_manual(values=cols) + geom_tiplab2(aes(label=name), offset=1)
p <- ggtree(tree, layout='circular', branch.length='none') %<+% info + geom_tippoint(aes(color=ST), size=4) + scale_color_manual(values=cols) + geom_tiplab2(aes(label=name), offset=1)
png("ggtree_selected_genes.png", width=1260, height=1260)
#svg("ggtree_selected_genes.svg", width=1260, height=1260)
p
dev.off()
#gheatmap(p, heatmapData2, width=0.1, colnames_position="top", colnames_angle=90, colnames_offset_y = 0.1, hjust=0.5, font.size=4, offset = 5) + scale_fill_manual(values=heatmap.colours) +  theme(legend.text = element_text(size = 14)) + theme(legend.title = element_text(size = 14)) + guides(fill=guide_legend(title=""), color = guide_legend(override.aes = list(size = 5)))
#svg("ggtree_and_gheatmap_mibi_selected_genes.svg", width=17, height=15)

png("ggtree_and_gheatmap_selected_genes.png", width=1690, height=1400)
gheatmap(p, heatmapData2, width=4, colnames_position="top", colnames_angle=45, colnames_offset_y = 0.0, hjust=0.5, font.size=5.8, offset = 3.8) + scale_fill_manual(values=heatmap.colours) +  theme(legend.text = element_text(size = 16)) + theme(legend.title = element_text(size = 16)) + guides(fill=guide_legend(title=""), color = guide_legend(override.aes = list(size = 5)))
dev.off()

(optional) Prepare the long fasta if we use only the long sequencing

ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_01_K01_conservative.fasta HDRNA_01_K01.fasta
ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_03_K01_bold_bandage.fasta HDRNA_03_K01.fasta
ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_06_K01_conservative.fasta HDRNA_06_K01.fasta
ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_07_K01_conservative.fasta HDRNA_07_K01.fasta
ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_08_K01_conservative.fasta HDRNA_08_K01.fasta
ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_12_K01_bold.fasta HDRNA_12_K01.fasta
ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_16_K01_conservative.fasta HDRNA_16_K01.fasta
ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_17_K01_conservative.fasta HDRNA_17_K01.fasta
ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_19_K01_bold.fasta HDRNA_19_K01.fasta
ln -s ../Data_Holger_S.epidermidis_long/finished_assemblies/HDRNA_20_K01_conservative.fasta HDRNA_20_K01.fasta

(optional) Perform blastn searching for long-sequencing

#under ~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/plotTreeHeatmap_long
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/gltA_gold.fasta gltA.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/psm.β_gold.fasta psm.β.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/ebpS_gold.fasta ebpS.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/tagB_gold.fasta tagB.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/pgsC_gold.fasta pgsC.fasta #early is capC
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/sepA_gold.fasta sepA.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/fmtC_gold.fasta fmtC.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/sceD_gold.fasta sceD.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/esp_gold.fasta esp.fasta
cp ~/DATA/Data_Luise_Sepi_STKN/plotTreeHeatmap_mibi_isolates/ecpA_gold.fasta ecpA.fasta

# -- makeblastdb --
for sample in HDRNA_01_K01 HDRNA_03_K01 HDRNA_06_K01 HDRNA_07_K01 HDRNA_08_K01 HDRNA_12_K01 HDRNA_16_K01 HDRNA_17_K01 HDRNA_19_K01 HDRNA_20_K01; do
        makeblastdb -in ../assembly/${sample}.fasta -dbtype nucl
done

for id in gyrB    fumC gltA icd    apsS sigB sarA agrC yycG    psm.β hlb    atlE sdrG sdrH ebh ebpS tagB    pgsC sepA dltA fmtC lipA sceD SE0760    esp ecpA; do
#for id in gltA psm.β ebpS tagB pgsC sepA fmtC sceD esp ecpA; do
echo "mkdir ${id}"
echo "for sample in in HDRNA_01_K01 HDRNA_03_K01 HDRNA_06_K01 HDRNA_07_K01 HDRNA_08_K01 HDRNA_12_K01 HDRNA_16_K01 HDRNA_17_K01 HDRNA_19_K01 HDRNA_20_K01; do"
        echo "blastn -db ../assembly/${sample}.fasta -query ${id}.fasta -evalue 1e-40 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ./${id}/\${sample}.blastn"
echo "done"
done

python ~/Scripts/process_directory.py gyrB typing.csv typing_until_gyrB.csv
python ~/Scripts/process_directory.py fumC typing_until_gyrB.csv typing_until_fumC.csv
python ~/Scripts/process_directory.py gltA typing_until_fumC.csv typing_until_gltA.csv
python ~/Scripts/process_directory.py icd typing_until_gltA.csv typing_until_icd.csv

python ~/Scripts/process_directory.py apsS typing_until_icd.csv typing_until_apsS.csv
python ~/Scripts/process_directory.py sigB typing_until_apsS.csv typing_until_sigB.csv
python ~/Scripts/process_directory.py sarA typing_until_sigB.csv typing_until_sarA.csv
python ~/Scripts/process_directory.py agrC typing_until_sarA.csv typing_until_agrC.csv
python ~/Scripts/process_directory.py yycG typing_until_agrC.csv typing_until_yycG.csv
python ~/Scripts/process_directory.py psm.β typing_until_yycG.csv typing_until_psm.β.csv
python ~/Scripts/process_directory.py hlb typing_until_psm.β1.csv typing_until_hlb.csv
python ~/Scripts/process_directory.py atlE typing_until_hlb.csv typing_until_atlE.csv
python ~/Scripts/process_directory.py sdrG typing_until_atlE.csv typing_until_sdrG.csv
python ~/Scripts/process_directory.py sdrH typing_until_sdrG.csv typing_until_sdrH.csv

python ~/Scripts/process_directory.py ebh typing_until_sdrH.csv typing_until_ebh.csv
python ~/Scripts/process_directory.py ebpS typing_until_ebh.csv typing_until_ebpS.csv
python ~/Scripts/process_directory.py tagB typing_until_ebp.csv typing_until_tagB.csv
python ~/Scripts/process_directory.py pgsC typing_until_tagB.csv typing_until_pgsC.csv
python ~/Scripts/process_directory.py sepA typing_until_capC.csv typing_until_sepA.csv
python ~/Scripts/process_directory.py dltA typing_until_sepA.csv typing_until_dltA.csv
python ~/Scripts/process_directory.py fmtC typing_until_dltA.csv typing_until_fmtC.csv
python ~/Scripts/process_directory.py lipA typing_until_fmtC.csv typing_until_lipA.csv

python ~/Scripts/process_directory.py sceD typing_until_lipA.csv typing_until_sceD.csv
python ~/Scripts/process_directory.py SE0760 typing_until_sceD.csv typing_until_SE0760.csv
python ~/Scripts/process_directory.py esp typing_until_SE0760.csv typing_until_esp.csv
python ~/Scripts/process_directory.py ecpA typing_until_esp.csv typing_until_ecpA.csv

Report

Attached is the presence-absence table (typing_all.xlsx), which includes the presence and absence status of all selected genes (listed below), as well as all genes from the three phages MT880870, MT880871, and MT880872.

You may notice some differences in the results for the selected genes compared to the ones I sent earlier. This is because Holger provided updated sequences, correcting those I previously used for the selected genes.

Additionally, I’ve included two graphics visualizing the presence-absence table:
- Selected genes: ggtree_and_gheatmap_selected_genes.png
- Phage genes (MT880870, MT880871, and MT880872): ggtree_and_gheatmap_phages.png
Here’s the list of selected genes:
- Housekeeping gene: gyrB
- Metabolic genes: fumC, gltA, icd
- Virulence regulators: apsS, sigB, sarA, agrC, yycG
- Toxins: psmβ
- Biofilm formation: atlE, sdrG, sdrH, ebh, ebpS, tagB
- Immune evasion & colonization: pgsC, sepA, dltA, fmtC, lipA, sceD, SE0760
- Serine protease: esp, ecpA

Microbial bioinformatics

Microbial bioinformatics uses computational tools to analyze genomes, track evolution, and study functions in microorganisms, including bacteria and viruses.

Presence-Absence Table and Graphics for Selected Genes in Data_Patricia_Sepi_7samples

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