Data_Tam_DNAseq_2025 NACHREICHEN_1

gene_x 0 like s 169 view s

Tags: pipeline

For Clinical and HF

a) Please proceed with SNP and indel calling for both clinical samples.

  1.     #-- DNAseq_2025_AYE: clinical vs CP149838 (Acinetobacter baumannii strain 2024CK-00130) --
  3.     mkdir -p bacto_clinical_vs_CP149838/raw_data; cd bacto_clinical_vs_CP149838;
  4.     ln -s ~/Tools/bacto/db/ .;
  5.     ln -s ~/Tools/bacto/envs/ .;
  6.     ln -s ~/Tools/bacto/local/ .;
  7.     cp ~/Tools/bacto/Snakefile .;
  8.     cp ~/Tools/bacto/bacto-0.1.json .;
  9.     cp ~/Tools/bacto/cluster.json .;
  11.     cd raw_data
  12.     ln -s ../../X101SC25015922-Z01-J001/01.RawData/clinical/clinical_1.fq.gz clinical_R1.fastq.gz
  13.     ln -s ../../X101SC25015922-Z01-J001/01.RawData/clinical/clinical_2.fq.gz clinical_R2.fastq.gz
  15.     (bengal3_ac3) /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds
  17.     # Annotate the snippy results using SNP_matrix.sh from spandx
  18.     (spandx) cd bacto_clinical_vs_CP149838/snippy/clinical
  19.     (spandx) cp ~/Tools/spandx/bin/SNP_matrix.sh ./
  20.     (spandx) cp clinical.filt.vcf out.filtered.vcf
  21.     (spandx) cp clinical.filt.vcf out.vcf
  22.     #Adapt to "/home/jhuang/miniconda3/envs/bengal3_ac3/bin/snpEff eff -no-downstream -no-intergenic -ud 100 -formatEff -c reference/snpeff.config -dataDir . ref out.vcf > out.annotated.vcf" in SNP_matrix.sh
  23.     (spandx) bash SNP_matrix.sh CP149838 .
  24.     #(ANNOTATED_RESULTS_SNIPPY) ~/Tools/csv2xls-0.4/csv_to_xls.py All_SNPs_indels_annotated.txt -d$'\t' -o ../../../clinical_vs_CP149838_SNPs_indels_annotated.xls
  26.     #-- DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2: HF vs CP095177.1 (Enterobacter hormaechei strain K528) --
  28.     mkdir -p bacto_HF_vs_CP095177/raw_data; cd bacto_HF_vs_CP095177;
  29.     ln -s ~/Tools/bacto/db/ .;
  30.     ln -s ~/Tools/bacto/envs/ .;
  31.     ln -s ~/Tools/bacto/local/ .;
  32.     cp ~/Tools/bacto/Snakefile .;
  33.     cp ~/Tools/bacto/bacto-0.1.json .;
  34.     cp ~/Tools/bacto/cluster.json .;
  36.     cd raw_data
  37.     ln -s ../../X101SC24115801-Z01-J001/01.RawData/HF/HF_1.fq.gz HF_R1.fastq.gz
  38.     ln -s ../../X101SC24115801-Z01-J001/01.RawData/HF/HF_2.fq.gz HF_R2.fastq.gz
  40.     mamba activate /home/jhuang/miniconda3/envs/bengal3_ac3
  41.     (bengal3_ac3) /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds
  43.     # Annotate the Snippy results using SNP_matrix.sh from SPANDx, disregarding the runtime error.
  44.     mamba activate /home/jhuang/miniconda3/envs/spandx
  45.     (spandx) cd bacto_HF_vs_CP095177/snippy/HF
  46.     (spandx) cp ~/Tools/spandx/bin/SNP_matrix.sh ./
  47.     (spandx) cp HF.filt.vcf out.filtered.vcf
  48.     (spandx) cp HF.filt.vcf out.vcf
  49.     #Adapt to "/home/jhuang/miniconda3/envs/bengal3_ac3/bin/snpEff eff -no-downstream -no-intergenic -ud 100 -formatEff -c reference/snpeff.config -dataDir . ref out.vcf > out.annotated.vcf" in SNP_matrix.sh
  50.     (spandx) bash SNP_matrix.sh CP095177 .
  51.     #(ANNOTATED_RESULTS_SNIPPY) ~/Tools/csv2xls-0.4/csv_to_xls.py All_SNPs_indels_annotated.txt -d$'\t' -o ../../../HF_vs_CP095177_SNPs_indels_annotated.xls

b) Additionally, I would like a phylogenetic tree (evolutionary relationship between our samples and other reference genomes) and

  1.         #using Dendogram tool

c) A sequence alignment figure (similar to this example: https://macvector.com/blog/2021/10/compare-a-pair-of-genomes/) comparing the reference genome with our samples.

  1.     # TODO: Using BRIG: see http://xgenes.com/article/article-content/325/analysis-of-snps-indels-transposons-and-is-elements-in-5-a-baumannii-strains/
  3.     * DNAseq_2025_AYE: others vs CU459141
  4.     cd ~/Tools/BRIG-0.95-dist
  5.     #IMPORTANT_CRITICAL_DEBUG: we should use jdk1.6, otherwise results in ERROR!
  6.     ~/Tools/Java1.6/jdk1.6.0_45/bin/java -Xmx25000M -jar BRIG.jar
  8.     #1. Select Reference Genbank file in first textfield.
  9.     #2. Prepare assembled fasta-files and sepecific gene-sequences as "Query sequence" in the folder "~/DATA/Data_Tam_DNAseq_2025_AYE/shovill/ and ~/DATA/Data_Tam_DNAseq_2025_AYE/" by "cp AYE-Q/contigs.fa AYE-Q.fasta; ...; samtools faidx CP059040.fasta CP059040:1844319-1845509 > adeA.fasta; ..." in second textfield.
  10.     #3. Saved bundle_sessions --> saved all files in a seperate directory --> open session brigWorkspace_AYE (Note that sometimes the fasta-files is too large (severeal GBs) and all of them have been copied to "~/brigWorkspace_adeABadeIJ_adeIJK_CM1_CM2/gen")
  12.     * DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2: others to CP059040
  14.             samtools faidx CP059040.fasta CP059040:1844319-1845509 > adeA.fasta
  15.             samtools faidx CP059040.fasta CP059040:1845506-1848616 > adeB.fasta
  16.             samtools faidx CP059040.fasta CP059040:740422-741672 > adeI_.fasta
  17.             revcomp adeI_.fasta > adeI.fasta
  18.             samtools faidx CP059040.fasta CP059040:737233-740409 > adeJ_.fasta
  19.             revcomp adeJ_.fasta > adeJ.fasta
  20.             samtools faidx CP059040.fasta CP059040:735779-737233 > adeK_.fasta
  21.             revcomp adeK_.fasta > adeK.fasta
  23.             gene            1844319..1845509
  24.             /gene="adeA"
  25.             /locus_tag="H0N29_08675"
  27.             gene            1845506..1848616
  28.             /gene="adeB"
  29.             /locus_tag="H0N29_08680"
  31.             gene            complement(740422..741672)
  32.             /gene="adeI"
  33.             /locus_tag="H0N29_03550"
  35.             gene            complement(737233..740409)
  36.             /gene="adeJ"
  37.             /locus_tag="H0N29_03545"
  39.             gene            complement(735779..737233)
  40.             /gene="adeK"
  41.             /locus_tag="H0N29_03540"
  43.     #jhuang@WS-2290C:~/brigWorkspace_adeABadeIJ_adeIJK_CM1_CM2/scratch$ vim adeAB.fastaVsCP059040.gb.fna.tab
  44.     #jhuang@WS-2290C:~/brigWorkspace_adeABadeIJ_adeIJK_CM1_CM2/scratch$ vim adeIJK.fastaVsCP059040.gb.fna.tab
  46.     #TODO: Optimize the cutoff so that vey short matches not used for the BRIG drawing!
  47.     #/home/jhuang/miniconda3/bin/blastn -outfmt 6 -query /home/jhuang/DATA/Data_Tam_DNAseq_2025_AYE/shovill/AYE-S.fasta -db /home/jhuang/brigWorkspace/scratch/CU459141.gb.fna -out /home/jhuang/brigWorkspace/scratch/AYE-S.fastaVsCU459141.gb.fna.tab   -task blastn
  48.     "-evalue 1e-1 -max_target_seqs 1"  #-strand both -num_threads 15 -outfmt 6
  49.     cd ~/brigWorkspace_adeABadeIJ_adeIJK_CM1_CM2/scratch/
  51.     cp /home/jhuang/Tools/bacto/db/CU459141.gb ~/brigWorkspace_AYE    #DEBUG due to the empty genbank file
  52.     #mv AYE-Q.fastaVsCU459141.gb.fna.tab AYE-Q.fastaVsCU459141.gb.fna.tab_
  53.     #mv AYE-S.fastaVsCU459141.gb.fna.tab AYE-S.fastaVsCU459141.gb.fna.tab_
  54.     #mv AYE-WT_on_Tig4.fastaVsCU459141.gb.fna.tab AYE-WT_on_Tig4.fastaVsCU459141.gb.fna.tab_
  55.     #mv AYE-craA_on_Tig4.fastaVsCU459141.gb.fna.tab AYE-craA_on_Tig4.fastaVsCU459141.gb.fna.tab_
  56.     #mv AYE-craA-1_on_Cm200.fastaVsCU459141.gb.fna.tab AYE-craA-1_on_Cm200.fastaVsCU459141.gb.fna.tab_
  57.     #mv AYE-craA-2_on_Cm200.fastaVsCU459141.gb.fna.tab AYE-craA-2_on_Cm200.fastaVsCU459141.gb.fna.tab_
  58.     python3 ~/Scripts/filter_blast.py AYE-Q.fastaVsCU459141.gb.fna.tab_ AYE-Q.fastaVsCU459141.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  59.     python3 ~/Scripts/filter_blast.py AYE-S.fastaVsCU459141.gb.fna.tab_ AYE-S.fastaVsCU459141.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  60.     python3 ~/Scripts/filter_blast.py AYE-WT_on_Tig4.fastaVsCU459141.gb.fna.tab_ AYE-WT_on_Tig4.fastaVsCU459141.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  61.     python3 ~/Scripts/filter_blast.py AYE-craA_on_Tig4.fastaVsCU459141.gb.fna.tab_ AYE-craA_on_Tig4.fastaVsCU459141.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  62.     python3 ~/Scripts/filter_blast.py AYE-craA-1_on_Cm200.fastaVsCU459141.gb.fna.tab_ AYE-craA-1_on_Cm200.fastaVsCU459141.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  63.     python3 ~/Scripts/filter_blast.py AYE-craA-2_on_Cm200.fastaVsCU459141.gb.fna.tab_ AYE-craA-2_on_Cm200.fastaVsCU459141.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  65.     cp /home/jhuang/Tools/bacto/db/CP059040.gb ~/brigWorkspace_adeABadeIJ_adeIJK_CM1_CM2    #DEBUG due to the empty genbank file
  66.     #mv deltaAdeIJK.fastaVsCP059040.gb.fna.tab deltaAdeIJK.fastaVsCP059040.gb.fna.tab_
  67.     #mv deltaAdeABIJ.fastaVsCP059040.gb.fna.tab deltaAdeABIJ.fastaVsCP059040.gb.fna.tab_
  68.     #mv CM1.fastaVsCP059040.gb.fna.tab CM1.fastaVsCP059040.gb.fna.tab_
  69.     #mv CM2.fastaVsCP059040.gb.fna.tab CM2.fastaVsCP059040.gb.fna.tab_
  70.     python3 ~/Scripts/filter_blast.py deltaAdeIJK.fastaVsCP059040.gb.fna.tab_ deltaAdeIJK.fastaVsCP059040.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  71.     python3 ~/Scripts/filter_blast.py deltaAdeABIJ.fastaVsCP059040.gb.fna.tab_ deltaAdeABIJ.fastaVsCP059040.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  72.     python3 ~/Scripts/filter_blast.py CM1.fastaVsCP059040.gb.fna.tab_ CM1.fastaVsCP059040.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  73.     python3 ~/Scripts/filter_blast.py CM2.fastaVsCP059040.gb.fna.tab_ CM2.fastaVsCP059040.gb.fna.tab --evalue 1e-10 --min_length 100 --min_identity 90
  75.     * DNAseq_2025_AYE: clinical vs gi|2707120326|gb|CP149838.1| (Acinetobacter baumannii strain 2024CK-00130)
  77.     mamba activate /home/jhuang/miniconda3/envs/bakta
  78.     bakta --db /mnt/nvme1n1p1/bakta_db ~/DATA/Data_Tam_DNAseq_2025_AYE/vrap_clinical/vrap_contig.fasta --prefix vrap_clinical
  80.     * DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2: HF vs gi|2239477175|gb|CP095177.1| (Enterobacter hormaechei strain K528)
  82.     bakta --db /mnt/nvme1n1p1/bakta_db ~/DATA/Data_Tam_DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2/vrap_HF/vrap_contig.fasta --prefix vrap_HF
  84.     # Using Artemis (ACT) to draw the comparisons (https://sanger-pathogens.github.io/Artemis/; https://github.com/sanger-pathogens/Artemis)
  85.     tar zxf artemis-unix-release-{version}.tar.gz
  87.             ~/Scripts/embl_to_gbk.py CP149838.embl CP149838.gbk
  88.             ~/Scripts/embl_to_gbk.py vrap_clinical.embl clinical.gbk
  89.             ~/Scripts/embl_to_gbk.py CP095177.embl CP095177.gbk
  90.             ~/Scripts/embl_to_gbk.py vrap_HF.embl HF.gbk
  92.     TODO_NEXT_WEEK: comparison_file_1 is defined as follows: Common formats: .crunch (BLAST), .delta (MUMmer), or .align (Exonerate).
  93.     blastn -query ref_genome.fasta -subject sample_genome.fasta -outfmt 6 > blast.crunch
  95.     # Step 1: Extract FASTA from GenBank (if needed for alignment)
  96.     ~/Scripts/genbank2fasta.py CP149838.gbk
  97.     ~/Scripts/genbank2fasta.py clinical.gbk
  98.     # Step 2: Run BLAST/MUMmer to generate comparison file
  99.     blastn -query CP149838.gbk_converted.fna -subject clinical.gbk_converted.fna -outfmt 6 > blast.crunch
  101.     Input:
  102.             Sequence file 1: reference.gb (annotated reference genome).
  103.             Sequence file 2: sample.gb (annotated sample genome).
  104.             Comparison file 1: nucmer.delta (generated with nucmer reference.fasta sample.fasta).
  105.     Output:
  106.     ACT will display both genomes with annotations, aligned based on the .delta file.
  108.             The image appears to be a genome visualization, possibly from a tool like Artemis, ACT (Artemis Comparison Tool), or another genome browser. Here’s what’s likely happening in the plot:
  109.             Genome Overview
  111.             Three Rows for Forward Strand and Three Rows for Reverse Strand
  113.                     Each genome sequence has genes and annotations on both forward and reverse strands.
  115.                     The three rows in each direction represent different levels of gene annotations to avoid overlap and make visualization easier.
  117.             Why Three Rows?
  119.                     Genes in a genome can be closely packed or even overlapping.
  121.                     To prevent clutter, genome visualization tools often distribute overlapping genes across multiple rows.
  123.                     This is purely for visual clarity and does not indicate different types of genes.
  125.             Key Features in the Image
  127.             Black vertical bars: Represent DNA sequences.
  129.             Blue/Green Annotations: Likely represent genes or coding sequences (CDS).
  131.             Gray Annotations: Might be regulatory elements or pseudogenes.
  133.             Thicker Blue Section: Could represent regions of high similarity between sequences
  135.             #Some genome browsers use frame lines to indicate the three possible reading frames on each strand.
  136.             #Top three lines: Represent the three possible reading frames of the forward strand (5' → 3').
  137.             #Bottom three lines: Represent the three possible reading frames of the reverse strand (3' → 5').
  138.             #Light grey areas = ORFs (regions that can actually be translated into proteins).
  139.             #If an area is not light grey, it likely means that no ORF exists in that reading frame, meaning there are stop codons breaking the sequence.
  141.             #TODO_NEXT_MONDAY: how to zoom in the ACT browser?

d) Would it be possible to perform genome annotation for these samples?

  1.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain AYE-Q ../shovill/AYE-Q.fasta
  2.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain AYE-S ../shovill/AYE-S.fasta
  3.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain AYE-WT_on_Tig4 ../shovill/AYE-WT_on_Tig4.fasta
  4.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain AYE-craA_on_Tig4 ../shovill/AYE-craA_on_Tig4.fasta
  5.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain AYE-craA-1_on_Cm200 ../shovill/AYE-craA-1_on_Cm200.fasta
  6.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain AYE-craA-2_on_Cm200 ../shovill/AYE-craA-2_on_Cm200.fasta
  7.             cd ~/DATA/Data_Tam_DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2/bakta
  8.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain deltaAdeIJK ../shovill/deltaAdeIJK.fasta
  9.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain deltaAdeABIJ ../shovill/deltaAdeABIJ.fasta
  10.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain CM1 ../shovill/CM1.fasta
  11.             bakta --db /mnt/nvme1n1p1/bakta_db --genus Acinetobacter --species baumannii --strain CM2 ../shovill/CM2.fasta
  12.             #END

e) Could you please perform a search for HF sample against CP095179 and CP095178 as Enterobacter hormaechei strain K528 harbours two plasmids.

  1.     In addition to my comments from my previous email, could you please perform a search for HF sample against CP095179 and CP095178 as Enterobacter hormaechei strain K528 harbours two plasmids.
  3.     Solution: Using the bowtie of vrap to map the reads on ref_genome/reference.fasta (The reference refers to the closest related genome found from the list generated by vrap)
  5.     mamba activate /home/jhuang/miniconda3/envs/vrap
  6.     (vrap) vrap/vrap.py  -1 HF_trimmed_P_1.fastq.gz -2 HF_trimmed_P_2.fastq.gz  -o vrap_HF_on_CP095178_and_CP095179 --host /home/jhuang/DATA/Data_Tam_DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2/CP095178_CP095179.fasta   -t 100 -l 200  -g
  7.     cd bowtie
  8.     mv mapped mapped.sam
  9.     samtools view -S -b mapped.sam > mapped.bam
  10.     samtools sort mapped.bam -o mapped_sorted.bam
  11.     samtools index mapped_sorted.bam
  12.     samtools view -H mapped_sorted.bam
  13.     samtools flagstat mapped_sorted.bam
  15.     #14634302 + 0 in total (QC-passed reads + QC-failed reads)
  16.     #0 + 0 secondary
  17.     #0 + 0 supplementary
  18.     #0 + 0 duplicates
  19.     #177416 + 0 mapped (1.21% : N/A)
  20.     #11014010 + 0 paired in sequencing
  21.     #5507005 + 0 read1
  22.     #5507005 + 0 read2
  23.     #118328 + 0 properly paired (1.07% : N/A)
  24.     #123066 + 0 with itself and mate mapped
  25.     #10632 + 0 singletons (0.10% : N/A)
  26.     #0 + 0 with mate mapped to a different chr
  27.     #0 + 0 with mate mapped to a different chr (mapQ>=5)
  29.     samtools depth -m 0 -a mapped_sorted.bam > coverage.txt
  30.     grep "CP095178" coverage.txt > CP095178_coverage.txt
  31.     grep "CP095179" coverage.txt > CP095179_coverage.txt
  33.             import pandas as pd
  34.             import matplotlib.pyplot as plt
  36.             # Load coverage data
  37.             df = pd.read_csv("CP095178_coverage.txt", sep="\t", header=None, names=["chr", "pos", "coverage"])
  39.             # Plot
  40.             plt.figure(figsize=(10,4))
  41.             plt.plot(df["pos"], df["coverage"], color="blue", linewidth=0.5)
  42.             plt.xlabel("Genomic Position")
  43.             plt.ylabel("Coverage Depth")
  44.             plt.title("BAM Coverage Plot")
  45.             plt.show()
  47.     RESULTS: say CP095178 does not exist, but CP095179 exists (see attached coverage plots)!

Kindly perform a comparative analysis on this data (DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2 against CP059040):

  1.     adeABadeIJ sample (knockout of adeA - H0N29_08675, adeB - H0N29_08680, adeI - H0N29_03550, and adeJ - H0N29_03545, please confirm whether these genes are successfully knocked out.)
  2.     adeIJK sample (knockout of adeI - H0N29_03550, adeJ - H0N29_03545, and adeK - H0N29_03540,, please confirm whether these genes are successfully knocked out.)
  4.     for sample in adeABadeIJ adeIJK CM1 CM2; do
  5.             makeblastdb -in ~/DATA/Data_Tam_DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2/shovill/${sample}/contigs.fa -dbtype nucl
  6.     done
  7.     for id in adeA adeB adeI adeJ adeK; do
  8.             echo "mkdir ${id}"
  9.             echo "for sample in adeABadeIJ adeIJK CM1 CM2; do"
  10.                     echo "blastn -db ~/DATA/Data_Tam_DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2/shovill/\${sample}/contigs.fa -query ${id}.fasta -evalue 1e-1 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > ./${id}/\${sample}.blastn"
  11.             echo "done"
  12.     done
  13.     cp mlst/all_mlst_may_be_wrong.txt typing.csv
  14.     python ~/Scripts/process_directory.py adeA typing.csv typing_until_adeA.csv
  15.     python ~/Scripts/process_directory.py adeB typing_until_adeA.csv typing_until_adeB.csv
  16.     python ~/Scripts/process_directory.py adeI typing_until_adeB.csv typing_until_adeI.csv
  17.     python ~/Scripts/process_directory.py adeJ typing_until_adeI.csv typing_until_adeJ.csv
  18.     python ~/Scripts/process_directory.py adeK typing_until_adeJ.csv typing_until_adeK.csv
  20.     #Note that the length of adeK is 1455 nt long. In the sample adeIJK sample, we can find the subsequence from the position 14 to 1455 of the genes in the contig00007 (position 44108-45549).

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