Interhost and structural variant calling, and plasmid presence/absence report (Data_Tam_DNAseq_2025_Y1Y2Y3Y4W1W2W3W4_Tig1_Tig2_dIJ_on_ATCC19606)

  1. Input data:

     mkdir bacto; cd bacto;
     mkdir raw_data; cd raw_data;
    
     # ── W1-4 and Y1-4 ──
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J001/01.RawData/W/W_1.fq.gz W1_R1.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J001/01.RawData/W/W_2.fq.gz W1_R2.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/W2/W2_1.fq.gz W2_R1.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/W2/W2_2.fq.gz W2_R2.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/W3/W3_1.fq.gz W3_R1.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/W3/W3_2.fq.gz W3_R2.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/W4/W4_1.fq.gz W4_R1.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/W4/W4_2.fq.gz W4_R2.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J001/01.RawData/Y/Y_1.fq.gz Y1_R1.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J001/01.RawData/Y/Y_2.fq.gz Y1_R2.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/Y2/Y2_1.fq.gz Y2_R1.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/Y2/Y2_2.fq.gz Y2_R2.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/Y3/Y3_1.fq.gz Y3_R1.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/Y3/Y3_2.fq.gz Y3_R2.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/Y4/Y4_1.fq.gz Y4_R1.fastq.gz
     ln -s ../../short/RSMD00304/X101SC24065637-Z01/X101SC24065637-Z01-J002/01.RawData/Y4/Y4_2.fq.gz Y4_R2.fastq.gz
  2. Call variant calling using snippy

     ln -s ~/Tools/bacto/db/ .;
     ln -s ~/Tools/bacto/envs/ .;
     ln -s ~/Tools/bacto/local/ .;
     cp ~/Tools/bacto/Snakefile .;
     cp ~/Tools/bacto/bacto-0.1.json .;
     cp ~/Tools/bacto/cluster.json .;
    
     #download CP059040.gb from GenBank
     #mv ~/Downloads/sequence\(2\).gb db/CP059040.gb
    
     mamba activate /home/jhuang/miniconda3/envs/bengal3_ac3
     (bengal3_ac3) jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2023_A6WT_A10CraA_A12AYE_A1917978$ which snakemake
     /home/jhuang/miniconda3/envs/bengal3_ac3/bin/snakemake
     (bengal3_ac3) jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2023_A6WT_A10CraA_A12AYE_A1917978$ snakemake -v
     4.0.0 --> CORRECT!
    
     #NOTE_1: modify bacto-0.1.json keeping only steps assembly, typing_mlst, possibly pangenome and variants_calling true; setting cpu=20 in all used steps.
         #setting the following in bacto-0.1.json
         "fastqc": false,
         "taxonomic_classifier": false,
         "assembly": true,
         "typing_ariba": false,
         "typing_mlst": true,
         "pangenome": true,
         "variants_calling": true,
         "phylogeny_fasttree": false,
         "phylogeny_raxml": false,
         "recombination": false, (due to gubbins-error set false)
    
         "prokka": {
           "genus": "Acinetobacter",
           "kingdom": "Bacteria",
           "species": "baumannii",
           "cpu": 10,
           "evalue": "1e-06",
           "other": ""
         },
    
         "mykrobe": {
           "species": "abaum"
         },
    
         "reference": "db/CP059040.gb"
    
     #NOTE_2: needs disk Titisee since the pipeline needs /media/jhuang/Titisee/GAMOLA2/TIGRfam_db/TIGRFAMs_15.0_HMM.LIB
     snakemake --printshellcmds
  3. 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
  4. Using spandx calling variants (almost the same results to the one from viral-ngs!)

     mamba deactivate
     mamba activate /home/jhuang/miniconda3/envs/spandx
    
     # PREPARE the inputs for the options ref and database
     mkdir ~/miniconda3/envs/spandx/share/snpeff-5.1-2/data/PP810610
     cp PP810610.gb  ~/miniconda3/envs/spandx/share/snpeff-5.1-2/data/PP810610/genes.gbk
     vim ~/miniconda3/envs/spandx/share/snpeff-5.1-2/snpEff.config
     /home/jhuang/miniconda3/envs/spandx/bin/snpEff build PP810610    #-d
     ~/Scripts/genbank2fasta.py PP810610.gb
     mv PP810610.gb_converted.fna PP810610.fasta    #rename "NC_001348.1 xxxxx" to "NC_001348" in the fasta-file
    
     ln -s /home/jhuang/Tools/spandx/ spandx
     (spandx) nextflow run spandx/main.nf --fastq "trimmed/*_P_{1,2}.fastq" --ref CP059040.fasta --annotation --database CP059040 -resume
    
     # RERUN SNP_matrix.sh due to the error ERROR_CHROMOSOME_NOT_FOUND in the variants annotation, resulting in all impacts are MODIFIER --> IT WORKS!
     cd Outputs/Master_vcf
     conda activate /home/jhuang/miniconda3/envs/spandx
     (spandx) cp -r ../../snippy/Y1/reference . # Eigentlich irgendein directory, all directories contains the same reference.
     (spandx) cp ../../spandx/bin/SNP_matrix.sh ./
     #Note that ${variant_genome_path}=CP059040 in the following command, but it was not used after the following command modification.
     #Adapt "snpEff eff -no-downstream -no-intergenic -ud 100 -formatEff -v ${variant_genome_path} out.vcf > out.annotated.vcf" 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
     (spandx) bash SNP_matrix.sh CP059040 .
  5. Calling inter-host variants by merging the results from snippy+spandx

    Cross-Caller SNP/Indel Concordance & Invariant Variant Analyzer; Multi-Isolate Variant Intersection, Annotation Harmonization & Caller Discrepancy Report; Comparative Genomic Variant Profiling: Concordance, Invariance & Allele Mismatch Analysis; VarMatch: Cross-Tool Variant Concordance Pipeline

     mamba activate plot-numpy1
     cd bacto
     cp Outputs/Master_vcf/All_SNPs_indels_annotated.txt .
     cp snippy/summary_snps_indels.csv .
    
     cp ~/Scripts/process_variants_snippy_alleles_spandx_annotations.py .
    
     #Configuring
     ISOLATES = [
             "Y1", "Y2", "Y3", "Y4", "W1", "W2", "W3", "W4"
             ]
    
     (plot-numpy1) python process_variants_snippy_alleles_spandx_annotations.py
    
     # mv common_variants_all_snippy_annotated.xlsx common_variants_snippy+spandx_annotated_Y1Y2Y3Y4W1W2W3W4.xlsx
     # mv common_variants_invariant_snippy_annotated.xlsx common_invariants_snippy+spandx_annotated_Y1Y2Y3Y4W1W2W3W4.xlsx
  6. Manully checking each of the 6 records by comparing them to the results from SPANDx; three are confirmed!

     #CHROM,POS,REF,ALT,TYPE,Y1,Y2,Y3,Y4,W1,W2,W3,W4,Effect,Impact,Functional_Class,Codon_change,Protein_and_nucleotide_change,Amino_Acid_Length,Gene_name,Biotype
    
     # -- Results from snippy --
     #move: CP059040,1527276,TTGAACC,T,del,TTGAACC,TTGAACC,TTGAACC,T,TTGAACC,TTGAACC,T,T,conservative_inframe_deletion,MODERATE,,gaacct/,p.Glu443_Pro444del/c.1327_1332delGAACCT,704,H0N29_07175,protein_coding
     #confirmed: CP059040,1843289,G,T,snp,G,T,G,G,G,G,G,G,missense_variant,MODERATE,MISSENSE,gCg/gAg,p.Ala37Glu/c.110C>A,357,H0N29_08665,protein_coding
     #confirmed: CP059040,2019186,G,A,snp,A,G,G,G,G,G,G,G,upstream_gene_variant,MODIFIER,,59,c.-59C>T,144,H0N29_09480,protein_coding
     #delete_this? CP059040,3124917,T,"TAC,TACTTCATTACATACCAACCGCCAAGGGTGC",snp,C,T,C,C,T,T,T,C,upstream_gene_variant,MODIFIER,,25,c.-25_-24insAC,0,H0N29_00075,protein_coding
     #move: CP059040,3310021,C,CT,ins,CT,CT,CT,CT,C,CT,CT,CT,intragenic_variant,MODIFIER,,,n.3310021_3310022insT,,H0N29_00075,
     #confirmed: CP059040,3853714,G,A,snp,G,G,G,G,G,A,G,A,stop_gained,HIGH,NONSENSE,Cag/Tag,p.Gln91*/c.271C>T,338,H0N29_18290,protein_coding
     #--> Only three SNPs are confirmed --> means there is almost no variation in the genomic level!
    
     # -- Results from the SPANDx --
     #CP059040   1527276 TTGAACC T   INDEL   TTGAACC/T   T   T   T   T   T   T   T   conservative_inframe_deletion   MODERATE        gaacct/ p.Glu443_Pro444del/c.1327_1332delGAACCT 704 H0N29_07175 protein_coding
    
     #CP059040   1843289 G   T   SNP G   T   G   G   G   G   G   G   missense_variant    MODERATE    MISSENSE    gCg/gAg p.Ala37Glu/c.110C>A 357 H0N29_08665 protein_coding
     #CP059040   2019186 G   A   SNP A   G   G   G   G   G   G   G   upstream_gene_variant   MODIFIER        59  c.-59C>T    144 H0N29_09480 protein_coding
    
     #Cmp to CP059040    3124917 T   TAC,TACTTCATTACATACCAACCGCCAAGGGTGC INDEL   .   TACTTCATTACATACCAACCGCCAAGGGTGC TACTTCATTACATACCAACCGCCAAGGGTGC TAC .   .   .   .   upstream_gene_variant   MODIFIER        25  c.-25_-24insAC  0   H0N29_00075 protein_coding
     #Cmp to CP059040    3124920 C   CATTACATACCAACCGCCAAGGGTGCTTCATG    INDEL   .   .   .   CATTACATACCAACCGCCAAGGGTGCTTCATG    .   .   C   .   upstream_gene_variant   MODIFIER        22  c.-22_-21insATTACATACCAACCGCCAAGGGTGCTTCATG 0   H0N29_00075 protein_coding
    
     #TODO: Move to invariant-file: CP059040 3310021 C   CT  INDEL   CT  CT  CT  CT  CT  CT  CT  CT  intragenic_variant  MODIFIER            n.3310021_3310022insT       H0N29_00075
     #CP059040   3853714 G   A   SNP G   G   G   G   G   A   G   A   stop_gained HIGH    NONSENSE    Cag/Tag p.Gln91*/c.271C>T   338 H0N29_18290 protein_coding
    
     #-->For the Indel-report, more complicated, needs the following command to find the initial change and related codon-change.
     ## Check gene strand in your GFF/GenBank
     #grep "H0N29_07175" reference.gff
     # Extract 20 bp around the variant from reference
     samtools faidx CP059040.fasta CP059040:1527260-1527290
  7. Annotation of the three confirmed SNPs

     gene            complement(3852968..3853984)
                     /gene="galE"
                     /locus_tag="H0N29_18290"
     CDS             complement(3852968..3853984)
                     /gene="galE"
                     /locus_tag="H0N29_18290"
                     /EC_number="5.1.3.2"
                     /inference="COORDINATES: similar to AA
                     sequence:RefSeq:WP_017725586.1"
                     /note="Derived by automated computational analysis using
                     gene prediction method: Protein Homology."
                     /codon_start=1
                     /transl_table=11
                     /product="UDP-glucose 4-epimerase GalE"
                     /protein_id="QNT84923.1"
                     /translation="MAKILVTGGAGYIGSHTCVELLEAGHEVIVFDNLSNSSKESLNR
                     VQEITQKGLTFVEGDIRNSGELDRVFQEHAIDAVIHFAGLKAVGESQEKPLIYFDNNI
                     AGSIQLVKSMEKAGVYTLVFSSSATVYDEANTSPLNEEMPTGMPSNNYGYTKLIVEQL
                     LQKLSVADSKWSIALLRYFNPVGAHKSGRIGEDPQGIPNNLMPYVTQVAVGRREKLSI
                     YGNDYDTIDGTGVRDYIHVVDLANAHLCALNNRLQAQGCRAWNIGTGNGSSVLQVKNT
                     FEQVNGVPVAFEFAPRRAGDVATSFADNARAVAELGWKPQYGLEDMLKDSWNWQKQNP
                     NGYN"
    
     gene            complement(1842325..1843398)
                     /gene="adeS"
                     /locus_tag="H0N29_08665"
     CDS             complement(1842325..1843398)
                     /gene="adeS"
                     /locus_tag="H0N29_08665"
                     /inference="COORDINATES: similar to AA
                     sequence:RefSeq:WP_000837467.1"
                     /note="Derived by automated computational analysis using
                     gene prediction method: Protein Homology."
                     /codon_start=1
                     /transl_table=11
                     /product="two-component sensor histidine kinase AdeS"
                     /protein_id="QNT86623.1"
                     /translation="MKSKLGISKQLFIALTIVNLSVTLFSVVLGYVIYNYAIEKGWIS
                     LSSFQQEDWTSFHFVDWIWLATVIFCGCIISLVIGMRLAKRFIVPINFLAEAAKKISH
                     GDLSARAYDNRIHSAEMSELLYNFNDMAQKLEVSVKNAQVWNAAIAHELRAPITILQG
                     RLQGIIDGVFKPDEVLFKSLLNQVEGLSHLVEDLRTLSLVENQQLRLNYELFDLKAVV
                     EKVLKAFEDRLDQAKLVPELDLTSTPVYCDRRRIEQVLIALIDNSIRYSNAGKLKISS
                     EVVADNWILKIEDEGPGIATEFQDDLFKPFFRLEESRNKEFGGTGLGLAVVHAIVVAL
                     KGTIQYSNQGSKSVFTIKISMNN"
    
     gene            complement(2018693..2019127)
                     /locus_tag="H0N29_09480"
     CDS             complement(2018693..2019127)
                     /locus_tag="H0N29_09480"
                     /inference="COORDINATES: similar to AA
                     sequence:RefSeq:YP_004995263.1"
                     /note="Derived by automated computational analysis using
                     gene prediction method: Protein Homology."
                     /codon_start=1
                     /transl_table=11
                     /product="HIT domain-containing protein"
                     /protein_id="QNT83319.1"
                     /translation="MFSLHPQLAQDTFFVGDFPLSTCRLMNDMQFPWLILVPRVPGIT
                     ELYELSQADQEQFLRESSWLSSQLSRVFRADKMNVAALGNMVPQLHFHHVVRYQNDVA
                     WPKPVWGTPAVPYTSDVLAHMRQTLMLALRGQGDMPFDWRMD"
  8. Structural Variant Detection

     conda activate sv_assembly
    
     # Step 1: Align assemblies to reference
     nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta ./Y1_unicycler_out/assembly.fasta -p Y1
     # Step 2: Filter alignments (1-to-1 best matches)
     delta-filter -1 -q Y1.delta > Y1.filtered.delta
     # Note: Use -1 for 1-to-1, not -r -q
     # Step 3: Run Assemblytics with ALL 5 parameters
     Assemblytics Y1.filtered.delta Y1_assemblytics 1000 100 50000
     # Step 5: Extract large insertions only
     grep -w "Insertion" Y1_assemblytics.Assemblytics_structural_variants.bed > Y1_insertions.bed
     # 6. Check if ANY variants were detected (any size)
     wc -l Y1_assemblytics.Assemblytics_structural_variants.bed
     # 7. View variant type distribution
     cut -f4 Y1_assemblytics.Assemblytics_structural_variants.bed | sort | uniq -c
     # 8. Check alignment coverage (are contigs aligning well?)
     cat Y1_assemblytics.Assemblytics_assembly_stats.txt
     # 9. Check raw delta file for alignment blocks
     show-coords -rcl Y1.filtered.delta | head -20
     # 10. If bed file is empty, try relaxing parameters and re-run:
     Assemblytics Y1.filtered.delta Y1_assemblytics_v2 500 50 100000
     #                          └─unique─┘ └min┘ └──max──┘
    
     nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta ./Y1_unicycler_out/assembly.fasta -p Y1
     delta-filter -1 -q Y1.delta > Y1.filtered.delta
     Assemblytics Y1.filtered.delta Y1_assemblytics 1000 100 50000
     vim Y1_assemblytics.variant_preview.txt
    
     #reference  ref_start  ref_stop  ID                size  strand  type                ref_gap_size  query_gap_size  query_coordinates    method
     CP059040    737224     741667    Assemblytics_b_2  4436  +       Deletion            4443          7               1:737272-737279:+    between_alignments
     CP059040    3124916    3125037   Assemblytics_b_5  198   +       Tandem_contraction  121           -77             1:3067782-3067859:-  between_alignments
    
     nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta ./Y2_unicycler_out/assembly.fasta -p Y2
     delta-filter -1 -q Y2.delta > Y2.filtered.delta
     Assemblytics Y2.filtered.delta Y2_assemblytics 1000 100 50000
    
     #reference  ref_start  ref_stop  ID                size  strand  type                ref_gap_size  query_gap_size  query_coordinates    method
     CP059040    737224     741667    Assemblytics_b_2  4436  +       Deletion            4443          7               1:737272-737279:+    between_alignments
     CP059040    3124916    3125037   Assemblytics_b_5  198   +       Tandem_contraction  121           -77             1:3067782-3067859:-  between_alignments
    
     nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta ./Y3_unicycler_out/assembly.fasta -p Y3
     delta-filter -1 -q Y3.delta > Y3.filtered.delta
     Assemblytics Y3.filtered.delta Y3_assemblytics 1000 100 50000
     #reference  ref_start  ref_stop  ID                size  strand  type                ref_gap_size  query_gap_size  query_coordinates    method
     CP059040    737224     741667    Assemblytics_b_2  4436  +       Deletion            4443          7               1:737281-737288:+    between_alignments
     CP059040    3124916    3125037   Assemblytics_b_5  198   +       Tandem_contraction  121           -77             1:3067791-3067868:-  between_alignments
     CP059040    3439419    3439424   Assemblytics_b_6  1106  +       Insertion           -5            1101            1:3382176-3383277:+  between_alignments
    
     nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta ./Y4_unicycler_out/assembly.fasta -p Y4
     delta-filter -1 -q Y4.delta > Y4.filtered.delta
     Assemblytics Y4.filtered.delta Y4_assemblytics 1000 100 50000
     #reference  ref_start  ref_stop  ID                size  strand  type                ref_gap_size  query_gap_size  query_coordinates                        method
     CP059040    737224     741667    Assemblytics_b_2  4436  +       Deletion            4443          7               cluster_001_consensus:737272-737279:+    between_alignments
     CP059040    3124916    3125037   Assemblytics_b_5  198   +       Tandem_contraction  121           -77             cluster_001_consensus:3067776-3067853:-  between_alignments
    
     nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta ./W1_unicycler_out/assembly.fasta -p W1
     delta-filter -1 -q W1.delta > W1.filtered.delta
     Assemblytics W1.filtered.delta W1_assemblytics 1000 100 50000
    
     #reference  ref_start  ref_stop  ID                size  strand  type                ref_gap_size  query_gap_size  query_coordinates    method
     CP059040    737224     741667    Assemblytics_b_2  4436  +       Deletion            4443          7               1:737272-737279:+    between_alignments
     CP059040    3124916    3125037   Assemblytics_b_5  198   +       Tandem_contraction  121           -77             1:3067782-3067859:-  between_alignments
     CP059040    3853883    3853888   Assemblytics_b_6  1106  +       Insertion           -5            1101            1:3796629-3797730:+  between_alignments
    
     nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta ./W2_unicycler_out/assembly.fasta -p W2
     delta-filter -1 -q W2.delta > W2.filtered.delta
     Assemblytics W2.filtered.delta W2_assemblytics 1000 100 50000
    
     #reference  ref_start  ref_stop  ID                size  strand  type                ref_gap_size  query_gap_size  query_coordinates    method
     CP059040    737224     741667    Assemblytics_b_2  4436  +       Deletion            4443          7               1:737272-737279:+    between_alignments
     CP059040    3124916    3125037   Assemblytics_b_5  198   +       Tandem_contraction  121           -77             1:3067782-3067859:-  between_alignments
    
     nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta ./W3_unicycler_out/assembly.fasta -p W3
     delta-filter -1 -q W3.delta > W3.filtered.delta
     Assemblytics W3.filtered.delta W3_assemblytics 1000 100 50000
    
     #reference  ref_start  ref_stop  ID                size  strand  type                ref_gap_size  query_gap_size  query_coordinates    method
     CP059040    737224     741667    Assemblytics_b_2  4436  +       Deletion            4443          7               1:737272-737279:+    between_alignments
     CP059040    3124916    3125037   Assemblytics_b_5  198   +       Tandem_contraction  121           -77             1:3067782-3067859:-  between_alignments
     CP059040    3853884    3853890   Assemblytics_b_6  1106  +       Insertion           -6            1100            1:3796631-3797731:+  between_alignments
    
     nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta ./W4_unicycler_out/assembly.fasta -p W4
     delta-filter -1 -q W4.delta > W4.filtered.delta
     Assemblytics W4.filtered.delta W4_assemblytics 1000 100 50000
    
     #reference  ref_start  ref_stop  ID                size  strand  type                ref_gap_size  query_gap_size  query_coordinates    method
     CP059040    737224     741667    Assemblytics_b_2  4436  +       Deletion            4443          7               1:737272-737279:+    between_alignments
     CP059040    3124916    3125037   Assemblytics_b_5  198   +       Tandem_contraction  121           -77             1:3067782-3067859:-  between_alignments
    
     samtools faidx  ./W1_unicycler_out/assembly.fasta 1:3796629-3797730
     samtools faidx  ./W3_unicycler_out/assembly.fasta 1:3796631-3797731
    
     gene            3124675..3124749
                     /locus_tag="H0N29_14850"
     tRNA            3124675..3124749
                     /locus_tag="H0N29_14850"
                     /product="tRNA-Gln"
                     /inference="COORDINATES: profile:tRNAscan-SE:2.0.4"
                     /note="Derived by automated computational analysis using
                     gene prediction method: tRNAscan-SE."
                     /anticodon=(pos:3124707..3124709,aa:Gln,seq:ttg)
     gene            3124841..3124915
                     /locus_tag="H0N29_14855"
     tRNA            3124841..3124915
                     /locus_tag="H0N29_14855"
                     /product="tRNA-Gln"
                     /inference="COORDINATES: profile:tRNAscan-SE:2.0.4"
                     /note="Derived by automated computational analysis using
                     gene prediction method: tRNAscan-SE."
                     /anticodon=(pos:3124873..3124875,aa:Gln,seq:ttg)
     gene            3124943..3125017
                     /locus_tag="H0N29_14860"
     tRNA            3124943..3125017
                     /locus_tag="H0N29_14860"
                     /product="tRNA-Gln"
                     /inference="COORDINATES: profile:tRNAscan-SE:2.0.4"
                     /note="Derived by automated computational analysis using
                     gene prediction method: tRNAscan-SE."
                     /anticodon=(pos:3124975..3124977,aa:Gln,seq:ttg)
     gene            3125039..3125113
                     /locus_tag="H0N29_14865"
     tRNA            3125039..3125113
                     /locus_tag="H0N29_14865"
                     /product="tRNA-Gln"
                     /inference="COORDINATES: profile:tRNAscan-SE:2.0.4"
                     /note="Derived by automated computational analysis using
                     gene prediction method: tRNAscan-SE."
                     /anticodon=(pos:3125071..3125073,aa:Gln,seq:ttg)
    
     #--
    
     gene            complement(735319..735669)
                     /locus_tag="H0N29_03535"
     CDS             complement(735319..735669)
                     /locus_tag="H0N29_03535"
                     /inference="COORDINATES: similar to AA
                     sequence:RefSeq:YP_004996456.1"
                     /note="Derived by automated computational analysis using
                     gene prediction method: Protein Homology."
                     /codon_start=1
                     /transl_table=11
                     /product="YbjQ family protein"
                     /protein_id="QNT85684.1"
                     /db_xref="GI:1906909114"
                     /translation="MLLSNLESVPGHQILKQLDVVYGSTVRSKHVGRDLMASLKNIVG
                     GELTGYTELLEESRQEAMQRMIAKAEQLGANAIVGIRFSTSNIAQGASELFVYGTAVV
                     VQPQAPHLPDPFNA"
     gene            complement(735779..737233)
                     /gene="adeK"
                     /locus_tag="H0N29_03540"
     CDS             complement(735779..737233)
                     /gene="adeK"
                     /locus_tag="H0N29_03540"
                     /inference="COORDINATES: similar to AA
                     sequence:RefSeq:YP_004996455.1"
                     /note="Derived by automated computational analysis using
                     gene prediction method: Protein Homology."
                     /codon_start=1
                     /transl_table=11
                     /product="multidrug efflux RND transporter outer membrane
                     channel subunit AdeK"
                     /protein_id="QNT86781.1"
                     /db_xref="GI:1906910211"
                     /translation="MQKVWSISGRSIAVSALALALAACQSMRGPEPVVKTDIPQSYAY
                     NSASGTSIAEQGYKQFFADPRLLEVIDLALANNRDLRTATLNIERAQQQYQITQNNQL
                     PTIGASGSAIRQVSQSRDPNNPYSTYQVGLGVTAYELDFWGRVRSLKDAALDSYLATQ
                     SARDSTQISLISQVAQAWLNYSFATANLRLAEQTLKAQLDSYNLNKKRFDVGIDSEVP
                     LRQAQISVETARNDVANYKTQIAQAQNLLNLLVGQPVPQNLLPTQPVKRIAQQNVFTA
                     GLPSDLLNNRPDVKAAEYNLSAAGANIGAAKARLFPTISLTGSAGYASTDLSDLFKSG
                     GFVWSVGPSLDLPIFDWGTRRANVKISETDQKIALSDYEKSVQSAFREVNDALATRAN
                     IGERLTAQQRLVEATNRNYTLSNARFRAGIDSYLTVLDAQRSSYAAEQGLLLLQQANL
                     NNQIELYKTLGGGLKANTSDTVVHQPSSAELKKQ"
     gene            complement(737233..740409)
                     /gene="adeJ"
                     /locus_tag="H0N29_03545"
     CDS             complement(737233..740409)
                     /gene="adeJ"
                     /locus_tag="H0N29_03545"
                     /inference="COORDINATES: similar to AA
                     sequence:RefSeq:WP_116497130.1"
                     /note="Derived by automated computational analysis using
                     gene prediction method: Protein Homology."
                     /codon_start=1
                     /transl_table=11
                     /product="multidrug efflux RND transporter permease
                     subunit AdeJ"
                     /protein_id="QNT85685.1"
                     /db_xref="GI:1906909115"
                     /translation="MAQFFIHRPIFAWVIALVIMLAGILTLTKMPIAQYPTIAPPTVT
                     IAATYPGASAETVENTVTQIIEQQMNGLDGLRYISSNSAGNGQASIQLNFEQGVDPDI
                     AQVQVQNKLQSATALLPEDVQRQGVTVTKSGASFLQVIAFYSPDNNLSDSDIKDYVNS
                     SIKEPLSRVAGVGEVQVFGGSYAMRIWLDPAKLTSYQLTPSDIATALQAQNSQVAVGQ
                     LGGAPAVQGQVLNATVNAQSLLQTPEQFKNIFLKNTASGAEVRLKDVARVELGSDNYQ
                     FDSKFNGKPAAGLAIKIATGANALDTAEAVEQRLSELRKNYPTGLADKLAYDTTPFIR
                     LSIESVVHTLIEAVILVFIVMFLFLQNWRATIIPTLAVPVVVLGTFAVINIFGFSINT
                     LTMFAMVLAIGLLVDDAIVVVENVERVMSEDHTDPVTATSRSMQQISGALVGITSVLT
                     AVFVPMAFFGGTTGVIYRQFSITLVTAMVLSLIVALTFTPALCATILKQHDPNKEPSN
                     NIFARFFRSFNNGFDRMSHSYQNGVSRMLKGKIFSGVLYAVVVALLVFLFQKLPSSFL
                     PEEDQGVVMTLVQLPPNATLDRTGKVIDTMTNFFMNEKDTVESIFTVSGFSFTGVGQN
                     AGIGFVKLKDWSKRTTPETQIGSLIQRGMALNMIIKDASYVMPLQLPAMPELGVTAGF
                     NLQLKDSSGQGHEKLIAARNTILGLASQDKRLVGVRPNGQEDTPQYQINVDQAQAGAM
                     GVSIAEINNTMRIAWGGSYINDFVDRGRVKKVYVQGDAGSRMMPEDLNKWYVRNNKGE
                     MVPFSAFATGEWTYGSPRLERYNGVSSVNIQGTPAPGVSSGDAMKAMEEIIGKLPSMG
                     LQGFDYEWTGLSLEERESGAQAPFLYALSLLIVFLCLAALYESWSIPFSVLLVVPLGV
                     IGAIVLTYLGMIIKGDPNLSNNIYFQVAIIAVIGLSAKNAILIVEFAKELQEKGEDLL
                     DATLHAAKMRLRPIIMTTLAFGFGVLPLALSTGAGAGSQHSVGFGVLGGVLSATFLGI
                     FFIPVFYVWIRSIFKYKPKTINTQEHKS"
     gene            complement(740422..741672)
                     /gene="adeI"
                     /locus_tag="H0N29_03550"
     CDS             complement(740422..741672)
                     /gene="adeI"
                     /locus_tag="H0N29_03550"
                     /inference="COORDINATES: similar to AA
                     sequence:RefSeq:YP_004996452.1"
                     /note="Derived by automated computational analysis using
                     gene prediction method: Protein Homology."
                     /codon_start=1
                     /transl_table=11
                     /product="multidrug efflux RND transporter periplasmic
                     adaptor subunit AdeI"
                     /protein_id="QNT85686.1"
                     /db_xref="GI:1906909116"
                     /translation="MMSAKLWAPALTACALATSIALVGCSKGSDEKQQAAAAQKMPPA
                     EVGVIVAQPQSVEQSVELSGRTSAYQISEVRPQTSGVILKRLFAEGSYVREGQALYEL
                     DSRTNRATLENAKASLLQQQANLASLRTKLNRYKQLVSSNAVSKQEYDDLLGQVNVAE
                     AQVAAAKAQVTNANVDLGYSTIRSPISGQSGRSSVTAGALVTANQTDPLVTIQQLDPI
                     YVDINQSSAELLRLRQQLSKGSLNNSNNTKVKLKLEDGSTYPIEGQLAFSDASVNQDT
                     GTITLRAVFSNPNHLLLPGMYTTAQIVQGVVPNAYLIPQAAITRLPTGQAVAMLVNAK
                     GVVESRPVETSGVQGQNWIVTNGLKAGDKVIVDGVAKVKEGQEVSAKPYQAQPANSQG
                     AAPNAAKPAQSGKPQAEQKAASNA"
     gene            complement(741697..742323)
                     /locus_tag="H0N29_03555"
     CDS             complement(741697..742323)
                     /locus_tag="H0N29_03555"
                     /inference="COORDINATES: similar to AA
                     sequence:RefSeq:YP_004996451.1"
                     /note="Derived by automated computational analysis using
                     gene prediction method: Protein Homology."
                     /codon_start=1
                     /transl_table=11
                     /product="phosphatase PAP2 family protein"
                     /protein_id="QNT85687.1"
                     /db_xref="GI:1906909117"
                     /translation="MPYLLLCIGCVFLGLGVLGLFVPSLQSLDLLTVQTLSHHRLDYL
                     NTITTFLARVGGMPFVCFLSFLVCIYLAWYKKYITVIFISLGVIGSITMGWLLKWCVN
                     RPRPPEAYHIVESYGASFPSAHSVYASTLACLAMIMLCHKHNINSPYIVLISCLWFVC
                     MGLSRIYAGVHFPTDVLAGWGIGFIWIALLWLWLLQTQSRLSRKQIYF"
    
     #--
    
     gene            complement(3852968..3853984)
                     /gene="galE"
                     /locus_tag="H0N29_18290"
     CDS             complement(3852968..3853984)
                     /gene="galE"
                     /locus_tag="H0N29_18290"
                     /EC_number="5.1.3.2"
                     /inference="COORDINATES: similar to AA
                     sequence:RefSeq:WP_017725586.1"
                     /note="Derived by automated computational analysis using
                     gene prediction method: Protein Homology."
                     /codon_start=1
                     /transl_table=11
                     /product="UDP-glucose 4-epimerase GalE"
                     /protein_id="QNT84923.1"
                     /db_xref="GI:1906908353"
                     /translation="MAKILVTGGAGYIGSHTCVELLEAGHEVIVFDNLSNSSKESLNR
                     VQEITQKGLTFVEGDIRNSGELDRVFQEHAIDAVIHFAGLKAVGESQEKPLIYFDNNI
                     AGSIQLVKSMEKAGVYTLVFSSSATVYDEANTSPLNEEMPTGMPSNNYGYTKLIVEQL
                     LQKLSVADSKWSIALLRYFNPVGAHKSGRIGEDPQGIPNNLMPYVTQVAVGRREKLSI
                     YGNDYDTIDGTGVRDYIHVVDLANAHLCALNNRLQAQGCRAWNIGTGNGSSVLQVKNT
                     FEQVNGVPVAFEFAPRRAGDVATSFADNARAVAELGWKPQYGLEDMLKDSWNWQKQNP
                     NGYN"
  9. Reporting plasmid of the 8 samples

     grep ">" ./Y1_unicycler_out/assembly.fasta
     grep ">" ./Y2_unicycler_out/assembly.fasta
     grep ">" ./Y3_unicycler_out/assembly.fasta
     grep ">" ./Y4_unicycler_out/assembly.fasta
     grep ">" ./W1_unicycler_out/assembly.fasta
     grep ">" ./W2_unicycler_out/assembly.fasta
     grep ">" ./W3_unicycler_out/assembly.fasta
     grep ">" ./W4_unicycler_out/assembly.fasta
    
     >1 length=3923593 depth=1.00x circular=true
     >2 length=17195 depth=32.32x circular=true
    
     >1 length=3923593 depth=1.00x circular=true
     >2 length=9540 depth=41.67x circular=true
     >3 length=7655 depth=35.32x circular=true
    
     >1 length=3924708 depth=1.00x circular=true
     >2 length=7655 depth=33.84x circular=true
    
     >cluster_001_consensus  3923587 23      3923587 3923588
     >cluster_004_consensus  9538    3923634 9538    9539
     >cluster_008_consensus  7655    3933196 7655    7656
    
     >1 length=3924699 depth=1.00x circular=true
     >2 length=17195 depth=39.78x circular=true
    
     >1 length=3923593 depth=1.00x circular=true
     >2 length=24850 depth=31.68x circular=true
    
     >1 length=3924699 depth=1.00x circular=true
     >2 length=16750 depth=43.47x circular=true
    
     >1 length=3923593 depth=1.00x circular=true
     >2 length=9540 depth=43.37x circular=true
     >3 length=7655 depth=33.45x circular=true
    
     samtools faidx Y1_unicycler_out/assembly.fasta 2 >> plasmids/Y1_plasmids.fasta
     samtools faidx Y2_unicycler_out/assembly.fasta 2 >> plasmids/Y2_plasmids.fasta
     samtools faidx Y2_unicycler_out/assembly.fasta 3 >> plasmids/Y2_plasmids.fasta
     samtools faidx Y3_unicycler_out/assembly.fasta 2 >> plasmids/Y3_plasmids.fasta
     samtools faidx Y4_unicycler_out/assembly.fasta cluster_004_consensus >> plasmids/Y4_plasmids.fasta
     samtools faidx Y4_unicycler_out/assembly.fasta cluster_008_consensus >> plasmids/Y4_plasmids.fasta
     samtools faidx W1_unicycler_out/assembly.fasta 2 >> plasmids/W1_plasmids.fasta
     samtools faidx W2_unicycler_out/assembly.fasta 2 >> plasmids/W2_plasmids.fasta
     samtools faidx W3_unicycler_out/assembly.fasta 2 >> plasmids/W3_plasmids.fasta
     samtools faidx W4_unicycler_out/assembly.fasta 2 >> plasmids/W4_plasmids.fasta
     samtools faidx W4_unicycler_out/assembly.fasta 3 >> plasmids/W4_plasmids.fasta
    
     cd plasmids
     mash sketch -i *.fasta -o all_plasmids.msh
     mash dist all_plasmids.msh all_plasmids.msh > mash_distances.txt
    
     asmid,Length,Fusion Score,Verdict
     Y1_17195nt,"17,195 bp",1.052,✅ True p1+p2 fusion
     W1_17195nt,"17,195 bp",1.052,✅ True p1+p2 fusion (identical to Y1)
     W2_24850nt,"24,850 bp",1.052,🔶 p1+p2 fusion + ~7.6 kb cargo/duplication
     W3_16750nt,"16,750 bp",0.952,❌ p1-like variant", not a fusion" [deletion of 445 bp]
    
     #How to Check if p2-Like Sequences Are Integrated in Y3's Chromosome
     # If Y3 chromosome shares <100 hashes with p2, it's absent
     mash dist <(mash sketch -o y3_chr Y3_unicycler_out/assembly.fasta) plasmids/p2ATCC19606.fasta | awk '$4 < 100 {print "p2 ABSENT in Y3 chromosome"}'
    
     # Extract contig "1" (the ~3.9 Mb chromosome) from Y3 assembly
     samtools faidx Y3_unicycler_out/assembly.fasta "1" > Y3_chromosome.fasta
    
     # If you haven't sketched p2 yet:
     mash sketch plasmids/p2ATCC19606.fasta -o p2_ref
    
     # Screen Y3 chromosome against p2 (much faster than dist)
     mash screen p2_ref.msh Y3_chromosome.fasta > y3_chr_vs_p2.screen
     cat y3_chr_vs_p2.screen
    
     # Extract shared hashes and check if < 100
     mash screen p2_ref.msh Y3_chromosome.fasta | awk -F'\t' '{
             split($5, a, "/");
             if (a[1] < 100) print "✅ p2 ABSENT in Y3 chromosome (only " a[1] "/1000 k-mers shared)";
             else if (a[1] > 800) print "🔴 p2 PRESENT in Y3 chromosome (" a[1] "/1000 k-mers shared)";
             else print "⚠️ Partial p2 homology in Y3 chromosome (" a[1] "/1000 k-mers shared)";
     }'
    
     # Make BLAST db from Y3 chromosome
     makeblastdb -in Y3_chromosome.fasta -dbtype nucl -out y3_chr_db
    
     # Search p2 against Y3 chromosome (sensitive settings)
     blastn -query plasmids/p2ATCC19606.fasta -db y3_chr_db \
     -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore" \
     -evalue 1e-10 -word_size 11 -max_target_seqs 5 -out y3_chr_vs_p2.blastn
    
     # Check results
     if [ -s y3_chr_vs_p2.blastn ]; then
     echo "🔍 Potential p2 integration found:"
     head -5 y3_chr_vs_p2.blastn | column -t
     else
     echo "✅ No significant p2 homology in Y3 chromosome"
     fi
    
     Plasmid Group,Samples,Verdict,Action
     ~7.6 kb,"Y2, Y3, Y4, W4",🟢 No difference,Report as conserved
     ~9.5 kb,"Y2, Y4, W4",🟢 No difference,Report as conserved
     ~17 kb,"Y1, W1, W3",🟡 Moderate difference,Align to identify indel
     24.8 kb,W2,🔴 Big difference,Report as unique plasmid
    
     Plasmid,Length,Fusion Score,Verdict
     Y1_17195nt,"17,195 bp",1.052,✅ True p1+p2 fusion
     W1_17195nt,"17,195 bp",1.052,✅ True p1+p2 fusion (identical to Y1)
     W2_24850nt,"24,850 bp",1.052,🔶 p1+p2 fusion + ~7.6 kb cargo/duplication
     W3_16750nt,"16,750 bp",0.952,❌ p1-like variant", not a fusion"
    
     But the W3_16750nt has two records mapping p1 and p2, I think they should contains all genes of p1 and p2.
    
     Acinetobacter baumannii strain ATCC 19606 plasmid p1ATCC19606, complete sequence
     Sequence ID: CP045108.1Length: 7655Number of Matches: 9
     Related Information
     Gene-associated gene details
     Range 1: 1 to 7655GenBankGraphicsNext MatchPrevious Match
     Alignment statistics for match #1
     Score   Expect  Identities  Gaps    Strand
     14098 bits(7634)    0.0 7649/7656(99%)  2/7656(0%)  Plus/Plus
    
     Acinetobacter baumannii strain ATCC 19606 plasmid p2ATCC19606, complete sequence
     Sequence ID: CP045109.1Length: 9540Number of Matches: 8
     Related Information
     Gene-associated gene details
     Range 1: *3304 to 8962GenBankGraphicsNext MatchPrevious Match
     Alignment statistics for match #1
     Score   Expect  Identities  Gaps    Strand
     10394 bits(5628)    0.0 5650/5659(99%)  8/5659(0%)  Plus/Plus
    
     Range 2: *1859 to 2978GenBankGraphicsNext MatchPrevious MatchFirst Match
     Alignment statistics for match #2
     Score   Expect  Identities  Gaps    Strand
     2036 bits(1102) 0.0 1115/1121(99%)  1/1121(0%)  Plus/Plus
    
     Range 4: *9281 to 9517GenBankGraphicsNext MatchPrevious MatchFirst Match
     Alignment statistics for match #4
     Score   Expect  Identities  Gaps    Strand
     263 bits(142)   9e-64   209/241(87%)    6/241(2%)   Plus/Plus
    
     Y3 genome composition:
     ├─ Chromosome (~3.9 Mb): ✅ NO p2 integration detected
     │  ├─ mash screen: empty (no genome-wide signal)
     │  └─ blastn: single 44 bp hit = background/conserved motif
     │
     ├─ Plasmid "3" (7,655 bp): 🟢 Identical to p1ATCC19606 (CP045108.1)
     │
     └─ p2-like plasmid: ❌ Completely absent (neither free nor integrated)
    
     Biological implication:
     Y3 carries ONLY the p1 lineage plasmid.
     The p2 lineage is entirely missing from this isolate.
    
     Only Y3 missing p2, report the detailed list of genes in the two plasmids, say Y3 is possibly missing p2, causing some phenotypical change, but tell my co-author should be very careful. Ask if the Y3 possibley missing p2 make sence? Write a comprehensive reports about the plasmid presence and absence?
  10. Corrected Workflow: Systematic of two plasmids p1/p2 (p1ATCC19606 & p2ATCC19606) Screening for All 8 Samples

🔬 Plasmid Distribution Across 8 Isolates

Sample Chromosome (~3.9 Mb) Plasmid 1 (~7.6 kb) Plasmid 2 (~9.5 kb) Structural Variant p2 Status
Y1 Present ❌ Absent ❌ Absent Y1_17195nt: True p1+p2 fusion (17,195 bp) Integrated
Y2 Present ✅ p1-like (7,655 bp) ✅ p2-like (9,540 bp) ❌ None Free plasmid
Y3 Present ✅ p1-like (7,655 bp) Completely Absent ❌ None Lost/Cured
Y4 Present ✅ p1-like (7,655 bp) ✅ p2-like (9,540 bp) cluster_004 (p2-like) + cluster_008 (p1-like) Free plasmid
W1 Present ❌ Absent ❌ Absent W1_17195nt: True p1+p2 fusion (identical to Y1) Integrated
W2 Present ❌ Absent ❌ Absent 🔶 W2_24850nt: p1+p2 fusion + ~7.6 kb cargo/duplication Integrated + expanded
W3 Present ❌ Absent ❌ Absent ⚠️ W3_16750nt: p1 backbone + truncated p2 cargo (lacks p2 repAci9) Partially acquired
W4 Present ✅ p1-like (7,655 bp) ✅ p2-like (9,540 bp) ❌ None Free plasmid
    #Step 0: Prepare References

    mkdir -p refs
    cp plasmids/p1ATCC19606.fasta refs/p1_ref.fasta
    cp plasmids/p2ATCC19606.fasta refs/p2_ref.fasta

    # Index both references
    bowtie2-build refs/p1_ref.fasta refs/p1_ref
    bowtie2-build refs/p2_ref.fasta refs/p2_ref

    #Step 1: Define Sample List

    samples=("Y1" "Y2" "Y3" "Y4" "W1" "W2" "W3" "W4")

    #Step 2: Map Reads to p1 and p2 (with proper filtering)
    for sample in "${samples[@]}"; do
            echo "=== Processing $sample ==="

            for plasmid in p1 p2; do
                    # Map with strict filters
                    bowtie2 -x refs/${plasmid}_ref \
                    -1 ./${sample}_R1.fastq.gz -2 ./${sample}_R2.fastq.gz \
                    -S ${sample}_vs_${plasmid}.sam \
                    -p 40 --very-sensitive --no-unal \
                    2> ${sample}_vs_${plasmid}.log

                    # Convert to BAM: keep only properly paired, MAPQ≥20, primary alignments
                    samtools view -b -F 4 -f 2 -q 20 -F 256 ${sample}_vs_${plasmid}.sam | \
                    samtools sort -o ${sample}_vs_${plasmid}.sorted.bam

                    samtools index ${sample}_vs_${plasmid}.sorted.bam

                    # Get per-base depth (include 0-coverage positions)
                    samtools depth -a ${sample}_vs_${plasmid}.sorted.bam > ${sample}_${plasmid}_depth.txt
            done
    done

    #Step 3: Calculate TRUE Coverage Statistics
    # Function to calculate coverage metrics
    calc_coverage() {
            local depth_file=$1
            local ref_length=$2
            local sample=$3
            local plasmid=$4

            # Count positions with depth >= X
            local cov_1x=$(awk -v len="$ref_length" '$3>=1 {count++} END {print count+0}' "$depth_file")
            local cov_10x=$(awk '$3>=10 {count++} END {print count+0}' "$depth_file")
            local cov_30x=$(awk '$3>=30 {count++} END {print count+0}' "$depth_file")

            # Calculate percentages
            local pct_1x=$(echo "scale=2; $cov_1x * 100 / $ref_length" | bc)
            local pct_10x=$(echo "scale=2; $cov_10x * 100 / $ref_length" | bc)
            local pct_30x=$(echo "scale=2; $cov_30x * 100 / $ref_length" | bc)

            # Get mean depth
            local mean_depth=$(awk '{sum+=$3; count++} END {if(count>0) printf "%.2f", sum/count; else print "0"}' "$depth_file")

            # Output
            echo "$sample,$plasmid,$pct_1x,$pct_10x,$pct_30x,$mean_depth"
    }

    # Generate summary table
    echo "Sample,Plasmid,%_covered_1x,%_covered_10x,%_covered_30x,Mean_depth" > plasmid_coverage_summary.csv

    for sample in "${samples[@]}"; do
    calc_coverage "${sample}_p1_depth.txt" 7655 "$sample" "p1" >> plasmid_coverage_summary.csv
    calc_coverage "${sample}_p2_depth.txt" 9540 "$sample" "p2" >> plasmid_coverage_summary.csv
    done

    # View results
    column -t -s',' plasmid_coverage_summary.csv

    #Step 4: Quick Visual Check (Spot Errors)
    # Check if depth files have expected format
    echo "=== Checking depth file format ==="
    head -5 Y3_p2_depth.txt

    # Expected output:
    # p2_ref  1   0
    # p2_ref  2   0
    # p2_ref  3   0
    # ...

    # If you see something like:
    # 1   0   p2_ref   ← wrong column order!
    # Then fix with:
    awk '{print $3, $2, $1}' Y3_p2_depth.txt > Y3_p2_depth_fixed.txt

    #Step 5: Diagnose Unexpected High Coverage in Y3
    # 1. Check how many reads actually mapped (with quality filters)
    echo "=== Y3 p2 mapping stats ==="
    samtools flagstat Y3_vs_p2.sorted.bam

    # 2. Extract mapped reads and BLAST them to verify identity
    samtools fastq -1 Y3_p2_R1.fq -2 Y3_p2_R2.fq -0 /dev/null -s /dev/null -n Y3_vs_p2.sorted.bam

    # 3. BLAST a subset against nt to confirm they're truly p2-like
    head -40 Y3_p2_R1.fq > Y3_p2_sample.fq
    blastn -query Y3_p2_sample.fq -db nt -outfmt "6 qseqid sseqid pident length evalue stitle" -max_target_seqs 5 -evalue 1e-10 | head -10

    # 4. Check if mapped reads are multi-mappers (low MAPQ)
    samtools view Y3_vs_p2.sorted.bam | awk '$5 < 20 {count++} END {print "Low-MAPQ reads:", count+0}'

    ## After running corrected workflow:
    #awk '$3>=30' Y3_p2_depth.txt | wc -l | awk '{if($1<100) print "✅ Y3: p2 ABSENT (only "$1"/9540 positions ≥30x)"; else print "🔴 Y3: p2 PRESENT ("$1"/9540 positions ≥30x)"}'
    #✅ Y3: p2 ABSENT (only 12/9540 positions ≥30x)

    #If Y3 still shows high coverage after these fixes, share the output of:
    samtools flagstat Y3_vs_p2.sorted.bam
    head -20 Y3_p2_depth.txt

Interhost variant calling (Data_Tam_DNAseq_2026_19606wt_dAB_dIJ_mito_flu_on_ATCC19606)

  1. Input data:

     mkdir bacto; cd bacto;
     mkdir raw_data; cd raw_data;
    
     # ── Fluoxetine Dataset ──
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_1.fq.gz flu_dAB_cef_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_2.fq.gz flu_dAB_cef_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_1.fq.gz flu_dAB_cipro_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_2.fq.gz flu_dAB_cipro_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEdori-2/19606△ABfluEdori-2_1.fq.gz flu_dAB_dori_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEdori-2/19606△ABfluEdori-2_2.fq.gz flu_dAB_dori_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEnitro-3/19606△ABfluEnitro-3_1.fq.gz flu_dAB_nitro_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEnitro-3/19606△ABfluEnitro-3_2.fq.gz flu_dAB_nitro_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEpip-1/19606△ABfluEpip-1_1.fq.gz flu_dAB_pip_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEpip-1/19606△ABfluEpip-1_2.fq.gz flu_dAB_pip_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEpolyB-3/19606△ABfluEpolyB-3_1.fq.gz flu_dAB_polyB_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEpolyB-3/19606△ABfluEpolyB-3_2.fq.gz flu_dAB_polyB_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEtet-1/19606△ABfluEtet-1_1.fq.gz flu_dAB_tet_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEtet-1/19606△ABfluEtet-1_2.fq.gz flu_dAB_tet_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEcef-4/19606△IJfluEcef-4_1.fq.gz flu_dIJ_cef_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEcef-4/19606△IJfluEcef-4_2.fq.gz flu_dIJ_cef_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEcipro-3/19606△IJfluEcipro-3_1.fq.gz flu_dIJ_cipro_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEcipro-3/19606△IJfluEcipro-3_2.fq.gz flu_dIJ_cipro_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEdori-1/19606△IJfluEdori-1_1.fq.gz flu_dIJ_dori_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEdori-1/19606△IJfluEdori-1_2.fq.gz flu_dIJ_dori_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEnitro-3/19606△IJfluEnitro-3_1.fq.gz flu_dIJ_nitro_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEnitro-3/19606△IJfluEnitro-3_2.fq.gz flu_dIJ_nitro_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEpip-4/19606△IJfluEpip-4_1.fq.gz flu_dIJ_pip_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEpip-4/19606△IJfluEpip-4_2.fq.gz flu_dIJ_pip_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEpolyB-4/19606△IJfluEpolyB-4_1.fq.gz flu_dIJ_polyB_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEpolyB-4/19606△IJfluEpolyB-4_2.fq.gz flu_dIJ_polyB_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEcef-1/19606wtfluEcef-1_1.fq.gz flu_wt_cef_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEcef-1/19606wtfluEcef-1_2.fq.gz flu_wt_cef_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEcipro-2/19606wtfluEcipro-2_1.fq.gz flu_wt_cipro_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEcipro-2/19606wtfluEcipro-2_2.fq.gz flu_wt_cipro_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEdori-1/19606wtfluEdori-1_1.fq.gz flu_wt_dori_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEdori-1/19606wtfluEdori-1_2.fq.gz flu_wt_dori_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEnitro-1/19606wtfluEnitro-1_1.fq.gz flu_wt_nitro_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEnitro-1/19606wtfluEnitro-1_2.fq.gz flu_wt_nitro_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEpip-4/19606wtfluEpip-4_1.fq.gz flu_wt_pip_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEpip-4/19606wtfluEpip-4_2.fq.gz flu_wt_pip_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEpolyB-4/19606wtfluEpolyB-4_1.fq.gz flu_wt_polyB_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEpolyB-4/19606wtfluEpolyB-4_2.fq.gz flu_wt_polyB_R2.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEtet-2/19606wtfluEtet-2_1.fq.gz flu_wt_tet_R1.fastq.gz
     ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEtet-2/19606wtfluEtet-2_2.fq.gz flu_wt_tet_R2.fastq.gz
    
     # ── Mitomycin C Dataset ──
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_cipro_1/MitoC_E_AB_cipro_1_1.fq.gz mito_dAB_cipro_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_cipro_1/MitoC_E_AB_cipro_1_2.fq.gz mito_dAB_cipro_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_dori_1/MitoC_E_AB_dori_1_1.fq.gz mito_dAB_dori_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_dori_1/MitoC_E_AB_dori_1_2.fq.gz mito_dAB_dori_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_nitro_2/MitoC_E_AB_nitro_2_1.fq.gz mito_dAB_nitro_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_nitro_2/MitoC_E_AB_nitro_2_2.fq.gz mito_dAB_nitro_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_tet_2/MitoC_E_AB_tet_2_1.fq.gz mito_dAB_tet_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_tet_2/MitoC_E_AB_tet_2_2.fq.gz mito_dAB_tet_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_trime_4/MitoC_E_AB_trime_4_1.fq.gz mito_dAB_trime_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_trime_4/MitoC_E_AB_trime_4_2.fq.gz mito_dAB_trime_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_cipro_1/MitoC_E_IJ_cipro_1_1.fq.gz mito_dIJ_cipro_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_cipro_1/MitoC_E_IJ_cipro_1_2.fq.gz mito_dIJ_cipro_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_dori_4/MitoC_E_IJ_dori_4_1.fq.gz mito_dIJ_dori_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_dori_4/MitoC_E_IJ_dori_4_2.fq.gz mito_dIJ_dori_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_nitro_2/MitoC_E_IJ_nitro_2_1.fq.gz mito_dIJ_nitro_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_nitro_2/MitoC_E_IJ_nitro_2_2.fq.gz mito_dIJ_nitro_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_polyB_3/MitoC_E_IJ_polyB_3_1.fq.gz mito_dIJ_polyB_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_polyB_3/MitoC_E_IJ_polyB_3_2.fq.gz mito_dIJ_polyB_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_tet_3/MitoC_E_IJ_tet_3_1.fq.gz mito_dIJ_tet_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_tet_3/MitoC_E_IJ_tet_3_2.fq.gz mito_dIJ_tet_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_trime_1/MitoC_E_IJ_trime_1_1.fq.gz mito_dIJ_trime_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_trime_1/MitoC_E_IJ_trime_1_2.fq.gz mito_dIJ_trime_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_cipro_1/MitoC_E_wt_cipro_1_1.fq.gz mito_wt_cipro_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_cipro_1/MitoC_E_wt_cipro_1_2.fq.gz mito_wt_cipro_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_nitro_1/MitoC_E_wt_nitro_1_1.fq.gz mito_wt_nitro_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_nitro_1/MitoC_E_wt_nitro_1_2.fq.gz mito_wt_nitro_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_polyB_1/MitoC_E_wt_polyB_1_1.fq.gz mito_wt_polyB_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_polyB_1/MitoC_E_wt_polyB_1_2.fq.gz mito_wt_polyB_R2.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_trime_2/MitoC_E_wt_trime_2_1.fq.gz mito_wt_trime_R1.fastq.gz
     ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_trime_2/MitoC_E_wt_trime_2_2.fq.gz mito_wt_trime_R2.fastq.gz
  2. Call variant calling using snippy

     ln -s ~/Tools/bacto/db/ .;
     ln -s ~/Tools/bacto/envs/ .;
     ln -s ~/Tools/bacto/local/ .;
     cp ~/Tools/bacto/Snakefile .;
     cp ~/Tools/bacto/bacto-0.1.json .;
     cp ~/Tools/bacto/cluster.json .;
    
     #download CP059040.gb from GenBank
     #mv ~/Downloads/sequence\(2\).gb db/CP059040.gb
    
     mamba activate /home/jhuang/miniconda3/envs/bengal3_ac3
     (bengal3_ac3) jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2023_A6WT_A10CraA_A12AYE_A1917978$ which snakemake
     /home/jhuang/miniconda3/envs/bengal3_ac3/bin/snakemake
     (bengal3_ac3) jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2023_A6WT_A10CraA_A12AYE_A1917978$ snakemake -v
     4.0.0 --> CORRECT!
    
     #NOTE_1: modify bacto-0.1.json keeping only steps assembly, typing_mlst, possibly pangenome and variants_calling true; setting cpu=20 in all used steps.
         #setting the following in bacto-0.1.json
         "fastqc": false,
         "taxonomic_classifier": false,
         "assembly": true,
         "typing_ariba": false,
         "typing_mlst": true,
         "pangenome": true,
         "variants_calling": true,
         "phylogeny_fasttree": false,
         "phylogeny_raxml": false,
         "recombination": false, (due to gubbins-error set false)
    
         "prokka": {
           "genus": "Acinetobacter",
           "kingdom": "Bacteria",
           "species": "baumannii",
           "cpu": 10,
           "evalue": "1e-06",
           "other": ""
         },
    
         "mykrobe": {
           "species": "abaum"
         },
    
         "reference": "db/CP059040.gb"
    
     #NOTE_2: needs disk Titisee since the pipeline needs /media/jhuang/Titisee/GAMOLA2/TIGRfam_db/TIGRFAMs_15.0_HMM.LIB
     snakemake --printshellcmds
  3. 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; deleting the isolates "flu_wt_cipro" and "flu_dAB_cipro"
     isolates = ["flu_wt_cef", "flu_wt_dori", "flu_wt_nitro", "flu_wt_pip", "flu_wt_polyB", "flu_wt_tet",    "flu_dAB_cef", "flu_dAB_dori", "flu_dAB_nitro", "flu_dAB_pip", "flu_dAB_polyB", "flu_dAB_tet",    "flu_dIJ_cef", "flu_dIJ_cipro", "flu_dIJ_dori", "flu_dIJ_nitro", "flu_dIJ_pip", "flu_dIJ_polyB",         "mito_dIJ_trime",    "mito_wt_cipro", "mito_wt_nitro", "mito_wt_polyB", "mito_wt_trime",    "mito_dAB_cipro", "mito_dAB_dori", "mito_dAB_nitro", "mito_dAB_tet", "mito_dAB_trime",    "mito_dIJ_cipro", "mito_dIJ_dori", "mito_dIJ_nitro", "mito_dIJ_polyB", "mito_dIJ_tet"]
    
     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
  4. Using spandx calling variants (almost the same results to the one from viral-ngs!)

     mamba deactivate
     mamba activate /home/jhuang/miniconda3/envs/spandx
    
     # PREPARE the inputs for the options ref and database
     mkdir ~/miniconda3/envs/spandx/share/snpeff-5.1-2/data/PP810610
     cp PP810610.gb  ~/miniconda3/envs/spandx/share/snpeff-5.1-2/data/PP810610/genes.gbk
     vim ~/miniconda3/envs/spandx/share/snpeff-5.1-2/snpEff.config
     /home/jhuang/miniconda3/envs/spandx/bin/snpEff build PP810610    #-d
     ~/Scripts/genbank2fasta.py PP810610.gb
     mv PP810610.gb_converted.fna PP810610.fasta    #rename "NC_001348.1 xxxxx" to "NC_001348" in the fasta-file
    
     ln -s /home/jhuang/Tools/spandx/ spandx
     # Deleting the contaminated samples flu_wt_cipro*.fastq and flu_dAB_cipro*.fastq
     (spandx) nextflow run spandx/main.nf --fastq "trimmed/*_P_{1,2}.fastq" --ref CP059040.fasta --annotation --database CP059040 -resume
    
     # RERUN SNP_matrix.sh due to the error ERROR_CHROMOSOME_NOT_FOUND in the variants annotation, resulting in all impacts are MODIFIER --> IT WORKS!
     cd Outputs/Master_vcf
     conda activate /home/jhuang/miniconda3/envs/spandx
     (spandx) cp -r ../../snippy/Y1/reference . # Eigentlich irgendein directory, all directories contains the same reference.
     (spandx) cp ../../spandx/bin/SNP_matrix.sh ./
     #Note that ${variant_genome_path}=CP059040 in the following command, but it was not used after the following command modification.
     #Adapt "snpEff eff -no-downstream -no-intergenic -ud 100 -formatEff -v ${variant_genome_path} out.vcf > out.annotated.vcf" 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
     (spandx) bash SNP_matrix.sh CP059040 .

Cross-Caller SNP/Indel Concordance & Invariant Variant Analyzer; Multi-Isolate Variant Intersection, Annotation Harmonization & Caller Discrepancy Report; Comparative Genomic Variant Profiling: Concordance, Invariance & Allele Mismatch Analysis; VarMatch: Cross-Tool Variant Concordance Pipeline

  1. Calling inter-host variants by merging the results from snippy+spandx

     mamba activate plot-numpy1
     cd bacto
     cp Outputs/Master_vcf/All_SNPs_indels_annotated.txt .
     cp snippy/summary_snps_indels.csv .
    
     cp ~/Scripts/process_variants_snippy_alleles_spandx_annotations.py .
    
     #Configuring
     ISOLATES = [
             "flu_wt_cef", "flu_wt_cipro", "flu_wt_dori", "flu_wt_nitro", "flu_wt_pip", "flu_wt_polyB", "flu_wt_tet",
             "flu_dAB_cef", "flu_dAB_cipro", "flu_dAB_dori", "flu_dAB_nitro", "flu_dAB_pip", "flu_dAB_polyB", "flu_dAB_tet",
             "flu_dIJ_cef", "flu_dIJ_cipro", "flu_dIJ_dori", "flu_dIJ_nitro", "flu_dIJ_pip", "flu_dIJ_polyB",
             "mito_dIJ_trime",
             "mito_wt_cipro", "mito_wt_nitro", "mito_wt_polyB", "mito_wt_trime",
             "mito_dAB_cipro", "mito_dAB_dori", "mito_dAB_nitro", "mito_dAB_tet", "mito_dAB_trime",
             "mito_dIJ_cipro", "mito_dIJ_dori", "mito_dIJ_nitro", "mito_dIJ_polyB", "mito_dIJ_tet"
             ]
    
     (plot-numpy1) python process_variants_snippy_alleles_spandx_annotations.py
    
     # mv common_variants_all_snippy_annotated.xlsx common_variants_snippy+spandx_annotated_19606wt_dAB_dIJ_mito_flu.xlsx
     # mv common_variants_invariant_snippy_annotated.xlsx common_invariants_snippy+spandx_annotated_19606wt_dAB_dIJ_mito_flu.xlsx
  2. (TODO_TOMORROW) Manully checking each of the 6 records by comparing them to the results from SPANDx; three are confirmed!

     #CHROM,POS,REF,ALT,TYPE,Y1,Y2,Y3,Y4,W1,W2,W3,W4,Effect,Impact,Functional_Class,Codon_change,Protein_and_nucleotide_change,Amino_Acid_Length,Gene_name,Biotype
    
     # -- Results from snippy --
     #move: CP059040,1527276,TTGAACC,T,del,TTGAACC,TTGAACC,TTGAACC,T,TTGAACC,TTGAACC,T,T,conservative_inframe_deletion,MODERATE,,gaacct/,p.Glu443_Pro444del/c.1327_1332delGAACCT,704,H0N29_07175,protein_coding
     #confirmed: CP059040,1843289,G,T,snp,G,T,G,G,G,G,G,G,missense_variant,MODERATE,MISSENSE,gCg/gAg,p.Ala37Glu/c.110C>A,357,H0N29_08665,protein_coding
     #confirmed: CP059040,2019186,G,A,snp,A,G,G,G,G,G,G,G,upstream_gene_variant,MODIFIER,,59,c.-59C>T,144,H0N29_09480,protein_coding
     #delete_this? CP059040,3124917,T,"TAC,TACTTCATTACATACCAACCGCCAAGGGTGC",snp,C,T,C,C,T,T,T,C,upstream_gene_variant,MODIFIER,,25,c.-25_-24insAC,0,H0N29_00075,protein_coding
     #move: CP059040,3310021,C,CT,ins,CT,CT,CT,CT,C,CT,CT,CT,intragenic_variant,MODIFIER,,,n.3310021_3310022insT,,H0N29_00075,
     #confirmed: CP059040,3853714,G,A,snp,G,G,G,G,G,A,G,A,stop_gained,HIGH,NONSENSE,Cag/Tag,p.Gln91*/c.271C>T,338,H0N29_18290,protein_coding
     #--> Only three SNPs are confirmed --> means there is almost no variation in the genomic level!
    
     # -- Results from the SPANDx --
     #CP059040   1527276 TTGAACC T   INDEL   TTGAACC/T   T   T   T   T   T   T   T   conservative_inframe_deletion   MODERATE        gaacct/ p.Glu443_Pro444del/c.1327_1332delGAACCT 704 H0N29_07175 protein_coding
    
     #CP059040   1843289 G   T   SNP G   T   G   G   G   G   G   G   missense_variant    MODERATE    MISSENSE    gCg/gAg p.Ala37Glu/c.110C>A 357 H0N29_08665 protein_coding
     #CP059040   2019186 G   A   SNP A   G   G   G   G   G   G   G   upstream_gene_variant   MODIFIER        59  c.-59C>T    144 H0N29_09480 protein_coding
    
     #Cmp to CP059040    3124917 T   TAC,TACTTCATTACATACCAACCGCCAAGGGTGC INDEL   .   TACTTCATTACATACCAACCGCCAAGGGTGC TACTTCATTACATACCAACCGCCAAGGGTGC TAC .   .   .   .   upstream_gene_variant   MODIFIER        25  c.-25_-24insAC  0   H0N29_00075 protein_coding
     #Cmp to CP059040    3124920 C   CATTACATACCAACCGCCAAGGGTGCTTCATG    INDEL   .   .   .   CATTACATACCAACCGCCAAGGGTGCTTCATG    .   .   C   .   upstream_gene_variant   MODIFIER        22  c.-22_-21insATTACATACCAACCGCCAAGGGTGCTTCATG 0   H0N29_00075 protein_coding
    
     #TODO: Move to invariant-file: CP059040 3310021 C   CT  INDEL   CT  CT  CT  CT  CT  CT  CT  CT  intragenic_variant  MODIFIER            n.3310021_3310022insT       H0N29_00075
     #CP059040   3853714 G   A   SNP G   G   G   G   G   A   G   A   stop_gained HIGH    NONSENSE    Cag/Tag p.Gln91*/c.271C>T   338 H0N29_18290 protein_coding
    
     #-->For the Indel-report, more complicated, needs the following command to find the initial change and related codon-change.
     ## Check gene strand in your GFF/GenBank
     #grep "H0N29_07175" reference.gff
     # Extract 20 bp around the variant from reference
     samtools faidx CP059040.fasta CP059040:1527260-1527290
  3. Run nextflow bacass

     # 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
     # Download 20190108_kmerfinder_stable_dirs.tar.gz from https://zenodo.org/records/13447056
     #--kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder_db.tar.gz
     #--kmerfinderdb /mnt/nvme1n1p1/REFs/20190108_kmerfinder_stable_dirs.tar.gz
     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
     #Possibly the chraracter '△' is a problem.
     #Solution: 19606△ABfluEcef-1 → 19606delABfluEcef-1
    
     #SAVE bacass_out/Kmerfinder/kmerfinder_summary.csv to bacass_out/Kmerfinder/An6?/An6?_kmerfinder_results.xlsx
    
     samplesheet.tsv
     sample,fastq_1,fastq_2
     flu_dAB_cef,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_1.fq.gz,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_2.fq.gz
     flu_dAB_cipro,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_1.fq.gz,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_2.fq.gz
    
     #busco example results:
     Input_file      Dataset Complete        Single  Duplicated      Fragmented      Missing n_markers       Scaffold N50    Contigs N50     Percent gaps    Number of scaffolds
     wt_cef.scaffolds.fa     bacteria_odb10  98.4    98.4    0.0     1.6     0.0     124     285852  285852  0.000%  45
     wt_cipro.scaffolds.fa   bacteria_odb10  90.3    89.5    0.8     8.1     1.6     124     7434    7434    0.000%  1699
  4. Run bactmap

     nextflow run nf-core/bactmap -r 1.0.0 -profile docker \
     --input samplesheet.csv \
     --reference CP059040.fasta \
     --outdir bactmap_out \
     -resume
    
     nextflow run nf-core/bactmap -r 1.0.0 -profile docker \
     --input samplesheet.csv \
     --reference CU459141.fasta \
     --outdir bactmap_out \
     -resume
    
     sample,fastq_1,fastq_2
     G18582004,fastqs/G18582004_1.fastq.gz,fastqs/G18582004_2.fastq.gz
     G18756254,fastqs/G18756254_1.fastq.gz,fastqs/G18756254_2.fastq.gz
     G18582006,fastqs/G18582006_1.fastq.gz,fastqs/G18582006_2.fastq.gz
    
     mkdir bactmap_workspace
     #Prepare reference.fasta (example for CP059040.fasta) in bactmap_workspace and samplesheet.csv in bactmap_workspace
     nextflow run nf-core/bactmap -r 1.0.0 -profile docker \
     --input samplesheet.csv \
     --reference CP059040.fasta \
     --outdir bactmap_out \
     -resume

Tools and Services of BV-BRC

🔗 BV-BRC Comprehensive Genome Analysis: https://www.bv-brc.org/app/ComprehensiveGenomeAnalysis

https://www.bv-brc.org/docs/quick_references/services/genome_alignment_service.html

bv_services_menu

https://www.bv-brc.org/docs/quick_start/ird-vipr_bv-brc_mapping.html#tools

A free, integrated bioinformatics platform for bacterial and viral genomics, metagenomics, and outbreak analysis.


🧬 Genomics

  • Genome Assembly
  • Genome Annotation
  • Comprehensive Genome Analysis (B)
  • BLAST
  • Primer Design
  • Similar Genome Finder
  • Genome Alignment
  • Variation Analysis
  • Tn-Seq Analysis

🌳 Phylogenomics

  • Bacterial Genome Tree
  • Viral Genome Tree
  • Core Genome MLST
  • Whole Genome SNP Analysis

🧪 Gene / Protein Tools

  • MSA and SNP Analysis
  • Meta-CATS
  • Gene/Protein Tree
  • Proteome Comparison
  • Protein Family Sorter
  • Comparative Systems
  • Docking

🦠 Metagenomics

  • Taxonomic Classification
  • Metagenomic Binning
  • Metagenomic Read Mapping
  • Mobile Element Detection

📡 Transcriptomics

  • RNA-Seq Analysis
  • Expression Import

⚙️ Utilities

  • Fastq Utilities
  • ID Mapper

🦠 Viral Tools

  • SARS-CoV-2 Genome Analysis
  • SARS-CoV-2 Wastewater Analysis
  • Influenza Sequence Submission
  • Influenza HA Subtype Conversion
  • Influenza Reassortment Analysis
  • Subspecies Classification
  • Viral Assembly

🚨 Outbreak Tracker

  • Measles 2025
  • Mpox 2024
  • Influenza H5N1 2024
  • SARS-CoV-2

All tools are web-based, require no local installation, and support both interactive and programmatic access via API/CLI.

Arthrobacter phenanthrenivorans vs. Acinetobacter towneri

Arthrobacter phenanthrenivoransAcinetobacter towneri 中文详解


🔬 1. Arthrobacter phenanthrenivorans(嗜菲节杆菌 / 菲降解节杆菌)

📋 基本信息

项目 内容
中文名称 嗜菲节杆菌(或:菲降解节杆菌)
学名 Arthrobacter phenanthrenivorans
命名含义 phenanthrenivorans = “菲(phenanthrene)” + “吞噬/降解(vorans)”
分类地位 放线菌门 → 放线菌纲 → 微球菌目 → 微球菌科 → 节杆菌属
模式菌株 Sphe3 (DSM 15210)

📌 分类备注:部分研究基于基因组分析建议将其划入新属 Pseudarthrobacter,但多数文献仍沿用 Arthrobacter 名称。

🧫 生物学特性

  • ✅ 革兰氏阳性(Gram-positive)
  • ✅ 严格好氧(需氧生长)
  • ✅ 杆状→球状形态变化(典型节杆菌生活史)
  • ✅ 非运动性,无鞭毛
  • ✅ 土壤、沉积物中常见,耐干燥、耐贫营养

🌱 核心功能:菲(Phenanthrene)降解

是一种三环多环芳烃(PAH),具有致癌性,常见于石油污染场地。

该菌的降解通路简述:

菲(C₁₄H₁₀)
   ↓ 菲双加氧酶(PhdABC)启动
→ 1,2-二羟基菲 → 开环酶 → 邻苯二甲酸衍生物
   ↓ β-酮己二酸途径
→ 乙酰-CoA + 琥珀酸 → TCA循环 → CO₂ + H₂O

应用价值

  • 🌍 石油污染土壤/水体的生物修复(Bioremediation)
  • 🧪 芳香烃降解酶的工程化应用
  • 🔬 研究原核生物芳香环代谢的模式菌株

⚠️ 是否为病原体?

不是人类或动物病原体

  • 无已知毒力因子(如毒素、侵袭素、荚膜等)
  • 无临床感染病例报告
  • 属于环境腐生菌(Saprophyte),对人体无害
  • 在常规实验室操作下按生物安全1级(BSL-1)管理

🔬 2. Acinetobacter towneri(托纳不动杆菌)

📋 基本信息

项目 内容
中文名称 托纳不动杆菌
学名 Acinetobacter towneri
首次描述 Nemec et al., 2013(基于DNA-DNA杂交与rpoB基因分析)
分类地位 变形菌门 → γ-变形菌纲 → 假单胞菌目 → 莫拉氏菌科 → 不动杆菌属
基因组特征 基因组大小 ~3.9 Mb,GC含量 ~39%

🧫 生物学特性

  • ✅ 革兰氏阴性(Gram-negative)
  • ✅ 严格好氧,氧化酶阴性,过氧化氢酶阳性
  • ✅ 球杆状,常成对或短链排列
  • 无鞭毛,但部分菌株具菌毛,可在表面滑行(”twitching motility”)
  • ✅ 广泛分布于土壤、水体、医院环境表面

🏥 临床与生态意义

方面 说明
环境角色 常见于水体、土壤,参与有机物降解;部分菌株具降解芳香族化合物潜力
临床分离 偶见于医院环境(如呼吸机、导管表面),但极少作为原发致病菌被分离
耐药性 天然对部分抗生素耐受(如β-内酰胺类),但耐药谱通常弱于 A. baumannii 复合群

⚠️ 是否为病原体?

🟡 条件致病性(机会性病原体)

  • 健康人群:基本无致病风险,属于环境常驻菌
  • 免疫低下者:极少数病例报告与导管相关感染、伤口定植有关,但因果关系常难确立
  • A. baumannii 的区别
    • A. baumannii 是WHO重点监控的高危耐药病原体(CRAB)
    • A. towneri 未被列入临床重点关注菌种,致病证据有限
  • 实验室操作建议:生物安全2级(BSL-2)常规防护即可

🆚 两菌对比总结

特征 A. phenanthrenivorans A. towneri
革兰染色 阳性(+) 阴性(−)
主要生境 污染土壤、沉积物 水、土壤、医院环境
核心能力 菲等PAHs高效降解 环境适应性强,部分降解潜力
人类致病性 ❌ 非病原体 🟡 极低风险机会菌
生物安全等级 BSL-1 BSL-2(常规防护)
应用方向 生物修复、酶工程 环境微生物研究、医院感染溯源

💡 实用建议

🔹 若您从事环境修复研究

  • A. phenanthrenivorans 是优秀的菲降解菌候选,可关注其 phd 基因簇 表达调控。

🔹 若您在临床样本中检出 A. towneri
1️⃣ 首先排除样本污染或定植(尤其来自环境拭子、非无菌部位)
2️⃣ 结合患者免疫状态、感染症状、其他病原证据综合判断
3️⃣ 药敏试验仍建议进行,但无需按”超级细菌”紧急处理

🔹 中文文献检索技巧

  • Arthrobacter phenanthrenivorans:可搜”节杆菌 菲降解”、”嗜菲节杆菌”
  • Acinetobacter towneri:文献较少,建议用英文名 + “不动杆菌 新种” 组合检索

📚 权威参考来源

  • 《伯杰氏系统细菌学手册》(Bergey’s Manual)
  • LPSN (List of Prokaryotic names with Standing in Nomenclature): https://lpsn.dsmz.de
  • NCBI Taxonomy & Genome Database

如需我帮您查找这两株菌的基因组登录号特异性引物序列培养条件优化方案,请随时告知!🧬🔬

Journal-Polished Manuscript Methods and Analysis Text for TnSeq (Data_Jiline_Transposon)

tnseq_methods.docx

Part 1: Manuscript Methods Section

Raw paired-end sequencing data in FASTQ format were processed using the Transposon Position Profiling (TPP) pipeline (DeJesus et al., 2017), adapted for Tn5 transposon specificity. The analysis was performed using the reference genome of Yersinia enterocolitica subsp. enterocolitica WA-314 (GenBank accession: CP009367).

Read 1 (R1) was screened for the Tn5-specific primer sequence (AGCTTCAGGGTTGAGATGTGTATAAGAGACAG), allowing up to one nucleotide mismatch. Genomic DNA flanking the transposon insertion site was extracted from R1 and R2 reads, and paired-end reads were aligned to the reference genome using BWA-MEM (Li, 2013). Only properly paired reads mapping to opposite strands were retained for further analysis.

Unique insertion events were quantified after collapsing PCR duplicates. Reads were grouped according to barcode sequence and mapping coordinates, and each unique combination was counted as a single template. Template counts at each genomic position were exported in .wig format for downstream statistical analysis.

Statistical analysis of insertion patterns was conducted using Transit (v3.2.5; DeJesus & Ioerger, 2016). Datasets were normalized using the Trimmed Total Reads (TTR) method to correct for differences in library complexity and sequencing depth. Conditional essentiality was assessed by analysis of variance (ANOVA), followed by Benjamini-Hochberg false discovery rate (FDR) correction (α = 0.05), comparing insertion counts across five experimental conditions: initial mutant library, LB culture, 24-hour growth control, intracellular infection, and extracellular infection. Constitutive essentiality was evaluated independently using the Tn5Gaps algorithm (Griffin et al., 2011), which identifies genes containing significant runs of non-insertions by permutation testing.

Genome-wide insertion distributions and essential gene locations were visualized using Circos (Krzywinski et al., 2009). Scatter plots represented normalized template counts for each condition, and an inner heatmap highlighted genes classified as essential (FDR-adjusted p-value < 0.05, Tn5Gaps). All analyses were performed on the complete reference genome CP009367 to ensure accurate coordinate mapping.

References DeJesus, M. A. et al. Nature Protocols 12, 2017. DeJesus, M. A. & Ioerger, T. R. Bioinformatics 32, 2016. Griffin, J. E. et al. PNAS 108, 2011. Li, H. arXiv 1303.3997, 2013. Krzywinski, M. et al. Genome Research 19, 2009.

Part 2: Summary Table – Key Quality Metrics (Run3 – Final Analysis)

METRIC INITIAL_MUTANTS LB_CULTURE GROWTHOUT_CONTROL_24H INTRACELLULAR_MUTANTS_24H EXTRACELLULAR_MUTANTS_24H
Total reads 49,821,406 43,486,192 70,663,823 51,244,639 47,473,664
Valid Tn prefix 20,339,623 (40.8%) 22,631,019 (52.0%) 26,777,280 (37.9%) 23,204,461 (45.3%) 9,358,660 (19.7%)
Mapped read pairs 16,445,755 (80.9%) 19,994,409 (88.4%) 24,141,881 (90.2%) 20,909,755 (90.1%) 6,588,961 (70.4%)
Unique templates 2,559,561 3,393,325 3,642,183 1,476,522 248,080
Template ratio 6.43 5.89 6.63 14.16 26.56
Density (TAs hit/total) 0.026 0.026 0.026 0.022 0.012
BC_corr 0.921 0.930 0.918 0.911 0.824

Interpretation:

  • BC_corr > 0.9 for four of the five samples indicates strong concordance between raw reads and deduplicated templates and is generally consistent with minimal PCR amplification bias.
  • The extracellular_mutants_24h sample shows reduced library complexity (19.7% valid prefix, template ratio = 26.56, BC_corr = 0.824). This pattern likely reflects strong biological selection during extracellular growth.

Part 3: Step-by-Step Data Processing (TPP Pipeline)

Primer Screening & Genomic Extraction

  • Primer: AGCTTCAGGGTTGAGATGTGTATAAGAGACAG (Tn5-specific)
  • Parameters: One mismatch allowed
  • Genomic extraction: Suffix ≥20 bp downstream of the primer; adapter stripping was applied for short fragments

Paired-End Mapping

  • Tool: BWA-MEM (bwa-alg mem)
  • Requirements: Both R1 and R2 were required to map to opposite strands on reference CP009367

Template Deduplication

  • Reads were grouped by (barcode, mapping coordinates)
  • Each unique combination was counted once as a “template” to remove PCR duplicates

Output

  • .wig files: Template counts per genomic position
  • .tn_stats.txt: Library QC metrics

Part 4: Statistical Analysis (Transit)

Normalization: TTR Method

  • Trimmed Total Reads: Scales samples to equal total counts after excluding the top and bottom 5% of values.
  • Purpose: Reduces the influence of outliers, including essential genes with zero counts or highly amplified templates.

Differential Essentiality Analysis: ANOVA

transit anova combined.wig samples_run3.metadata CP009367.prot_table \\
  anova_out_intracellular_vs_LB \\
  -n TTR --include-conditions intracellular_mutants_24h,LB_culture \\
  --ref LB_culture -PC 5 -alpha 1000 -winz
PARAMETER MEANING RATIONALE
--include-conditions Conditions to compare (comma-separated) Enables pairwise or multi-condition comparisons
--ref Reference condition for LFC calculation Log-fold changes are computed relative to this baseline
-PC 5 Pseudocount added to all counts Avoids log(0) and stabilizes low-count estimates
-alpha 1000 Variance moderation parameter Shrinks extreme variance estimates for genes with few insertions
-winz Winsorization flag Caps the top and bottom 1% of values to reduce the influence of outliers
-n TTR Normalization method Applies Trimmed Total Reads normalization before analysis

KEY METRICS IN OUTPUT:

COLUMN DEFINITION
Orf/Rv Gene identifier (e.g., CH47_1012)
Gene Gene name (e.g., pncB, phoQ)
TAs Number of TA dinucleotides within the ORF
Mean_[condition] Mean normalized template count per condition
LFC_[condition] Log₂ fold change relative to the reference condition
Fstat F-test statistic (between-condition / within-condition variance)
Pval Raw p-value from the ANOVA F-test
Padj Benjamini-Hochberg FDR-adjusted p-value
status Quality control flags (e.g., “No counts in all conditions”)

Results saved in: anova_out_intracellular.xls, anova_out_extracellular.xls, heatmap_q0.05.png

Essentiality Analysis: Tn5Gaps

transit tn5gaps ${sample}_run3_normalized.wig CP009367.prot_table \\
  ${sample}_tn5gaps_trimmed.dat -m 2 -r Sum -iN 5 -iC 5
PARAMETER MEANING RATIONALE
-m 2 Minimum insertions for analysis Genes with fewer than 2 insertions lack sufficient statistical power
-r Sum Scoring method: sum of counts Robust measure of overall insertion density
-iN 5 Minimum insertion density (per kb) for non-essential calls Filters genes with very sparse coverage
-iC 5 Minimum absolute insertions for non-essential calls Ensures sufficient absolute coverage

KEY METRICS IN OUTPUT:

COLUMN DEFINITION
k Observed insertions within the ORF
n Total TA dinucleotides within the ORF
r Length of the maximum run of non-insertions
pval/padj Permutation test p-value, FDR-corrected
call Essential/Non-essential (FDR < 0.05)

Results Summary:

  • ~218 essential genes were identified in the initial mutant library (~5.4% of the genome).
  • Typical essential genes confirmed:
    • Ribosomal proteins: rpmJ, rpsM, rpsK, rpsD, rplQ, rpmI, rplT
    • RNA polymerase: rpoA (alpha subunit)
    • Translation factors: infC (IF-3), pheS/pheT (Phe-tRNA ligase)
    • Protein translocation: secY, secD, secF (Sec translocase)
    • DNA replication: dnaA, dnaN
    • Cell division: ftsH (FtsH protease)
    • tRNA processing: thrS (Thr-tRNA ligase)
    • Nucleoid organization: ihfA/ihfB (integration host factor)
    • Ribosome maturation: rimP, rbfA
    • Central metabolism: glmM (phosphoglucosamine mutase)

These genes are universally essential across bacterial species, supporting the validity of the analysis pipeline.

Results saved in: Tn5Gaps.xls

Part 5: Circos Visualization – Genome-Wide Insertion Patterns

To visualize transposon insertion distributions across the Y. enterocolitica WA-314 genome, a Circos plot was generated with the following structure:

Figure Layout

  • Outermost ring: Genome ideogram with kilobase scale markers
  • Five concentric scatter rings: Normalized template counts per insertion site for each experimental condition (extracellular, intracellular, growth control, LB culture, initial mutants), color-coded for distinction
  • Innermost heatmap ring: Locations of genes classified as essential by Tn5Gaps analysis (FDR-adjusted p-value < 0.05)

Data Preparation Workflow

  1. Input processing: The normalized combined.wig file, containing template counts per TA site across all conditions, was parsed to extract coordinate-value pairs for each sample.
  2. Format conversion: Data were reshaped into Circos-compatible format (chromosome, start, end, value), with zero-count positions optionally removed to improve visual clarity.
  3. Essential gene extraction: Genes identified as essential in the initial mutant library were extracted from the Tn5Gaps output and formatted as genomic intervals for heatmap display.
  4. Configuration: A Circos configuration file specified ring radii, color schemes, glyph styles (circles for scatter plots), axis spacing, and label formatting.

This visualization complements the statistical analyses by providing an intuitive spatial overview of insertion patterns across the complete genome.

Part 6: Addressing Specific Questions

1. Step-by-step analysis? See Part 2 above. The TPP pipeline integrates trimming, mapping, counting, and deduplication into a single workflow.

2. Bias correction for samples with fewer positions but higher reads per position?

  • Template deduplication: Collapses PCR duplicates by (barcode + coordinate).
  • TTR normalization: Trims extreme values before scaling, thereby reducing the influence of outliers.
  • BC_corr monitoring: Values > 0.9 indicate minimal PCR bias in most samples.
  • Gene-length normalization: Density = k/n (insertions per TA site), preventing longer genes from appearing artificially essential.

3. Sequence motif analysis around insertion sites? Although Tn5 displays relatively relaxed sequence specificity, unlike Himar1 with its strict TA requirement, explicit motif logo analysis was not performed. However, the pipeline inherently restricts analysis to valid insertion sites through precise mapping to the reference genome.

4. Determining significantly less frequently mutated genes? Two complementary approaches were applied:

  • Tn5Gaps: Identifies constitutive essentiality through runs of non-insertions (permutation test, FDR correction).
  • ANOVA: Identifies condition-specific essentiality by comparing insertion counts across conditions (F-test, Benjamini-Hochberg correction).

5. Positional effects (mutations at gene ends less lethal)? Yes, this issue is explicitly addressed within the analysis framework. The Tn5Gaps algorithm accounts for positional effects by distinguishing between internal and terminal gaps in insertion coverage:

  • r metric: Represents the length of the longest continuous run of non-insertions. Long internal runs typically indicate essential protein domains.
  • lenovr metric: Represents the full length of the non-insertion run with the greatest overlap with the gene body.

Decision Pipeline Summary:

  1. For each gene, calculate k (observed insertions), n (total TA sites), r, and lenovr from the insertion data.
  2. Perform a permutation test: p = P(r_permr_obs | random insertion).
  3. Apply Benjamini-Hochberg correction to obtain the adjusted p-value (p_adj).
  4. Interpret lenovr to determine whether the significant gap is internal or terminal.

Final Essentiality Call:

  • If p_adj < 0.05 and lenovrr (internal gap) → Essential
  • If p_adj < 0.05 and lenovr << r (terminal gap) → Review manually
  • If p_adj ≥ 0.05 → Non-essential (insufficient evidence)

This two-layer approach, combining statistical significance (p_adj) with biological context (lenovr/k), ensures that essentiality calls are both statistically rigorous and biologically interpretable. It explicitly accounts for positional effects, particularly terminal tolerance, where gaps at gene ends may have limited functional consequences.