Structural variant results (Data_Tam_DNAseq_2025_Y1Y2Y3Y4W1W2W3W4_Tig1_Tig2_dIJ_on_ATCC19606)

  • SV_3
  • SV_2
  • SV_1

关于两个结构变异的详细解释

让我用中文详细解释这两个变异的机制:


1️⃣ 1,101-bp ISAba11 插入:不是”替换”,而是”插入+复制”

您的理解偏差:认为 1,101 bp 替换了原来的 6 bp。

实际情况

【插入前 - 参考基因组】
...NNNNN-TTAAA-[galE 编码区]-TTAAA-NNNNN...
          ↑
      原始靶位点 (5 bp,如 TTAAA)

【插入后 - 您的样本】
...NNNNN-TTAAA-[galE 5′端]-TTAAA-[ISAba11 1,101 bp]-TTAAA-[galE 3′端]-TTAAA-NNNNN...
          ↑_________________↑         ↑_________________↑
         原始 TSD            新复制的 TSD (靶位点重复)

关键点:

  1. 不是替换:原来的 TTAAA 仍然保留(变成了左侧的 TSD)
  2. 插入 + 复制
    • 插入 1,101 bp 的 ISAba11 元件
    • 转座酶在靶位点产生交错切口(staggered cut)
    • 宿主 DNA 聚合酶填补缺口 → 复制了一份 TTAAA(形成右侧 TSD)
  3. 净增加:基因组总长度增加了 1,101 bp + 5 bp = 1,106 bp

为什么坐标显示 6 bp (3853883–3853888)?

  • Assemblytics 报告的 ref_start–ref_stop插入位点的区间
  • 这个区间覆盖了:
    • 原始靶位点 (5 bp)
    • 可能包括 galE 被中断的边界 (1 bp)
  • 不是”被替换的序列长度”,而是”插入事件发生的坐标范围”

验证方法:

# 提取插入位点两侧序列,应该看到相同的 5-bp TSD
samtools faidx ./Y3_unicycler_out/assembly.fasta 1:3853870-3853900

# 预期输出:
# ...TTAAA-[部分 galE]-TTAAA-[ISAba11]-TTAAA-[部分 galE]-TTAAA...
#            ↑左 TSD↑              ↑右 TSD↑

2️⃣ 198-bp 串联收缩:为什么坐标差 122 bp,但变异大小是 198 bp?

您的困惑:3125037 – 3124916 = 121 bp(或 +1 = 122 bp),但 Assemblytics 报告 size = 198 bp。

解释

参考基因组的实际结构:

3124916..3124942  [间隔区]        27 bp
3124943..3125017  [tRNA-Gln #3]   75 bp  ← 这个基因被"收缩"丢失
3125018..3125037  [间隔区]        20 bp
───────────────────────────────
总跨度            122 bp (坐标范围)

但 Assemblytics 计算的”变异大小”是:

丢失的序列 = 完整的 tRNA-Gln 基因 + 两侧部分间隔区
           = 75 bp (tRNA) + ~61 bp (两侧间隔区 + 重复单元边界) + 相邻重复单元的部分序列
           ≈ 198 bp (总序列差异)

为什么会有这个差异?

关键概念:Assemblytics 的 size 字段表示参考序列与查询序列之间的总长度差异,而不是简单的坐标跨度。

【参考基因组】
[tRNA #1]--spacer--[tRNA #2]--spacer--[tRNA #3]--spacer--[tRNA #4]
←──────────────────────────────── 4 个拷贝,总跨度 ~438 bp ────────→

【您的样本】
[tRNA #1]--spacer--[tRNA #2]--spacer--[tRNA #4]
←──────────────────────────── 3 个拷贝,总跨度 ~240 bp ──────────→

长度差异 = 438 - 240 = 198 bp ← 这就是 Assemblytics 报告的 "size"

坐标范围 (122 bp) vs 变异大小 (198 bp):

指标 数值 含义
ref_start–ref_stop 122 bp Assemblytics 分析的局部区间(包含断点)
size 198 bp 参考与查询之间的总序列长度差异(丢失的完整重复单元)

类比理解:

想象一本书的目录:

  • 参考:第1章、第2章、第3章、第4章(共4章)
  • 您的样本:第1章、第2章、第4章(第3章缺失)

如果第3章 + 前后过渡页共 198 页,但 Assemblytics 只标注”缺失发生在第3124916–3125037页之间”(122页范围),这是因为:

  • 断点精确位置在这个区间内
  • 但实际丢失的内容跨越了更大的范围(包括完整的第3章)

📊 总结对比

变异类型 坐标范围 实际序列变化 机制
ISAba11 插入 6 bp (3853883–3853888) +1,101 bp (插入) + 5 bp (TSD 复制) 转座子”复制-粘贴”
tRNA 收缩 122 bp (3124916–3125037) −198 bp (丢失 1 个重复单元) 复制滑动

核心区别

  • 插入:坐标范围小(插入位点),但实际增加大量序列
  • 收缩:坐标范围小(断点区间),但实际丢失的序列跨越多个重复单元

🔍 验证命令

# 1. 验证 ISAba11 插入 + TSD
samtools faidx ./Y3_unicycler_out/assembly.fasta 1:3853870-3853900 | grep -v "^>"
# 应该看到:...TTAAA...TTAAA...[ISAba11]...TTAAA...

# 2. 验证 tRNA 收缩
# 提取参考和样本的 tRNA 区域
samtools faidx bacto/CP059040.fasta CP059040:3124600-3125200 > ref_trna.fasta
samtools faidx ./W1_unicycler_out/assembly.fasta 1:3067700-3067900 > query_trna.fasta

# 比对查看重复单元数量差异
mafft --auto ref_trna.fasta query_trna.fasta | less
# 参考应有 4 个 ~75 bp 的峰,样本只有 3 个


Figure 1: Homologous Recombination-Mediated 4.4-kb Deletion

Illustrates: Loss of the adeIJK multidrug efflux pump locus
Panels:

  • A (Reference) Intact gene arrangement: YbjQadeKadeJadeIPAP2
  • B (Variant) Direct junction after 4,443-bp deletion; truncated adeK fused to PAP2
  • C (Mechanism) Unequal homologous recombination between microhomologous sequences (5′-GCTTA-3′) flanking the deletion region, excising a circular intermediate

Key annotations: Scale bar (1 kb), gene labels, recombination arrows, “AdeIJK efflux pump” functional annotation
Use in manuscript: Results section for conserved SVs; Supplementary Fig. S1 for mechanism details


Figure 2: ISAba11 Transposon Insertion Disrupting galE Conferring Colistin Resistance

Illustrates: Mobile element insertion linking genotype to colistin resistance phenotype
Panels:

  • A (Reference) Intact galE (UDP-glucose 4-epimerase) essential for LPS biosynthesis
  • B (Variant) galE interrupted by 1,101-bp ISAba11; features shown: inverted repeats (IR-L/IR-R), tnpA transposase, 5-bp target site duplication (TSD: 5′-TTAAA-3′)
  • C (Mechanism) Stepwise transposition model: TnpA-mediated excision, staggered target cut, insertion with gap repair
  • D (Phenotype) Bacterial envelope schematic: LPS truncation → reduced membrane negative charge → diminished colistin binding → resistance

Key annotations: Gene coordinates, TSD highlight, colistin molecule (purple), LPS structure simplified
Use in manuscript: Central figure for resistance mechanism; ideal for main text Figure 3 or 4


Figure 3: Replication Slippage-Mediated Tandem Contraction in tRNA-Gln Array

Illustrates: Copy-number variation in a non-coding repetitive locus
Panels:

  • Top (Reference) Four tandem tRNA-Gln genes (75 bp each), total span ~438 bp
  • Middle (Variant) Three copies remaining after 198-bp contraction; one repeat unit lost
  • Bottom (Mechanism) Replication fork schematic: nascent strand slippage at repeat boundary → misalignment → skipping of one repeat unit during synthesis

Key annotations: “Microhomology-mediated slippage” callout, scale bar (100 bp), neutral evolution note
Use in manuscript: Supplementary figure for lineage markers; Methods section for SV calling validation


🎨 Design Specifications (All Figures)

Feature Specification
Style Clean vector line art, minimal shading
Color palette Professional: teal (genes), orange (variants), gray (spacers), purple (antibiotics)
Typography Sans-serif (Arial/Helvetica), English labels only
Scalability Export-ready for PDF/EPS; legible at single-column (8.5 cm) or double-column (17 cm) width
Compliance No isolate names, no proprietary data; generic “Reference” vs “Variant” labeling

📋 Suggested Figure Legends (Copy-Paste Ready)

Figure 1. Homologous recombination mediates a conserved 4.4-kb deletion disrupting the AdeIJK multidrug efflux system.
(A) Genomic context in reference strain (CP059040). (B) Variant structure after deletion, showing fusion of truncated adeK to downstream PAP2. (C) Proposed mechanism: unequal crossover between microhomologous 5-bp sequences (GCTTA) excises the intervening 4,443-bp fragment as a circular intermediate. Gene arrows indicate transcriptional orientation; scale bar, 1 kb.

Figure 2. ISAba11 insertion into galE provides a molecular basis for colistin resistance.
(A) Intact galE encodes UDP-glucose 4-epimerase, required for lipopolysaccharide (LPS) core biosynthesis. (B) In resistant isolates, a 1,101-bp ISAba11 element inserts 96 bp downstream of the galE start codon, disrupting the open reading frame. ISAba11 features: inverted repeats (IRs), transposase gene (tnpA), and 5-bp target site duplication (TSD). (C) Stepwise transposition model. (D) Phenotypic consequence: truncated LPS reduces membrane negative charge, decreasing binding of cationic colistin. Scale bar, 200 bp.

Figure 3. Replication slippage drives tandem contraction in a tRNA-Gln gene array.
(Top) Reference configuration: four identical tRNA-Gln copies in head-to-tail orientation. (Middle) Variant configuration after 198-bp contraction, reducing copy number to three. (Bottom) Molecular mechanism: DNA polymerase slippage at repeat boundaries causes misalignment and skipping of one repeat unit during synthesis. This neutral variant serves as a stable lineage marker. Scale bar, 100 bp.


Let me know if you would like:

  • Adjustments to colors, labels, or layout
  • Export in specific formats (SVG, PDF, EPS)
  • Additional panels (e.g., IGV screenshot integration, phylogenetic context)
  • German or Chinese versions for internal use

These figures are ready for integration into your manuscript or presentation. 🧬🔬

Interhost variant calling (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"

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.