Daily Archives: 2026年5月22日

DEBUG methylomic analysis of Data_Tam_DNAseq_2026_An6_BG5

🔧 Lösung: Bereits alignierte modBAM-Dateien mit nf-core/methylong v2.0.0

📊 Analyse Ihrer BAM-Datei

Gute Nachrichten: Ihre Datei An6_6mA_mapped.mod.bam ist:

  • Bereits aligniert (@SQ Header mit Chromosomen/Positionen vorhanden)
  • Enthält 6mA-Modifikationen (MM:Z:A+a… und ML:B:C Tags sichtbar)
  • Von Dorado basecalled (PN:dorado VN:2.0.0+…)

Problem: Die Pipeline versucht trotzdem, den Dorado-Aligner erneut auszuführen → Docker-Auth-Fehler.


🚨 Shell-Fehler beheben: Befehl korrekt eingeben

Die Fehler --ont_aligner: command not found entstehen, weil Multi-Line-Befehle mit \ nicht korrekt verarbeitet werden.

Option A: Als einzeiliger Befehl (empfohlen):

nextflow run nf-core/methylong -r 2.0.0 -profile docker --input samplesheet_6mA.csv --outdir methylome_out_6mA --ont_aligner minimap2 --m6a -resume -work-dir methylome_out_6mA/work

Option B: Multi-Line mit korrekter Syntax (kein Leerzeichen nach \!):

nextflow run nf-core/methylong \
  -r 2.0.0 \
  -profile docker \
  --input samplesheet_6mA.csv \
  --outdir methylome_out_6mA \
  --ont_aligner minimap2 \
  --m6a \
  -resume \
  -work-dir methylome_out_6mA/work

Option C: In ein Skript schreiben (run_methylong.sh):

#!/bin/bash
nextflow run nf-core/methylong \
  -r 2.0.0 \
  -profile docker \
  --input samplesheet_6mA.csv \
  --outdir methylome_out_6mA \
  --ont_aligner minimap2 \
  --m6a \
  -resume \
  -work-dir methylome_out_6mA/work
chmod +x run_methylong.sh && ./run_methylong.sh

⚙️ Samplesheet für bereits alignierte modBAM-Dateien

Da Ihre BAM-Datei bereits aligniert ist, gibt es zwei Ansätze:

🔹 Ansatz 1: --reset verwenden (Pipeline aligniert neu)

group,sample,path,ref,method
group1,An6,/mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/An6_6mA_mapped.mod.bam,/mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/bacass_out/Medaka/An6-unicycler-medaka_polished_genome.fa,ont
# Befehl mit --reset (entfernt alte Alignment-Info, aligniert mit minimap2 neu)
nextflow run nf-core/methylong -r 2.0.0 -profile docker --input samplesheet_6mA.csv --outdir methylome_out_6mA --ont_aligner minimap2 --reset --m6a -resume

🔹 Ansatz 2: Pipeline “austricksen” – unalignierte BAM simulieren

Wenn Sie das bestehende Alignment behalten möchten:

  1. BAM ohne Alignment-Info exportieren (behält MM/ML-Tags):

    samtools collate -Ou An6_6mA_mapped.mod.bam | \
    samtools fastq -T MM,ML - > An6_6mA_with_mods.fastq
  2. Samplesheet mit FASTQ-Pfad (Pipeline basecallt nicht, da keine Pod5/FAST5):

    group,sample,path,ref,method
    group1,An6,/pfad/zu/An6_6mA_with_mods.fastq,/pfad/zu/genome.fa,ont
  3. Pipeline mit --skip_basecalling-Ersatz starten:

    ⚠️ Da --skip_basecalling nicht existiert: Die Pipeline erkennt FASTQ ohne Basecalling-Info und springt direkt zu Alignment. Kombinieren Sie mit --ont_aligner minimap2:

    nextflow run nf-core/methylong -r 2.0.0 -profile docker --input samplesheet_6mA.csv --outdir methylome_out_6mA --ont_aligner minimap2 --m6a -resume

🧪 Schnelltest: Läuft die Pipeline mit minimap2?

# 1. Work-Directory aufräumen (falls vorheriger Run fehlschlug)
rm -rf methylome_out_6mA/work/a1/

# 2. Minimaler Testlauf (nur bis Alignment)
nextflow run nf-core/methylong \
  -r 2.0.0 \
  -profile docker \
  --input samplesheet_6mA.csv \
  --outdir methylome_out_6mA_test \
  --ont_aligner minimap2 \
  --m6a \
  -resume \
  -work-dir methylome_out_6mA_test/work \
  -entry align 2>&1 | tee test_align.log

💡 Der -entry align Parameter startet die Pipeline nur bis zum Alignment-Schritt (falls in v2.0.0 verfügbar – sonst weglassen).


📋 Checkliste vor dem Start

Prüfpunkt Befehl/Ziel
✅ Samplesheet-Spalten group,sample,path,ref,method (exakt!)
✅ Absolute Pfade Keine relativen Pfade wie ./file.bam
✅ BAM-Datei lesbar samtools quickcheck An6_6mA_mapped.mod.bam && echo "OK"
✅ MM/ML-Tags vorhanden samtools view file.bam \| head -1 \| grep -q "MM:Z" && echo "Mods OK"
✅ Docker läuft docker run hello-world
✅ Ausreichend RAM/CPU Min. 16 GB RAM, 8 CPUs empfohlen

🔄 Alternative: Manueller modkit-Pileup (ohne Nextflow)

Falls die Pipeline weiterhin Probleme macht, können Sie modkit direkt auf Ihrer BAM-Datei anwenden:

# 1. Referenz indexieren
samtools faidx /pfad/zu/genome.fa

# 2. modkit pileup für 6mA
modkit pileup sample \
  An6_6mA_mapped.mod.bam \
  methylome_out_6mA/An6_6mA.pileup.bed \
  --ref /pfad/zu/genome.fa \
  --modify-motif 6mA \
  --cpg \
  -t 8

# 3. Zu BigWig konvertieren (für IGV)
bedGraphToBigWig methylome_out_6mA/An6_6mA.pileup.bed genome.fa.sizes methylome_out_6mA/An6_6mA.bw

📦 modkit installieren: pip install modkit oder via Docker: docker pull nanoporetech/modkit


🎯 Empfohlener nächster Schritt

# 1. Samplesheet prüfen
cat samplesheet_6mA.csv

# 2. Einzeiligen Nextflow-Befehl ausführen (mit minimap2):
nextflow run nf-core/methylong -r 2.0.0 -profile docker --input samplesheet_6mA.csv --outdir methylome_out_6mA --ont_aligner minimap2 --m6a -resume -work-dir methylome_out_6mA/work

Bei weiteren Fehlern: Bitte den Inhalt von .nextflow.log teilen – dann kann ich gezielt helfen! 🚀

Genomic analysis (Data_Tam_DNAseq_2026_An7_An22_Acinetobacter_sp)

  1. Run nextflow bacass

     conda deactivate
    
     # Downlod k2_standard_08_GB_20251015.tar.gz from https://benlangmead.github.io/aws-indexes/k2#kraken2--bracken
     # Download 20190108_kmerfinder_stable_dirs.tar.gz from https://zenodo.org/records/13447056; 'tar xzf 20190108_kmerfinder_stable_dirs.tar.gz'  #The database does not work!
     # Download the kmerfinder database: https://www.genomicepidemiology.org/services/ --> https://cge.food.dtu.dk/services/KmerFinder/ --> https://cge.food.dtu.dk/services/KmerFinder/etc/kmerfinder_db.tar.gz  #The database works!
    
     # DEBUG: --kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder/bacteria/ not working!
    
     nextflow run nf-core/bacass -r 2.6.0 -profile docker --help
    
     # -- Hybrid assembly --
     nextflow run nf-core/bacass -r 2.6.0 -profile docker \
       --input samplesheet_bacass.tsv \
       --outdir bacass_out \
       --assembly_type hybrid \
       --assembler unicycler,dragonflye \
       --kraken2db /mnt/nvme1n1p1/REFs/k2_standard_08_GB_20251015.tar.gz \
       --skip_kmerfinder \
       -resume \
       -work-dir bacass_out/work
    
     # -- Short assembly --
     #Maybe BUG is from '--skip_kmerfinder for -r 2.6.0, using db in 2.5.0'
     nextflow run nf-core/bacass -r 2.5.0 -profile docker \
       --input samplesheet.tsv \
       --outdir bacass_out \
       --assembly_type short \
       --kraken2db /mnt/nvme1n1p1/REFs/k2_standard_08_GB_20251015.tar.gz \
       --kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder/bacteria/ \
       -resume \
       -work-dir bacass_out/work
    
     # Using prokka assembly since medaka was not generated!
     jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2026_An6_An7_An22_Acinetobacter_sp/bacass_out/Prokka/An7.fna
     jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2026_An6_An7_An22_Acinetobacter_sp/bacass_out/Prokka/An22.fna
  2. Species Identification: 快速筛查用 Mash → 精确分类用 GTDB-Tk → 种级验证用 FastANI,三者结合可最大限度提高物种鉴定的准确性和可解释性。

     # 1. 创建环境(推荐 mamba)
     mamba create -n gtdbtk -c conda-forge -c bioconda gtdbtk
     mamba activate gtdbtk
    
     # 2. 下载数据库(仅需首次,约 60GB)
     gtdbtk download --data_dir ./gtdb_data --release 220
    
     wget https://data.gtdb.aau.ecogenomic.org/releases/release232/232.0/auxillary_files/gtdbtk_package/full_package/gtdbtk_r232_data.tar.g
     mamba env config vars set GTDBTK_DATA_PATH="/mnt/nvme4n1p1/gtdb_data/release232"
     # 先退出当前环境,再重新激活
     mamba deactivate
     mamba activate gtdbtk
    
     # 验证环境变量是否加载成功
     echo $GTDBTK_DATA_PATH
     # 应输出:/mnt/nvme4n1p1/gtdb_data/release232
    
     # 3. 运行分类(你提供的命令 + 实用参数)
     gtdbtk classify_wf \
       --genome_dir ./bacass_out/Prokka \
       --out_dir gtdb_out \
       --cpus 64 \
       --extension .fna \
       --prefix mygenome
    
     # 4. 查看结果
     cat gtdb_out/classify/mygenome.bac120.summary.tsv   # 细菌结果
    
     #For An7
     user_genome classification  closest_genome_reference    closest_genome_reference_radius closest_genome_taxonomy closest_genome_ani  closest_genome_af
     An7 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__Acinetobacter harbinensis  GCF_000816495.1 95  d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__Acinetobacter harbinensis  97.43   0.882
    
     #For An22
     user_genome classification  closest_genome_reference    closest_genome_reference_radius closest_genome_taxonomy closest_genome_ani  closest_genome_af
     An22    d__Bacteria;p__Actinomycetota;c__Actinomycetes;o__Actinomycetales;f__Micrococcaceae;g__Arthrobacter;s__Arthrobacter sp024124825 GCF_029964055.1 95  d__Bacteria;p__Actinomycetota;c__Actinomycetes;o__Actinomycetales;f__Micrococcaceae;g__Arthrobacter;s__Arthrobacter sp024124825 99.23   0.929
    
     other_related_references(genome_id,species_name,radius,ANI,AF)
     GCA_052245515.1, s__Arthrobacter sp052245515, 95.0, 83.71, 0.177;
     GCF_020532825.1, s__Arthrobacter sp020532825, 95.0, 85.21, 0.268;
     GCF_937873245.1, s__Arthrobacter sp937873245, 95.0, 85.74, 0.21;
     GCF_009928425.1, s__Arthrobacter sp009928425, 95.0, 86.58, 0.356;
     GCA_963698285.1, s__Arthrobacter sp963698285, 95.0, 83.14, 0.159;
     GCF_020532805.1, s__Arthrobacter sp020532805, 95.0, 83.75, 0.189;
     GCF_001512305.1, s__Arthrobacter sp001512305, 95.0, 83.7, 0.199;
     GCF_001750145.1, s__Arthrobacter sp001750145, 95.0, 84.97, 0.268;
     GCF_019977335.1, s__Arthrobacter sp019977335, 95.0, 84.61, 0.231;
     GCA_035466775.1, s__Arthrobacter sp035466775, 95.0, 85.13, 0.243;
     GCA_035467435.1, s__Arthrobacter sp035467435, 95.0, 86.43, 0.25;
     GCF_001422645.1, s__Arthrobacter sp001422645, 95.0, 84.35, 0.224;
     GCF_000427315.1, s__Arthrobacter sp000427315, 95.0, 87.23, 0.32;
     GCA_039636775.1, s__Arthrobacter sp039636775, 95.0, 83.78, 0.217;
     GCF_029960645.1, s__Arthrobacter sp029960645, 95.0, 84.79, 0.253;
     GCF_031456935.1, s__Arthrobacter ginsengisoli, 95.0, 84.68, 0.223;
     GCF_052113365.1, s__Arthrobacter sp052113365, 95.0, 84.71, 0.234;
     GCA_036390955.1, s__Arthrobacter sp036390955, 95.0, 86.36, 0.167;
     GCF_030812335.1, s__Arthrobacter oxydans_B, 95.0, 85.33, 0.273;
     GCF_040547365.1, s__Arthrobacter sp040547365, 95.0, 85.47, 0.29;
     GCF_007679325.1, s__Arthrobacter sp007679325, 95.0, 84.42, 0.219;
     GCF_040547025.1, s__Arthrobacter sp040547025, 95.0, 85.41, 0.318;
     GCF_030433895.1, s__Arthrobacter sp030433895, 95.0, 84.8, 0.252;
     GCF_050157025.1, s__Arthrobacter sp050157025, 95.0, 82.71, 0.153;
     GCF_040547005.1, s__Arthrobacter sp040547005, 95.0, 83.22, 0.151;
     GCA_034376805.1, s__Arthrobacter sp034376805, 95.0, 85.48, 0.304;
     GCA_028370155.1, s__Arthrobacter sp028370155, 95.0, 84.56, 0.235
  3. Antimicrobial resistance gene profiling and Resistome and Virulence Profiling with Abricate and RGI (Reisistance Gene Identifier)

     conda activate /home/jhuang/miniconda3/envs/bengal3_ac3
     abricate --list
    
     conda deactivate
    
     ENV_NAME=/home/jhuang/miniconda3/envs/bengal3_ac3 \
     ASM=bacass_out/Prokka/An22.fna \
     SAMPLE=An22 \
     OUTDIR=resistome_virulence_An22 \
     MINID=70 MINCOV=50 \
     THREADS=32 \
     ~/Scripts/run_abricate_resistome_virulome_one_per_gene.sh
    
     #ABRicate thresholds: MINID=80 MINCOV=60
     Database        Hit_lines       File
     MEGARes 0       resistome_virulence_An7/raw/An7.megares.tab
     CARD    0       resistome_virulence_An7/raw/An7.card.tab
     ResFinder       0       resistome_virulence_An7/raw/An7.resfinder.tab
     VFDB    0       resistome_virulence_An7/raw/An7.vfdb.tab
    
     #ABRicate thresholds: MINID=70 MINCOV=50
     Database        Hit_lines       File
     MEGARes 5       resistome_virulence_An7/raw/An7.megares.tab
     CARD    5       resistome_virulence_An7/raw/An7.card.tab
     ResFinder       0       resistome_virulence_An7/raw/An7.resfinder.tab
     VFDB    3       resistome_virulence_An7/raw/An7.vfdb.tab
    
     Database        Hit_lines       File
     MEGARes 2       resistome_virulence_An22/raw/An22.megares.tab
     CARD    1       resistome_virulence_An22/raw/An22.card.tab
     ResFinder       0       resistome_virulence_An22/raw/An22.resfinder.tab
     VFDB    2       resistome_virulence_An22/raw/An22.vfdb.tab
    
     conda activate /home/jhuang/miniconda3/envs/bengal3_ac3
     #NEED_TO_ADAPT: OUTDIR = Path("resistome_virulence_An7")
     #NEED_TO_ADAPT: SAMPLE = "An7"
     python ~/Scripts/merge_amr_sources_by_gene.py
    
     python ~/Scripts/export_resistome_virulence_to_excel_py36.py \
       --workdir resistome_virulence_An22 \
       --sample An22 \
       --out Resistome_Virulence_An22.xlsx
     # Delete the column 'COVERAGE_MAP' in all 'Raw_*' sheets
  4. Report

Dear XXXX,

Please find below a summary of genomic analyses for samples An7 and An22.

1. Species Identification

Sample An7: Acinetobacter harbinensis ✅ Confirmed

Parameter Value Interpretation
Closest Reference GCF_000816495.1 Type strain of A. harbinensis
ANI 97.43% ✅ Well above 95% species threshold
AF (Alignment Fraction) 0.882 ✅ 88.2% of genome aligns; ANI estimate is robust
Final Taxonomy d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__Acinetobacter harbinensis Consistent with genomic expectations

🟢 Conclusion: An7 is confidently assigned to Acinetobacter harbinensis.


Sample An22: Arthrobacter sp. strain An22 🟡 Potential Novel Species

Parameter Value Interpretation
Closest Reference GCF_029964055.1 (Arthrobacter sp024124825) 🟡 Unclassified candidate species
ANI 99.23% ✅ Highly similar to unclassified reference
AF (Alignment Fraction) 0.929 ✅ Reliable ANI estimate
Final Taxonomy d__Bacteria;p__Actinomycetota;c__Actinomycetes;o__Actinomycetales;f__Micrococcaceae;g__Arthrobacter;s__Arthrobacter sp024124825 Clear genus assignment; species-level novelty

Comparison with Named Arthrobacter Species:

Reference Species ANI (%) AF Same Species?
A. ginsengisoli (GCF_031456935.1) 84.68 0.223 ❌ ANI < 95%
A. oxydans B (GCF_030812335.1) 85.33 0.273 ❌ ANI < 95%
A. sp000427315 (GCF_000427315.1) 87.23 0.320 ❌ (highest among named/unclassified)
A. sp035467435 (GCA_035467435.1) 86.43 0.250
A. sp036390955 (GCA_036390955.1) 86.36 0.167
A. sp009928425 (GCF_009928425.1) 86.58 0.356
A. sp040547365 (GCF_040547365.1) 85.47 0.290
A. sp040547025 (GCF_040547025.1) 85.41 0.318
A. sp034376805 (GCA_034376805.1) 85.48 0.304
A. sp020532825 (GCF_020532825.1) 85.21 0.268
A. sp035466775 (GCA_035466775.1) 85.13 0.243
A. sp052113365 (GCF_052113365.1) 84.71 0.234
A. sp029960645 (GCF_029960645.1) 84.79 0.253
A. sp019977335 (GCF_019977335.1) 84.61 0.231
A. sp030433895 (GCF_030433895.1) 84.80 0.252
A. sp028370155 (GCA_028370155.1) 84.56 0.235
A. sp001750145 (GCF_001750145.1) 84.97 0.268
A. sp001422645 (GCF_001422645.1) 84.35 0.224
A. sp039636775 (GCA_039636775.1) 83.78 0.217
A. sp020532805 (GCF_020532805.1) 83.75 0.189
A. sp052245515 (GCA_052245515.1) 83.71 0.177
A. sp001512305 (GCF_001512305.1) 83.70 0.199
A. sp040547005 (GCF_040547005.1) 83.22 0.151
A. sp963698285 (GCA_963698285.1) 83.14 0.159
A. sp050157025 (GCF_050157025.1) 82.71 0.153
A. sp937873245 (GCF_937873245.1) 85.74 0.210

🟡 Conclusion: An22 shows >99% ANI to an unclassified Arthrobacter reference genome (GCF_029964055.1) but <86% ANI to all named Arthrobacter species (including A. ginsengisoli and A. oxydans). This supports An22 representing a candidate novel species, tentatively labeled Arthrobacter sp. strain An22.


2. AMR Genes Summary

An7 (A. harbinensis): 6 genes detected (CARD/MEGARes consensus)

  • adeIJK (RND efflux pump complex) → multidrug resistance (carbapenems, cephalosporins, fluoroquinolones, macrolides, tetracyclines, etc.)
  • abeM (MATE efflux pump) → fluoroquinolones, disinfecting agents & antiseptics
  • LpsB → intrinsic resistance to colistin and other peptide antibiotics
  • MEXT → RND efflux regulator (multi-compound & biocide resistance)

An22 (Arthrobacter sp. strain An22): 3 genes detected

  • rpoB mutants (CARD) → rifamycin resistance (mutations in rifampicin-binding pocket)
  • MTRAD (MEGARes) → multi-drug RND efflux regulator
  • PARY (MEGARes) → aminocoumarin-resistant DNA topoisomerase (aminocoumarin resistance)

📝 Note: Efflux regulators (MEXT, MTRAD) and intrinsic/target-modification genes are frequently observed in environmental Arthrobacter/Acinetobacter isolates. Phenotypic AST validation is recommended if clinical or biotechnological applications are planned.


3. Virulence Factors (VFDB)

Sample Hits Key Genes Implication
An7 3 htpB (Hsp60), katA (catalase), pilT (twitching motility) Stress survival, oxidative defense, adhesion/biofilm formation
An22 2 icl (isocitrate lyase), ideR (iron-dependent regulator) Metabolic adaptation (glyoxylate shunt), iron homeostasis & potential persistence

4. Methylome Data

“Could you please clarify if the datasets include methylome data?”

Yes – Datasets include POD5 files (Oxford Nanopore) containing raw signal data for base modification detection. Methylome analysis is in progress.


5. Attachments

  • Resistome_Virulence_An7.xlsx – Detailed AMR/virulence tables for A. harbinensis An7
  • Resistome_Virulence_An22.xlsx – Detailed AMR/virulence tables for Arthrobacter sp. strain An22

Each file includes CARD/MEGARes/ResFinder annotations and VFDB virulence factors (%ID, coverage, genomic coordinates, and strand orientation).

Please let me know if you need further breakdowns or phenotypic correlation analysis.

Best, YYYY

Genomic analysis (Data_Tam_DNAseq_2026_An6_BG5)

  1. cat longreads

./cat_longreads.sh

  1. Run unicycler

     conda activate /home/jhuang/miniconda3/envs/trycycler
    
     unicycler -1 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/An6/An6_L1_1.clean.rd.fq.gz -2 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/An6/An6_L1_2.clean.rd.fq.gz -l /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J003/Release-X101SC26036392-Z01-J003-20260513_01/Data-X101SC26036392-Z01-J003/An6/2157_4C_PBK79106_7ec05c46/merged_An6_longreads.fastq.gz --mode normal -t 100 -o An6_unicycler_normal
    
     unicycler -1 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/BG5/BG5_L1_1.clean.rd.fq.gz -2 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/BG5/BG5_L1_2.clean.rd.fq.gz -l /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J003/Release-X101SC26036392-Z01-J003-20260513_01/Data-X101SC26036392-Z01-J003/BG5/2157_4C_PBK79106_7ec05c46/merged_BG5_longreads.fastq.gz --mode normal -t 100 -o BG5_unicycler_normal
    
     unicycler -1 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/An6/An6_L1_1.clean.rd.fq.gz -2 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/An6/An6_L1_2.clean.rd.fq.gz -l /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J003/Release-X101SC26036392-Z01-J003-20260513_01/Data-X101SC26036392-Z01-J003/An6/2157_4C_PBK79106_7ec05c46/merged_An6_longreads.fastq.gz --mode conservative -t 100 -o An6_unicycler_conservative
    
     unicycler -1 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/BG5/BG5_L1_1.clean.rd.fq.gz -2 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/BG5/BG5_L1_2.clean.rd.fq.gz -l /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J003/Release-X101SC26036392-Z01-J003-20260513_01/Data-X101SC26036392-Z01-J003/BG5/2157_4C_PBK79106_7ec05c46/merged_BG5_longreads.fastq.gz --mode conservative -t 100 -o BG5_unicycler_conservative
    
     unicycler -1 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/An6/An6_L1_1.clean.rd.fq.gz -2 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/An6/An6_L1_2.clean.rd.fq.gz -l /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J003/Release-X101SC26036392-Z01-J003-20260513_01/Data-X101SC26036392-Z01-J003/An6/2157_4C_PBK79106_7ec05c46/merged_An6_longreads.fastq.gz --mode bold -t 100 -o An6_unicycler_bold
    
     unicycler -1 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/BG5/BG5_L1_1.clean.rd.fq.gz -2 /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J002/clean_data/BG5/BG5_L1_2.clean.rd.fq.gz -l /mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/X101SC26036392-Z01-J003/Release-X101SC26036392-Z01-J003-20260513_01/Data-X101SC26036392-Z01-J003/BG5/2157_4C_PBK79106_7ec05c46/merged_BG5_longreads.fastq.gz --mode bold -t 100 -o BG5_unicycler_bold
  2. Run nextflow bacass

     conda deactivate
    
     # Downlod k2_standard_08_GB_20251015.tar.gz from https://benlangmead.github.io/aws-indexes/k2#kraken2--bracken
     # Download 20190108_kmerfinder_stable_dirs.tar.gz from https://zenodo.org/records/13447056; 'tar xzf 20190108_kmerfinder_stable_dirs.tar.gz'  #The database does not work!
     # Download the kmerfinder database: https://www.genomicepidemiology.org/services/ --> https://cge.food.dtu.dk/services/KmerFinder/ --> https://cge.food.dtu.dk/services/KmerFinder/etc/kmerfinder_db.tar.gz  #The database works!
    
     # DEBUG: --kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder/bacteria/ not working!
    
     nextflow run nf-core/bacass -r 2.6.0 -profile docker --help
     nextflow run nf-core/bacass -r 2.6.0 -profile docker \
       --input samplesheet_bacass.tsv \
       --outdir bacass_out \
       --assembly_type hybrid \
       --assembler unicycler,dragonflye \
       --kraken2db /mnt/nvme1n1p1/REFs/k2_standard_08_GB_20251015.tar.gz \
       --skip_kmerfinder \
       -resume \
       -work-dir bacass_out/work
    
     #We can skip kmerfinder since kraken2 do similar function:
     功能,KmerFinder,Kraken2,您的需求
     物种鉴定,✅ 基于 k-mer 频率,✅ 基于 k-mer 分类,二者选一即可
     污染筛查,✅ 可检测外源序列,✅ 可检测外源序列,✅ 有 Kraken2 足够
     数据库大小,~32 GB,~8 GB,Kraken2 更轻量
     运行速度,较慢,较快,Kraken2 更快
     细菌特异性,✅ 针对细菌优化,✅ 支持细菌,二者均可
    
     # Using Polished genomes for downstream analyses
     jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2026_An6_BG5/bacass_out/Medaka$ grep ">" An6-unicycler-medaka_polished_genome.fa
     jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2026_An6_BG5/bacass_out/Medaka$ grep ">" BG5-unicycler-medaka_polished_genome.fa
  3. Species Identification: 快速筛查用 Mash → 精确分类用 GTDB-Tk → 种级验证用 FastANI,三者结合可最大限度提高物种鉴定的准确性和可解释性。

🔬 主流物种鉴定工具对比

工具 适用对象 核心原理 优势 局限性
GTDB-Tk(CHOSEN) [[12]] 细菌/古菌 基于120+个单拷贝标记基因的系统发育分析 分类标准客观(基于GTDB数据库),分辨率高,适合新物种发现 仅支持原核生物,计算量较大
Mash / Mash Screen [[35]] 所有生物 MinHash算法快速估算基因组距离(ANI近似) 速度极快(秒级),可筛查污染,支持参考库自定义 分辨率依赖参考库完整性,对远缘物种区分有限
Kraken2 [[25]] 所有生物 k-mer + LCA(最低共同祖先)分类 速度快,支持自定义数据库,可处理混合样本 内存需求高(标准库~30GB),假阳性需置信度过滤
FastANI [[36]] 细菌/古菌 全基因组平均核苷酸一致性(ANI)计算 金标准方法,95-96% ANI ≈ 同种,结果可解释性强 需两两比对,大规模筛查较慢
NCBI BLAST+ 16S/全基因组 [[2]] 所有生物 序列相似性比对 数据库最全,结果直观,适合初步筛查 16S分辨率有限(种内难区分),全基因组BLAST慢
    # 1. 创建环境(推荐 mamba)
    mamba create -n gtdbtk -c conda-forge -c bioconda gtdbtk
    mamba activate gtdbtk

    # 2. 下载数据库(仅需首次,约 60GB)
    gtdbtk download --data_dir ./gtdb_data --release 220

    wget https://data.gtdb.aau.ecogenomic.org/releases/release232/232.0/auxillary_files/gtdbtk_package/full_package/gtdbtk_r232_data.tar.g
    mamba env config vars set GTDBTK_DATA_PATH="/mnt/nvme4n1p1/gtdb_data/release232"
    # 先退出当前环境,再重新激活
    mamba deactivate
    mamba activate gtdbtk

    # 验证环境变量是否加载成功
    echo $GTDBTK_DATA_PATH
    # 应输出:/mnt/nvme4n1p1/gtdb_data/release232

    # 3. 运行分类(你提供的命令 + 实用参数)
    gtdbtk classify_wf \
      --genome_dir ./bacass_out/Medaka \
      --out_dir gtdb_out \
      --cpus 64 \
      --extension .fa \
      --prefix mygenome

    # 4. 查看结果
    cat gtdb_out/classify/mygenome.bac120.summary.tsv   # 细菌结果

        #For An6:
        #closest_genome_reference   closest_genome_reference_radius closest_genome_taxonomy closest_genome_ani  closest_genome_af
        #GCF_000816495.1    95  d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__Acinetobacter harbinensis  97.41   0.892

        #For BG5:
        #closest_genome_reference   closest_genome_reference_radius closest_genome_taxonomy closest_genome_ani  closest_genome_af
        #GCF_040547305.1    95  d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Sphingobacteriales;f__Sphingobacteriaceae;g__Pedobacter;s__Pedobacter sp040547305 98.45   0.894
        #other_related_references(genome_id,species_name,radius,ANI,AF)
        #GCF_040822065.1, s__Pedobacter sp040822065, 95.0, 89.17, 0.499;
        #GCF_014200595.1, s__Pedobacter cryoconitis_C, 95.0, 89.48, 0.577;
        #GCF_003259615.1, s__Pedobacter cryoconitis, 95.0, 89.22, 0.556;
        #GCF_040026395.1, s__Pedobacter lusitanus, 95.0, 83.93, 0.167;
        #GCA_014302835.1, s__Pedobacter sp014302835, 95.0, 84.24, 0.161;
        #GCF_014200605.1, s__Pedobacter cryoconitis_B, 95.0, 84.47, 0.194;
        #GCF_001590605.1, s__Pedobacter cryoconitis_A, 95.0, 91.97, 0.663
  1. Using https://www.bv-brc.org/app/ComprehensiveGenomeAnalysis to annotate the genome using scaffolded results from bacass. ComprehensiveGenomeAnalysis provides comprehensive overview of the data.

  2. Antimicrobial resistance gene profiling and Resistome and Virulence Profiling with Abricate and RGI (Reisistance Gene Identifier)

     # Table 4. Specialty Genes
     #     Source    Genes
     #     NDARO     1
     # Antibiotic Resistance     CARD    15
     # Antibiotic Resistance     PATRIC  55
     # Drug Target   TTD     38
     # Metal Resistance  BacMet  29
     # Transporter   TCDB    250
     # Virulance factor  VFDB    33
    
     # https://www.genomicepidemiology.org/services/
     # https://genepi.dk/
    
     conda activate /home/jhuang/miniconda3/envs/bengal3_ac3
     abricate --list
     #DATABASE        SEQUENCES       DBTYPE  DATE
     #vfdb    2597    nucl    2025-Oct-22
     #resfinder       3077    nucl    2025-Oct-22
     #argannot        2223    nucl    2025-Oct-22
     #ecoh    597     nucl    2025-Oct-22
     #megares 6635    nucl    2025-Oct-22
     #card    2631    nucl    2025-Oct-22
     #ecoli_vf        2701    nucl    2025-Oct-22
     #plasmidfinder   460     nucl    2025-Oct-22
     #ncbi    5386    nucl    2025-Oct-22
     abricate-get_db  --list #Choices: argannot bacmet2 card ecoh ecoli_vf megares ncbi plasmidfinder resfinder vfdb victors (default '').
    
     # CARD
     abricate-get_db --db card
     # MEGARes (automatically install, if error try MANUAL install as below)
     abricate-get_db --db megares
     # MANUAL install
     wget -O megares_database_v3.00.fasta \
       "https://www.meglab.org/downloads/megares_v3.00/megares_database_v3.00.fasta"
     #wget -O megares_drugs_database_v3.00.fasta \
       "https://www.meglab.org/downloads/megares_v3.00/megares_drugs_database_v3.00.fasta"
    
     # 1) Define dbdir (adjust to your env; from your logs it's inside the conda env)
     DBDIR=/home/jhuang/miniconda3/envs/bengal3_ac3/db
     # 2) Create a custom db folder for MEGARes v3.0
     mkdir -p ${DBDIR}/megares_v3.0
     # 3) Copy the downloaded MEGARes v3.0 nucleotide FASTA to 'sequences'
     cp megares_database_v3.00.fasta ${DBDIR}/megares_v3.0/sequences
     # 4) Build ABRicate indices
     abricate --setupdb
    
     #abricate-get_db --setupdb
     abricate --list | egrep 'card|megares'
     abricate --list | grep -i megares
    
     conda deactivate
     chmod +x run_abricate_resistome_virulome_one_per_gene.sh
     ENV_NAME=/home/jhuang/miniconda3/envs/bengal3_ac3 \
     ASM=BG5-unicycler-medaka_polished_genome.fa \
     SAMPLE=BG5 \
     OUTDIR=resistome_virulence_BG5 \
     MINID=70 MINCOV=50 \
     THREADS=32 \
     ./run_abricate_resistome_virulome_one_per_gene.sh
    
     #ABRicate thresholds: MINID=70 MINCOV=50
     Database        Hit_lines       File
     MEGARes 5       resistome_virulence_An6/raw/An6.megares.tab
     CARD    5       resistome_virulence_An6/raw/An6.card.tab
     ResFinder       0       resistome_virulence_An6/raw/An6.resfinder.tab
     VFDB    4       resistome_virulence_An6/raw/An6.vfdb.tab
    
     Database        Hit_lines       File
     MEGARes 3       resistome_virulence_BG5/raw/BG5.megares.tab
     CARD    2       resistome_virulence_BG5/raw/BG5.card.tab
     ResFinder       1       resistome_virulence_BG5/raw/BG5.resfinder.tab
     VFDB    0       resistome_virulence_BG5/raw/BG5.vfdb.tab
    
     #ABRicate thresholds: MINID=80 MINCOV=60
     Database        Hit_lines       File
     MEGARes 0       resistome_virulence_An6/raw/An6.megares.tab
     CARD    0       resistome_virulence_An6/raw/An6.card.tab
     ResFinder       0       resistome_virulence_An6/raw/An6.resfinder.tab
     VFDB    0       resistome_virulence_An6/raw/An6.vfdb.tab
    
     conda activate /home/jhuang/miniconda3/envs/bengal3_ac3
    
     #Adapted the parameters in merge_amr_sources_by_gene.py
     OUTDIR = Path("resistome_virulence_An6") or "resistome_virulence_BG5"
     SAMPLE = "An6" or "BG5"
     python merge_amr_sources_by_gene.py
    
     python export_resistome_virulence_to_excel_py36.py \
       --workdir resistome_virulence_BG5 \
       --sample BG5 \
       --out Resistome_Virulence_BG5.xlsx
     # Delete the column 'COVERAGE_MAP' in all 'Raw_*' sheets
    
     Methods sentence (AMR + virulence)
    
     AMR genes were identified by screening the genome assembly with ABRicate against the MEGARes and ResFinder databases, using minimum identity and coverage thresholds of X% and Y%, respectively. CARD-based AMR determinants were additionally predicted using RGI (Resistance Gene Identifier) to leverage curated resistance models. Virulence factors were screened using ABRicate against VFDB under the same thresholds.
    
     Replace X/Y with your actual values (e.g., 90/60) or state “default parameters” if you truly used defaults.
    
     Table 2 caption (AMR)
    
     Table 2. AMR gene profiling of the genome assembly. Hits were detected using ABRicate (MEGARes and ResFinder) and RGI (CARD). The presence of AMR-associated genes does not necessarily imply phenotypic resistance, which may depend on allele type, genomic context/expression, and/or SNP-mediated mechanisms; accordingly, phenotype predictions (e.g., ResFinder) should be interpreted cautiously.
    
     Table 3 caption (virulence)
    
     Table 3. Virulence factor profiling of the genome assembly based on ABRicate screening against VFDB, reporting loci with sequence identity and coverage above the specified thresholds.

Species identification

感谢您提供更完整的数据!现在包含 closest_genome_aniclosest_genome_af 列,我可以给出精确且权威的解读


🔑 关键澄清:radius vs ANI

列名 您的数据值 真实含义
closest_genome_reference_radius 95 GTDB 分类置信度评分(非 ANI),95 表示高置信度放置
closest_genome_ani 97.41 / 98.45 真正的平均核苷酸一致性(%),用于物种界定
closest_genome_af 0.892 / 0.894 比对覆盖率,>0.8 表示结果可靠

📌 结论:您之前看到的 “95” 是分类置信度,真正的物种鉴定依据是 closest_genome_ani


📊 两个样本的精确解读

🔹 样本 An6:✅ 确认为 Acinetobacter harbinensis

参考基因组: GCF_000816495.1
分类: ... g__Acinetobacter; s__Acinetobacter harbinensis
ANI: 97.41%    ← 远高于种级阈值
AF: 0.892      ← 比对覆盖充分
指标 标准 解读
ANI 97.41% ≥95-96% = 同种 ✅ 明确属于同一物种
AF 0.892 ≥0.8 = 可靠 ✅ 结果可信
参考物种 已命名 A. harbinensis ✅ 分类明确

🟢 结论:An6 可 confidently 注释为 Acinetobacter harbinensis,可直接用于后续分析或发表。


🔹 样本 BG5:🟡 高度疑似新 Pedobacter 物种

最近参考: GCF_040547305.1 (s__Pedobacter sp040547305) ← 未命名候选种
ANI: 98.45%    ← 与该未命名参考高度相似
AF: 0.894      ← 比对可靠

📋 与已命名物种的对比:

参考物种 ANI (%) AF 是否支持同种
Pedobacter sp040547305(未命名) 98.45 0.894 ✅ 高度相似,但参考本身未命名
P. cryoconitis_A(已命名) 91.97 0.663 ❌ ANI < 95%,不支持同种
P. cryoconitis_C 89.48 0.577 ❌ 远缘
其他 Pedobacter sp. 83-89% 0.16-0.55 ❌ 不同种

🔍 判断逻辑:

  1. BG5 与未命名参考基因组高度相似(ANI 98.45%)
    → 二者极可能属于同一”候选物种”(candidate species)

  2. 但与所有已命名 Pedobacter 物种 ANI < 92%
    → 不符合现有物种的界定标准

  3. 综合结论

    🟡 BG5 很可能代表一个新的 Pedobacter 物种,建议临时命名为:
    Pedobacter sp. strain BG5Pedobacter sp. nov. BG5(若后续验证充分)


🧭 原核生物物种界定标准(权威参考)

指标 阈值 含义 您的数据
ANI ≥ 95-96% 同一物种 An6: 97.41% ✅ / BG5 vs 已命名种: <92% ❌
ANI 90-95% 同属不同种 BG5 vs P. cryoconitis_A: 91.97% 🟡
AF ≥ 0.7-0.8 ANI 计算可靠 两者均 >0.89 ✅
dDDH ≥ 70% 同一物种(补充验证) 可选做

🚀 后续验证建议(针对 BG5 新种假设)

# 1. 用 FastANI 独立验证(可选,确认 GTDB-Tk 结果)
fastani -q BG5.fa -r GCF_040547305.1.fna -o bg5_ani.txt

# 2. 计算 dDDH(数字 DNA-DNA 杂交,新种鉴定金标准之一)
#    访问: https://ggdc.dsmz.de/ggdc.php
#    上传 BG5 + 最近3个参考基因组

# 3. 构建核心基因系统发育树(验证分类位置)
gtdbtk infer --msa_file gtdb_out/align/gtdbtk.bac120.user_msa.fasta \
             --out_dir gtdb_tree \
             --cpus 32

# 4. 检查独特基因/功能(支持新种证据)
prokka --outdir bg5_anno --prefix BG5 BG5.fa
roary -e -n -p 32 -f roary_out bg5_anno/*.gff reference_*.gff

📝 报告/论文中的推荐注释

样本 推荐物种注释 备注
An6 Acinetobacter harbinensis strain An6 GTDB-Tk + ANI 97.41% + AF 0.892 支持
BG5 Pedobacter sp. strain BG5 与未命名参考基因组 ANI 98.45%,但与已命名种 ANI < 92%,建议标注为"潜在新种",待 dDDH/表型验证

⚠️ 重要提醒:正式命名新种(sp. nov.)需满足《国际原核生物命名法规》多项要求,包括:

  • 系统发育位置独特
  • 表型/生理特征差异
  • 生态位信息
  • 基因组独特性(ANI < 95% + dDDH < 70%)
  • 模式菌株保藏(如 DSMZ, CGMCC)

💡 一句话总结

An6 = 已确认的 A. harbinensis;BG5 = 高度疑似新 Pedobacter 物种,建议以 “Pedobacter sp. BG5″ 临时命名,并补充 dDDH 和系统发育分析以支持新种描述。

需要我帮您:

  1. 编写 dDDH 提交指南或 FastANI 批量脚本?
  2. 解读 GTDB-Tk 的 classification 最终分类列?
  3. 提供新物种描述论文的结构模板?

请告诉我您的下一步需求!🔬🧬



结论:GTDB-Tk 仅支持本地运行,官方从未提供在线网页版。
你提供的命令 gtdbtk classify_wf ... 是标准的命令行接口(CLI),必须在本地 Linux/macOS 服务器、工作站或云计算实例上执行。


🖥️ 为什么不能在线运行?

原因 说明
数据库庞大 GTDB Release 220+ 参考库解压后约 60~90 GB,包含数万个参考基因组、HMM 模型和系统发育树
计算密集 需运行 HMMER 比对 → MSA 比对 → 系统发育定位(pplacer/EPA-ng)→ 分类器推理,单基因组通常需 30分钟~2小时(取决于硬件)
架构设计 纯 Python CLI 工具,依赖 Linux 环境、HMMER、prodigal、pplacer 等底层二进制文件,不适合封装为轻量级 Web 服务

📦 本地运行标准流程(推荐)

# 1. 创建环境(推荐 conda)
conda create -n gtdbtk -c conda-forge -c bioconda gtdbtk
conda activate gtdbtk

# 2. 下载数据库(仅需首次,约 60GB)
    GTDB-Tk v2.7.2 requires ~56GB of external data which needs to be downloaded
    and extracted. This can be done automatically, or manually.

    Automatic:

        1. Run the command "download-db.sh" to automatically download and extract to:
                    gtdbtk download --data_dir ./gtdb_data --release 220
            /home/jhuang/mambaforge/envs/gtdbtk/share/gtdbtk-2.7.2/db/

    Manual:

        1. Manually download the latest reference data:
            wget https://data.gtdb.aau.ecogenomic.org/releases/release232/232.0/auxillary_files/gtdbtk_package/full_package/gtdbtk_r232_data.tar.gz

        2. Extract the archive to a target directory:
            tar -xvzf gtdbtk_r232_data.tar.gz -C "/mnt/nvme4n1p1/gtdbtk_r232_data" --strip 1 > /dev/null
            rm gtdbtk_r232_data.tar.gz

        3. Set the GTDBTK_DATA_PATH environment variable by running:
            conda env config vars set GTDBTK_DATA_PATH="/path/to/target/db"

# 3. 运行分类(你提供的命令 + 实用参数)
gtdbtk classify_wf \
  --genome_dir ./ \
  --out_dir gtdb_out \
  --cpus 8 \
  --extension .fna \
  --write_unclassified \
  --prefix mygenome

# 4. 查看结果
cat gtdb_out/gtdbtk.bac120.summary.tsv   # 细菌结果
cat gtdb_out/gtdbtk.ar122.summary.tsv    # 古菌结果

硬件建议:≥16 GB RAM(推荐 32 GB)、8+ 核 CPU、≥100 GB 可用磁盘。完整基因组通常 15~40 分钟 可完成。


🌐 如果必须用“在线”方式?

虽然 GTDB-Tk 本身无网页版,但以下平台可实现类似功能(注意:分类体系与 GTDB 不同):

平台 特点 局限性
KBase / BV-BRC 上传基因组 → 选择 Taxonomic Classification 工作流,自动运行分类流程 底层多用 NCBI/传统分类,非 GTDB 标准
TYGS (Type Strain Genome Server) 基于全基因组 ANI/ddH 的在线种级分类 仅限已发表模式菌株比对,速度较慢
EzBioCloud 16S/全基因组在线鉴定,界面友好 分类树基于传统表型,与 GTDB 系统发育树不一致
云服务器临时实例 AWS/GCP/阿里云开 Ubuntu 实例 → Conda 装 GTDB-Tk → 跑完销毁 成本约 $1~3/次,需基础 Linux 操作能力

⚠️ 重要提示:在线平台不会输出 GTDB-Tk 专属的 gtdb_taxonomy 字段,其种名可能与 GTDB 差异较大(例如 Escherichia coli vs Escherichia coli clade I)。


💡 针对你的完整基因组

  • GTDB-Tk 对 complete genome 鉴定准确率极高,通常可直接给出 Species 级别分类。
  • 若结果中出现 Unclassifiedsp.Candidatus,结合 FastANI < 95% 可初步支持“潜在新种”假设。
  • 建议保留 gtdbtk.bac120.summary.tsv 中的 classificationaniaf 三列作为核心证据。

需要我提供 Docker 一键运行脚本AWS 云实例 5 分钟部署指南 吗?



是的,有多种工具可以从已组装的完整基因组中鉴定物种。以下是主流工具的分类、原理和推荐使用场景:


🔬 主流物种鉴定工具对比

工具 适用对象 核心原理 优势 局限性
GTDB-Tk [[12]] 细菌/古菌 基于120+个单拷贝标记基因的系统发育分析 分类标准客观(基于GTDB数据库),分辨率高,适合新物种发现 仅支持原核生物,计算量较大
Mash / Mash Screen [[35]] 所有生物 MinHash算法快速估算基因组距离(ANI近似) 速度极快(秒级),可筛查污染,支持参考库自定义 分辨率依赖参考库完整性,对远缘物种区分有限
Kraken2 [[25]] 所有生物 k-mer + LCA(最低共同祖先)分类 速度快,支持自定义数据库,可处理混合样本 内存需求高(标准库~30GB),假阳性需置信度过滤
FastANI [[36]] 细菌/古菌 全基因组平均核苷酸一致性(ANI)计算 金标准方法,95-96% ANI ≈ 同种,结果可解释性强 需两两比对,大规模筛查较慢
NCBI BLAST+ 16S/全基因组 [[2]] 所有生物 序列相似性比对 数据库最全,结果直观,适合初步筛查 16S分辨率有限(种内难区分),全基因组BLAST慢

🚀 推荐工作流程(以细菌基因组为例)

# ① 快速初筛:用 Mash 估算最近邻物种
mash screen refseq.msh your_genome.fna > mash_results.txt
# 输出示例:0.023  0.987  Escherichia coli strain XYZ  GCF_000008865.2

# ② 精确分类:用 GTDB-Tk 获得系统发育位置
gtdbtk classify_wf --genome_dir ./ --out_dir gtdb_out --cpus 8

# ③ 验证种级归属:用 FastANI 计算与近缘种的 ANI
fastani -q your_genome.fna -r reference_genomes/*.fna -o ani_results.txt
# ANI ≥ 95-96% 通常认为属于同一物种 [[36]]

⚠️ 关键注意事项

  1. 数据库版本影响结果
    GTDB(Release 220+)与 NCBI Taxonomy 的物种划分标准不同,同一基因组可能获得不同种名[[11]]。

  2. “完整基因组” ≠ “无污染”
    即使组装完整,也可能含质粒、噬菌体或宿主污染。建议先用 Mash ScreenKraken2 筛查污染[[33]]。

  3. 新物种的判断标准

    • GTDB-Tk 若标注为 sp.Candidatus,可能为新种
    • FastANI 与所有已知种 ANI < 95%,支持新种假设[[36]]
    • 需结合表型、生态位等多证据综合判定
  4. 真核生物需特殊处理
    上述工具主要针对原核生物。真核基因组建议:

    • 先用 Kraken2 + nt/nr 库初筛
    • 再用 BUSCO 评估完整性
    • 关键基因(如COI、ITS)BLAST验证

💡 实用小技巧

  • 节省内存:用 --memory-mapping 运行 Kraken2,避免全库加载[[25]]
  • 加速 GTDB-Tk:提前下载好 GTDB 数据库,避免每次重新下载
  • 结果可视化:用 pavianKrona 展示 Kraken2 分类结果
  • 批量处理:用 snakemakenextflow 搭建自动化流程

📌 一句话总结
快速筛查用 Mash → 精确分类用 GTDB-Tk → 种级验证用 FastANI,三者结合可最大限度提高物种鉴定的准确性和可解释性。

如果您能提供基因组的大致特征(如:预估大小、GC含量、是否原核),我可以帮您进一步推荐最优工具组合!

基因存在≠耐药表型:CARD与PATRIC注释结果差异的原因解析

CARD 与 PATRIC 在耐药基因注释逻辑上的核心差异之一: The CARD not only check if the gene is there, also check if it matches the certain genotype.


🔬 CARD 不仅看“基因是否存在”,更看“是否携带已知耐药突变”

CARD 使用的 RGI(Resistance Gene Identifier)工具内置了 Protein Variant Model (PVM),专门用于检测:

  • 管家基因(如 gyrArpoBrpsLfabI 等)中的 特定点突变(SNP)或短缺失/插入
  • 这些突变已被实验证实会改变药物靶点亲和力,从而直接导致耐药表型
  • RGI 会将您的序列与 CARD 中 curated 的“耐药相关变异位点”进行比对,只有匹配到已知耐药型突变才会报阳性

📌 举例:
氟喹诺酮耐药通常不是由“获得新基因”引起,而是 gyrAparC 上特定氨基酸替换(如 Ser83Leu、Asp87Asn)。CARD 的 PVM 会精准识别这些 SNP;而仅靠“基因存在与否”的工具会漏报或误报。


🆚 与 PATRIC 的对比

维度 CARD (RGI) PATRIC/BV-BRC
检测对象 基因存在性 + 已知耐药突变(SNP/Indel) 主要基于 k-mer 匹配已知 AMR 基因序列
对 SNP 的敏感度 高(内置 PVM,专为突变型耐药设计) 低(k-mer 默认按全长/高相似度基因匹配,不自动解析功能型 SNP)
结果含义 “该基因存在 携带已验证的耐药型变异” “该基因组含有与某 AMR 基因高度相似的序列”

因此,PATRIC 报出 33 个,可能包含:

  • 完整的获得性耐药基因(如 blaTEMtetM
  • 与耐药基因部分同源但无功能的旁系同源基因
  • 管家基因的野生型序列(无耐药 SNP,但 k-mer 仍能匹配上)

而 CARD 仅报 2 个,很可能是因为:

  • 仅 2 个基因通过了 Strict/Perfect 阈值
  • 仅 2 个携带 CARD 数据库中收录的 明确致耐药的 SNP/变异型

⚠️ 需要注意的细节

  1. CARD 的 SNP 检测依赖已知数据库:若您的菌株携带新型/未收录的耐药突变,CARD 会报阴性(假阴性)。
  2. PATRIC 并非完全不检 SNP:BV-BRC 近年也引入了部分变异注释,但覆盖面和验证严谨度仍不及 CARD 的 PVM。
  3. 表型 ≠ 基因型:即使 CARD 报出耐药 SNP,仍需结合表达水平、质粒拷贝数、外排泵协同作用等综合判断实际耐药程度。

💡 实践建议

  • 若您关注 突变型耐药(如结核分枝杆菌、淋球菌、流感嗜血杆菌的靶点突变),必须依赖 CARD 的 PVM 结果
  • 若您关注 获得性耐药基因(如 β-内酰胺酶、氨基糖苷修饰酶),可交叉比对 PATRIC 与 CARD 的 PHM(同源模型)结果,取交集提高可信度。
  • 需要时可使用 rgi main --input_sequence your.fasta --output_file rgi_out --alignment_tool blastp --clean --exclude_nudge 获取严格模式结果,或添加 --include_loose 查看宽松匹配。

如需,您可以提供 CARD 输出中的 model_type(PHM/PVM)或具体基因名,我可帮您判断这 2 个结果是来自“完整耐药基因”还是“特定耐药 SNP”。

Rsync when mv interrupted and Mount /mnt/nvme3n1p1

When you run mv across different filesystems (e.g., from an external drive /media/jhuang/Elements1 to your home directory ~/DATA), Linux does not actually move files. It performs a copy followed by a delete. If interrupted, you’re left with a partial copy at the destination and the original intact at the source. mv cannot resume.

Here’s how to safely complete the transfer using rsync, which is designed to resume, verify, and handle large datasets without corruption.

🔹 Step 1: Resume & Complete with rsync

rsync will automatically skip files that were fully copied, resume partially copied files, and only transfer what’s missing.

rsync -a --info=progress2 /media/jhuang/Elements1/Data_Ute_smallRNA_7/ ~/DATA/Data_Ute_smallRNA/Data_Ute_smallRNA_7/

Flags explained:

  • -a : Archive mode (preserves permissions, timestamps, symlinks, etc.)
  • --info=progress2 : Shows a single overall progress bar + transfer speed
  • Trailing / on source ensures contents are placed inside the destination directory (matching your original mv behavior)

⚠️ If you use SSH or worry about disconnections, run this inside tmux or screen:

tmux new -s data_move
# paste the rsync command inside
# Detach with Ctrl+B, D. Reattach later with: tmux attach -t data_move

🔹 Step 2: Verify the Transfer

Once rsync finishes, verify that both directories match:

# Compare total sizes
du -sh /media/jhuang/Elements1/Data_Ute_smallRNA_7/
du -sh ~/DATA/Data_Ute_smallRNA/Data_Ute_smallRNA_7/

# Optional: Deep verification (checks checksums, slower but thorough)
rsync -a --dry-run -c /media/jhuang/Elements1/Data_Ute_smallRNA_7/ ~/DATA/Data_Ute_smallRNA/Data_Ute_smallRNA_7/

If the dry run outputs nothing, the directories are identical.

🔹 Step 3: Safely Remove the Original

Only delete the source after verification:

rm -rf /media/jhuang/Elements1/Data_Ute_smallRNA_7/

Important Notes

  1. Never use mv for cross-drive transfers > few GB. Always use rsync or cp -a.
  2. Why rsync is safe here: It uses size + modification time to decide what to copy. If a file was interrupted, its size will differ, and rsync will re-transfer it. If it’s complete, it’s skipped instantly.
  3. Filesystem warnings: If your external drive is formatted as exFAT or NTFS, you may see harmless warnings like rsync: chown failed: Operation not permitted. This is normal and doesn’t affect data integrity. You can suppress them with --no-perms --no-owner --no-group if desired.
  4. Space check: Ensure ~/DATA/ has at least 650 GB free before starting:
    df -h ~/DATA/

Let me know if you want a script to automate verification or if the destination path structure needs adjustment! ✨



    Device  Partition   Appears in /dev Mounted?    Mount Point
    nvme0n1 p1  ✅   ✅   /mnt/nvme0n1p1
    nvme1n1 p1  ✅   ✅   /mnt/nvme1n1p1
    nvme2n1 p1  ✅   ✅   /mnt/nvme2n1p1
    nvme3n1 p1  ✅   ❌   (not mounted)
    nvme4n1 p1  ✅   ❌   (not mounted)
    nvme5n1 p1  ✅   ✅   /boot/efi
    nvme5n1 p2  ✅   ✅   / (root)
    nvme6n1 p1  ✅   ✅   /mnt/nvme6n1p1

    # 1. Check filesystem type first
    sudo blkid /dev/nvme3n1p1
    sudo blkid /dev/nvme4n1p1

    # 2. Create mount points (if needed)
    sudo mkdir -p /mnt/nvme3n1p1
    sudo mkdir -p /mnt/nvme4n1p1

    # 3. Mount (adjust -t if not ext4/xfs)
    sudo mount -t auto /dev/nvme3n1p1 /mnt/nvme3n1p1
    sudo mount -t auto /dev/nvme4n1p1 /mnt/nvme4n1p1

    # 4. Verify
    df -h | grep nvme3
    df -h | grep nvme4