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

Cheking modBAM 文件格式 and 🔬Dorado `–modified-bases` 参数详解(中文版)

modBAM 文件格式完全正确!

您看到的结果 正是预期的正确格式,只是 Dorado 的标签语法与您搜索的关键词略有不同。


🔍 关键发现:MM:Z:A+a.,... 就是 6mA 标签!

您搜索 MM:Z:6mA 无结果,但 MM:Z: 有结果,这是因为 Dorado 使用单字母代码表示修饰类型

✅ 您的输出: MM:Z:A+a.,0,1,9,11,0,0,3,3,1,0,0,0,6,1,7,1,0,7,13,2,...
                │ │ ││
                │ │ │└─ 修饰概率列表 (逗号分隔)
                │ │ └─── 分隔符
                │ └───── 修饰代码: 'a' = 6mA (N6-methyladenine)
                └─────── 基础碱基: 'A' = Adenine, '+' = 正链

📋 Dorado 修饰代码对照表

单字母代码 修饰类型 全称 适用生物
a 6mA N6-methyladenine ✅ 细菌主导
m 5mC 5-methylcytosine 真核/部分细菌
4c 4mC N4-methylcytosine ✅ 细菌
5m 5mC 5-methylcytosine 通用
h 5hmC 5-hydroxymethylcytosine 哺乳动物

🔑 结论MM:Z:A+a.,... 中的 a 就是 6mA!您的文件完全正确 ✅


🔧 配套标签:ML:B:C 存储修饰概率

您输出中的 ML:B:C,247,200,16,109,... 是对应的 修饰概率数组(0-255 整数,需除以 255 转为 0-1 概率):

# 示例:第一个位点的 6mA 概率 = 247/255 ≈ 96.9%
# 第二个位点 = 200/255 ≈ 78.4%

✅ 验证您的 modBAM 完全合格

# 1. 检查是否含 @SQ 头(已比对标志)
samtools view -H An6_6mA_mapped.mod.bam | grep "^@SQ" | head -3
# ✅ 应输出参考序列信息

# 2. 确认含 6mA 修饰标签(用正确代码 'a')
samtools view An6_6mA_mapped.mod.bam | grep "MM:Z:.*+a\." | head -3
# ✅ 应输出含 "A+a." 或 "T+a." 的行

# 3. 统计 6mA 位点数量
samtools view An6_6mA_mapped.mod.bam | grep -c "MM:Z:.*+a\."
# ✅ 应输出 >0 的数字

# 4. 用 modkit 快速验证(如果已安装)
modkit summary An6_6mA_mapped.mod.bam --reference ref.fasta 2>/dev/null | head -10
# ✅ 应输出 6mA 统计摘要

🚀 现在可以运行 methylong 了!

您的 modBAM 完全符合 nf-core/methylong 的输入要求。使用以下命令:

1️⃣ 创建 samplesheet(使用精确样本名)

# samplesheet_6mA.csv
group,sample,path,ref,method
An6,An6,/mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/An6_6mA_mapped.mod.bam,/mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/An6_unicycler_normal/assembly.fasta,ont
BG5,BG5,/mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/BG5_6mA_mapped.mod.bam,/mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/BG5_unicycler_normal/assembly.fasta,ont

2️⃣ 运行 methylong(跳过 basecalling)

#!/bin/bash
# run_methylong_final.sh

set -euo pipefail

echo "🚀 运行 methylong (使用预生成 6mA modBAM)..."

nextflow run nf-core/methylong \
  -r 2.0.0 \
  -profile docker \
  --input samplesheet_6mA.csv \
  --outdir methylome_out_6mA \
  --skip_basecalling \          # ⭐ 关键: 跳过内部 Dorado
  -resume \
  -work-dir methylome_out_6mA/work

echo "✅ 提交完成!"

📊 预期输出结构

methylome_out_6mA/
├── multiqc_report.html          # 📊 综合质控报告
├── methylation_calls/
│   ├── An6.6mA.bedMethyl.gz    # 🧬 6mA 位点 (bedMethyl 格式)
│   ├── An6.6mA.bigWig          # 🎨 IGV 可视化轨道
│   └── BG5.*                   # 同上
├── motifs/
│   ├── An6_6mA_motifs.tsv      # 🎯 富集的 6mA 基序 (如 GATC)
│   └── BG5_6mA_motifs.tsv
├── alignment/                   # (跳过,因已提供 modBAM)
└── pipeline_info/              # 🔬 可重复性元数据

🇩🇪 德语快速参考(Deutsch)

# ✅ 您的 modBAM 格式正确:
• MM:Z:A+a.,... → 'a' = 6mA (Dorado 单字母代码)
• ML:B:C,... → 修饰概率 (0-255 整数)
• @SQ 头 → 已比对到参考基因组

# 🔧 验证命令:
samtools view file.bam | grep "MM:Z:.*+a\." | head -3  # 应输出 6mA 标签

# 🚀 运行 methylong:
nextflow run nf-core/methylong \
  --input samplesheet_6mA.csv \
  --skip_basecalling -resume ...

# 📊 预期输出:
• methylation_calls/*.bedMethyl.gz → 碱基级 6mA 调用
• motifs/*_6mA_motifs.tsv → 细菌限制修饰基序 (如 GATC)

⚠️ 如果仍需运行 4mC 分析

如需同时分析 4mC,重复相同流程:

# 1. 用 4mC_5mC 模型生成 modBAM
dorado basecaller \
  --modified-bases 4mC_5mC \
  --emit-moves --device cuda:0 \
  --reference ref.fasta \
  sup@v3.0.0 ./pod5_pass/ | samtools view -b - > sample_4mC_5mC.mod.bam

# 2. 创建独立 samplesheet_4mC.csv
# 3. 运行 methylong with --skip_basecalling
# 4. 后期用 bedtools 或自定义脚本合并 6mA + 4mC 结果

您的系统现在完全准备好进行甲基化分析了! 🎉

请运行上述 run_methylong_final.sh 脚本,然后分享 methylome_out_6mA/multiqc_report.htmlmotifs/ 目录的初步结果,我将帮您解读细菌 6mA 的生物学意义!🦠🧬✨



📊 核心区别:4mC_5mC vs 5mC_5hmC

参数 检测的修饰类型 主要适用生物 生物学意义
4mC_5mC N4-甲基胞嘧啶 (4mC) + 5-甲基胞嘧啶 (5mC) 细菌/古菌 限制修饰系统 (R-M)、噬菌体防御、基因调控
5mC_5hmC 5-甲基胞嘧啶 (5mC) + 5-羟甲基胞嘧啶 (5hmC) 哺乳动物/真核生物 表观遗传调控、干细胞分化、神经发育、癌症

🔑 关键结论

  • 细菌研究(如您的 Acinetobacter/Pedobacter)→ 选 4mC_5mC6mA
  • 哺乳动物研究(如人类/小鼠)→ 选 5mC_5hmC
  • 不要混用:用 5mC_5hmC 分析细菌会漏检关键的 4mC/6mA 信号!

📋 所有 --modified-bases 选项详解

--modified-bases  A space separated list of modified base codes. 
Choose from: pseU, inosine_m6A, 5mCG, pseU_2OmeU, inosine_m6A_2OmeA, 
5mCG_5hmCG, 4mC_5mC, 2OmeG, 5mC_5hmC, 5mC, m5C, m5C_2OmeC, 6mA, m6A_DRACH, m6A

🔹 胞嘧啶修饰(Cytosine Modifications)

选项 全称 化学结构 主要生物 应用场景
5mC 5-methylcytosine 5-甲基胞嘧啶 真核 + 部分细菌 通用甲基化检测
4mC_5mC N4-methylcytosine + 5-methylcytosine N4-甲基 + 5-甲基胞嘧啶 细菌主导 细菌限制修饰系统分析
5mC_5hmC 5-methylcytosine + 5-hydroxymethylcytosine 5-甲基 + 5-羟甲基胞嘧啶 哺乳动物主导 表观遗传、癌症、神经科学
5mCG 5mC in CpG context CpG 位点的 5mC 哺乳动物 CpG 岛甲基化分析
5mCG_5hmCG 5mC + 5hmC in CpG context CpG 位点的 5mC + 5hmC 哺乳动物 高精度表观调控研究
m5C alternative notation for 5mC 同 5mC 通用 兼容旧版流程
m5C_2OmeC 5mC + 2′-O-methylcytosine 5-甲基 + 2′-O-甲基胞嘧啶 真核 + 病毒 RNA 修饰或特殊 DNA 修饰

🔹 腺嘌呤修饰(Adenine Modifications)

选项 全称 化学结构 主要生物 应用场景
6mA N6-methyladenine N6-甲基腺嘌呤 细菌主导 细菌限制修饰、毒力调控、复制起始
m6A alternative notation for 6mA 同 6mA 通用 兼容旧版流程
m6A_DRACH m6A in DRACH motif DRACH 基序中的 6mA 真核 (RNA) RNA m6A 修饰检测(需配合转录组)
inosine_m6A Inosine + m6A 次黄嘌呤 + N6-甲基腺嘌呤 特殊场景 编辑位点 + 甲基化联合分析
inosine_m6A_2OmeA Inosine + m6A + 2′-O-methyladenine 三重修饰 特殊场景 高级转录组分析

🔹 尿嘧啶/核糖修饰(Uracil/RNA Modifications)

选项 全称 化学结构 主要生物 应用场景
pseU Pseudouridine 假尿嘧啶 RNA 主导 转录后修饰、tRNA/rRNA 分析
pseU_2OmeU Pseudouridine + 2′-O-methyluridine 假尿嘧啶 + 2′-O-甲基尿嘧啶 RNA 复杂 RNA 修饰谱
2OmeG 2′-O-methylguanosine 2′-O-甲基鸟苷 RNA RNA 甲基化分析

🎯 针对您项目的推荐选择

您的样本:Acinetobacter harbinensis (An6) + Pedobacter cryoconitis (BG5)

修饰类型 推荐参数 理由
6mA(首选) --modified-bases 6mA 细菌限制修饰系统核心修饰,调控毒力/防御
4mC(备选) --modified-bases 4mC_5mC 部分细菌使用 4mC 作为限制酶识别标记
组合分析 分两次运行:6mA + 4mC_5mC 全面覆盖细菌甲基化谱,结果后期合并

不推荐5mC_5hmC5mCGpseU 等真核/修饰选项(细菌中极少或不存在)


🔧 使用示例(您的项目)

方案 1: 仅检测 6mA(最常用)

dorado basecaller \
  --modified-bases 6mA \
  --emit-moves \
  --device cuda:0 \
  sup@v3.0.0 \
  ./An6_pod5_pass/ \
  > An6_6mA.mod.bam

方案 2: 检测 4mC + 5mC(如需全面分析)

dorado basecaller \
  --modified-bases 4mC_5mC \
  --emit-moves \
  --device cuda:0 \
  sup@v3.0.0 \
  ./An6_pod5_pass/ \
  > An6_4mC_5mC.mod.bam

方案 3: 分两次运行 + 后期合并(推荐)

# 第一轮: 6mA
dorado basecaller --modified-bases 6mA ... > An6_6mA.mod.bam

# 第二轮: 4mC_5mC
dorado basecaller --modified-bases 4mC_5mC ... > An6_4mC_5mC.mod.bam

# 后期用 bedtools 或自定义脚本合并结果

🇩🇪 德语快速参考(Deutsch)

# 🔑 核心区别:
• 4mC_5mC → 细菌 (4mC + 5mC) → 限制修饰系统
• 5mC_5hmC → 哺乳动物 (5mC + 5hmC) → 表观遗传调控

# 🎯 您的项目 (细菌):
✅ 首选: --modified-bases 6mA
✅ 备选: --modified-bases 4mC_5mC
❌ 避免: 5mC_5hmC, pseU, inosine_* (真核/修饰)

# 🔧 命令示例:
dorado basecaller \
  --modified-bases 6mA \
  --emit-moves --device cuda:0 \
  sup@v3.0.0 ./pod5_pass/ > output.mod.bam

⚠️ 常见误区与注意事项

误区 正确理解
“5mC_5hmC 也能检测细菌甲基化” ❌ 细菌中 5hmC 极少,会漏检关键的 6mA/4mC
“组合参数越多越好” ❌ 多修饰模型可能降低单修饰检测灵敏度;建议分次运行
“模型名称可以随意组合” ❌ 必须使用文档列出的预训练组合,如 4mC_5mC,不能自定义 6mA_4mC
“RNA 修饰参数可用于 DNA” pseUinosine_* 等专为 RNA 设计,不适用于细菌基因组 DNA

📊 修饰类型与生物系统对应速查表

🦠 细菌/古菌:
   ├── 6mA          ← 最常用 (限制修饰系统)
   ├── 4mC_5mC      ← 备选 (部分菌种)
   └── 5mC          ← 少数菌种

🧬 哺乳动物/真核:
   ├── 5mC_5hmC     ← 最常用 (表观遗传)
   ├── 5mCG_5hmCG   ← 高精度 (CpG 位点)
   └── 5mC          ← 基础检测

🧫 RNA 修饰:
   ├── pseU         ← 假尿嘧啶
   ├── inosine_m6A  ← 编辑 + 甲基化
   └── 2OmeG        ← 2'-O-甲基鸟苷

🔍 如何验证修饰检测是否成功?

# 1. 检查 BAM 文件是否含修饰标签
samtools view An6_6mA.mod.bam | head -50 | grep -o "MM:Z:[^ ]*" | sort -u
# ✅ 应输出: MM:Z:6mA,...

# 2. 用 modkit 统计修饰频率
modkit pileup An6_6mA.mod.bam --reference ref.fasta output.bed
modkit summary output.bed

# 3. 检查基序富集 (细菌关键验证)
# 在 methylong 输出中查看 motifs/An6_6mA_motifs.tsv
# ✅ 应看到细菌常见基序: GATC, CCWGG, GANTC 等

总结:对于您的细菌甲基化研究,优先使用 6mA,如需更全面分析可补充运行 4mC_5mC。避免使用真核/修饰专用参数,确保检测的生物学相关性。

需要我帮您生成 6mA4mC_5mC 的批量运行脚本吗?🦠🧬✨

系统修复指南:专注修复 NVIDIA 驱动(不涉及 methylong)

✅ 系统清理正常进行中!

您的 apt autoremove 输出显示一切正常,系统正在安全地清理旧内核和驱动文件。


📊 输出解读

✅ 正在移除的包(正常且推荐)

包类型 示例 为什么移除
旧内核头文件 linux-headers-6.8.0-94-generic 您已升级到 6.8.0-117,旧版本不再需要
旧内核镜像 linux-image-6.8.0-94-generic 释放 /boot 空间,保持系统简洁
旧 NVIDIA 固件 nvidia-firmware-535-535.288.01 已升级到 535.309.01,旧固件冗余
失败的 545 固件 nvidia-firmware-545-545.29.06 545 驱动安装失败,清理残留

✅ GRUB 更新正常

Found linux image: /boot/vmlinuz-6.8.0-117-generic  ← 当前默认内核
Found linux image: /boot/vmlinuz-6.8.0-110-generic  ← 备用内核

⚠️ 关于 packagekit.service is masked 警告

Error: GDBus.Error:org.freedesktop.systemd1.UnitMasked: Unit packagekit.service is masked.

这完全无害,只是因为我们之前禁用了 packagekit 来释放 apt 锁:

  • ✅ 不影响 apt/dpkg 功能
  • ✅ 不影响系统更新或软件安装
  • ✅ 不影响 NVIDIA 驱动或 Docker
  • ℹ️ 如果未来需要恢复图形化软件中心,可执行:sudo systemctl unmask packagekit

🔍 验证系统已完全修复

清理完成后,请运行以下命令确认系统状态:

# 1. 验证 NVIDIA 驱动
nvidia-smi

成功输出应显示

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 535.309.01   Driver Version: 535.309.01   CUDA Version: 12.2    |
| RTX 4060 Ti ... 16380MiB |
+-----------------------------------------------------------------------------+
# 2. 验证 Docker GPU 访问
docker run --rm --gpus all nvidia/cuda:11.8.0-base-ubuntu22.04 nvidia-smi

成功输出:同样显示您的 RTX 4060 Ti

# 3. (可选) 检查当前内核
uname -r
# 应输出: 6.8.0-117-generic
# 4. (可选) 检查可用磁盘空间
df -h /boot /
# 应看到 /boot 释放了 ~500MB-1GB 空间

🎉 系统修复完成确认清单

[ ] ✅ nvidia-smi 显示驱动版本 535.309.01
[ ] ✅ docker run --gpus all ... 显示显卡信息
[ ] ✅ uname -r 显示 6.8.0-117-generic
[ ] ✅ apt autoremove 成功完成,无错误
[ ] ✅ 系统可正常启动图形界面

→ 如果全部 ✅,您的系统已完全修复!🎉

🚀 下一步:您现在可以…

选项 1: 继续之前的 methylong 分析(推荐)

现在驱动正常,您可以使用更高精度的模型

# 更新 nextflow.config 使用最新 Dorado 镜像
cat > nextflow.config << 'EOF'
process {
  withName: "*DORADO*" {
    containerOptions = '--gpus all'
    memory = '24.GB'
    cpus = 8
    time = '72.h'
    // ✅ 现在可用新版镜像 (需要 CUDA 12.2+)
    container = 'nanoporetech/dorado:7.1.2'
  }
}
docker {
  enabled = true
  runOptions = '-u $(id -u):$(id -g) --gpus all'
}
EOF

# 运行 methylong (现在可用 SUP 模型追求最高精度)
nextflow run nf-core/methylong \
  -r 2.0.0 -profile docker \
  --input samplesheet_methylong.csv \
  --outdir methylome_out_6mA \
  --dorado_model sup \          # ✅ 现在可用 SUP (之前用 HAC 是保守选择)
  --dorado_modification 6mA \
  -c nextflow.config \
  -resume \
  -work-dir methylome_out_6mA/work

选项 2: 先测试其他 GPU 应用

# 测试本地 Dorado (如果已安装)
dorado --version

# 或测试其他容器
docker run --rm --gpus all pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime \
  python -c "import torch; print(f'GPU: {torch.cuda.is_available()}')"

选项 3: 系统维护(可选)

# 更新系统包(现在驱动稳定,可安全更新)
sudo apt update
sudo apt upgrade -y  # 可选: 先查看将更新的包

# 清理更多缓存
sudo apt autoclean
sudo journalctl --vacuum-time=7d  # 保留 7 天日志

🇩🇪 德语总结(Deutsch)

# ✅ autoremove 输出正常:
• 旧内核 (6.8.0-94) 被安全移除
• 旧 NVIDIA 固件被清理
• GRUB 已更新,保留 117 + 110 内核
• "packagekit masked" 警告 = 无害 (我们之前禁用的)

# 🔍 验证修复:
1️⃣ nvidia-smi → 应显示 535.309.01
2️⃣ docker run --gpus all ... → 应显示 RTX 4060 Ti
3️⃣ uname -r → 应显示 6.8.0-117-generic

# 🚀 下一步:
• 系统已修复 → 可继续 methylong 分析
• 现在可用 --dorado_model sup (最高精度)
• 或使用任何需要 GPU 的应用

⚠️ 如果验证失败(不太可能)

# 如果 nvidia-smi 仍失败:
1️⃣ 检查驱动包: dpkg -l | grep nvidia-driver
2️⃣ 重新配置: sudo dpkg --configure -a
3️⃣ 重新加载模块: 
   sudo modprobe -r nvidia nvidia_uvm nvidia_drm nvidia_modeset
   sudo modprobe nvidia
4️⃣ 如果仍失败 → 分享输出,我帮您进一步诊断

请先运行 nvidia-smi 并告诉我输出结果

如果显示 Driver Version: 535.309.01,您的系统就完全修复好了,可以随时继续任何任务!🎉🔧✨



🔧 系统修复指南:专注修复 NVIDIA 驱动(不涉及 methylong)

理解您的需求:先修复系统,再处理分析任务


📊 当前系统状态总结

✅ 磁盘状态: 驱动 535.309.01 已正确安装
✅ DKMS 状态: 模块已为内核 6.8.0-110/117 成功构建
❌ 内存状态: 旧模块仍在运行,导致版本不匹配
❌ 模块使用: 被 1412+ 进程占用 (Xorg, gnome-shell, firefox, chrome, ollama...)
❌ nvidia-smi: "Driver/library version mismatch"

根本问题:内存中的旧内核模块 ≠ 磁盘上的新驱动库


🎯 修复路径选择

由于模块被大量图形进程占用,无需重启的修复极其困难。您有两个现实选择:

路径 操作 时间 风险 推荐
🔹 路径 A: 安全重启(推荐) 保存工作 → sudo reboot ~1 分钟 🟢 极低 首选
🔹 路径 B: 强制修复(高风险) 停止图形会话 → 卸载模块 → 重载 ~10 分钟 🔴 高 ⚠️ 仅当绝对不能重启

✅ 路径 A: 安全重启(推荐,最简单可靠)

步骤 1: 保存工作并关闭应用

# 1. 保存所有文档/代码
# 2. 关闭浏览器、终端中的未完成任务
# 3. 记录当前工作目录: pwd

# 4. (可选) 创建系统状态快照
echo "📦 创建修复前状态记录..."
{
  echo "=== 修复前状态 ===" 
  echo "时间: $(date)"
  echo "内核: $(uname -r)"
  echo "驱动包: $(dpkg -l | grep nvidia-driver | awk '{print $2,$3}')"
  echo "DKMS 状态: $(dkms status 2>/dev/null | head -3)"
} > ~/nvidia_fix_backup.txt

步骤 2: 执行重启

# 重启系统
sudo reboot

步骤 3: 重启后验证

# 1. 等待系统启动完成 (约 30-60 秒)

# 2. 验证驱动状态
nvidia-smi
# ✅ 应显示:
# +-----------------------------------------------------------------------------+
# | NVIDIA-SMI 535.309.01   Driver Version: 535.309.01   CUDA Version: 12.2    |
# | RTX 4060 Ti ... 16380MiB |
# +-----------------------------------------------------------------------------+

# 3. 验证 Docker GPU 访问
docker run --rm --gpus all nvidia/cuda:11.8.0-base-ubuntu22.04 nvidia-smi
# ✅ 应同样显示您的显卡信息

# 4. (可选) 清理旧内核包
sudo apt autoremove -y

步骤 4: 系统修复完成 ✅

🎉 系统已完全修复!
• 驱动版本: 535.309.01 (最新 535 系列)
• CUDA 支持: 12.2
• Docker GPU: 正常工作
• 可安全运行任何 GPU 应用

⚠️ 路径 B: 强制修复(仅在绝对不能重启时使用)

🔴 警告:此路径会关闭图形界面,可能丢失未保存工作。仅当路径 A 不可行时考虑。

步骤 1: 切换到文本模式(关闭图形界面)

# 保存所有工作后执行:
sudo systemctl isolate multi-user.target
# 或: sudo telinit 3

# 屏幕会变黑/切换到文本登录界面 → 正常
# 通过 SSH 或 Ctrl+Alt+F3 切换到终端继续

步骤 2: 停止占用 NVIDIA 模块的进程

# 1. 停止桌面相关服务
sudo systemctl stop gdm3 2>/dev/null || \
sudo systemctl stop lightdm 2>/dev/null || \
sudo systemctl stop sddm 2>/dev/null || true

# 2. 强制终止剩余图形进程
sudo pkill -9 Xorg 2>/dev/null || true
sudo pkill -9 gnome-shell 2>/dev/null || true
sudo pkill -9 ollama 2>/dev/null || true

# 3. 等待进程释放
sleep 3

步骤 3: 验证模块可卸载

# 检查模块使用计数
lsmod | grep nvidia
# ✅ 成功标志: 第三列 (使用计数) 全为 0 或接近 0
# ❌ 如果仍有高计数 → 停止更多进程或放弃此路径

步骤 4: 卸载旧模块 + 加载新模块

# 1. 卸载旧模块 (按依赖顺序)
sudo rmmod nvidia_uvm 2>/dev/null || true
sudo rmmod nvidia_drm 2>/dev/null || true
sudo rmmod nvidia_modeset 2>/dev/null || true
sudo rmmod nvidia 2>/dev/null || true

# 2. 加载新模块
sudo modprobe nvidia
sudo modprobe nvidia_uvm
sudo modprobe nvidia_drm
sudo modprobe nvidia_modeset

# 3. 验证
nvidia-smi
# ✅ 应显示驱动版本 535.309.01

步骤 5: 恢复图形界面(如果需要)

# 恢复图形登录
sudo systemctl isolate graphical.target
# 或: sudo telinit 5

🔍 诊断脚本:一键检查修复状态

#!/bin/bash
# nvidia_system_check.sh - 系统修复状态诊断

echo "🔍 NVIDIA 系统状态检查"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"

# 1. 驱动包状态
echo -e "\n📦 驱动包:"
dpkg -l | grep nvidia-driver | awk '{printf "   %-30s %s\n", $2, $3}'

# 2. DKMS 状态
echo -e "\n🔧 DKMS 模块:"
dkms status 2>/dev/null | grep nvidia | sed 's/^/   /' || echo "   (无 DKMS 信息)"

# 3. 内核模块
echo -e "\n🧩 加载的模块:"
lsmod | grep nvidia | awk '{printf "   %-20s 大小: %8s  引用: %s\n", $1, $2, $3}'

# 4. nvidia-smi 测试
echo -e "\n📊 nvidia-smi:"
if nvidia-smi &>/dev/null; then
  version=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader 2>/dev/null)
  echo "   ✅ 成功 (驱动版本: $version)"
else
  error=$(nvidia-smi 2>&1 | head -1)
  echo "   ❌ 失败: $error"
fi

# 5. Docker GPU 测试
echo -e "\n🐳 Docker GPU:"
if docker run --rm --gpus all nvidia/cuda:11.8.0-base-ubuntu22.04 \
   nvidia-smi --query-gpu=name --format=csv,noheader 2>&1 | grep -q "RTX"; then
  echo "   ✅ 正常工作"
else
  echo "   ❌ 访问失败"
fi

echo -e "\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "💡 提示: 如果 '引用' 列数字很大 → 需要重启才能完全修复"

使用

chmod +x nvidia_system_check.sh
./nvidia_system_check.sh

🇩🇪 德语总结(Deutsch)

# 🎯 修复目标:
使系统回到稳定状态,nvidia-smi 正常工作

# ✅ 推荐路径 (A): 安全重启
1️⃣ 保存工作 → 2️⃣ sudo reboot → 3️⃣ 验证: nvidia-smi

# ⚠️ 备选路径 (B): 强制修复 (高风险)
1️⃣ sudo systemctl isolate multi-user.target
2️⃣ sudo pkill -9 Xorg gnome-shell ollama
3️⃣ sudo rmmod nvidia* → sudo modprobe nvidia
4️⃣ sudo systemctl isolate graphical.target

# 🔍 验证命令:
• nvidia-smi                    # 应显示驱动版本
• docker run --gpus all ...    # 应显示显卡名称
• ./nvidia_system_check.sh     # 完整状态报告

📋 最终建议

如果您可以安排 1 分钟重启:

# 1. 保存工作
# 2. 执行:
sudo reboot

# 3. 重启后验证:
nvidia-smi
# ✅ 系统已修复!

如果绝对不能重启:

# 1. 先运行诊断脚本了解状态:
./nvidia_system_check.sh

# 2. 如果必须立即修复,谨慎执行路径 B
# 3. 或联系系统管理员协助

# 4. 临时方案: 系统当前状态可能仍支持 Docker GPU 访问
#    (即使 nvidia-smi 失败),可稍后重启完成最终修复

🎯 下一步行动

请选择

[ ] 路径 A: 我现在可以重启 → 执行 `sudo reboot`
[ ] 路径 B: 我不能重启 → 先运行 `./nvidia_system_check.sh` 并分享输出
[ ] 需要更多帮助 → 告诉我您的具体限制条件

我会根据您的选择提供下一步指导!🔧✨

方案 2: 回滚到原驱动 535(最安全)

1. 移除损坏的 545 包(不重启)

sudo apt remove -y nvidia-driver-545 nvidia-dkms-545 nvidia-kernel-source-545

2. 重新安装原驱动 535

sudo apt install -y nvidia-driver-535

3. 修复包状态

sudo dpkg –configure -a

4. 验证

nvidia-smi

✅ 应恢复显示: Driver Version: 535.288.01

LOG

    jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2026_An6_BG5$ # 1. 移除损坏的 545 包(不重启)
    sudo apt remove -y nvidia-driver-545 nvidia-dkms-545 nvidia-kernel-source-545

    # 2. 重新安装原驱动 535
    sudo apt install -y nvidia-driver-535

    # 3. 修复包状态
    sudo dpkg --configure -a

    # 4. 验证
    nvidia-smi
    # ✅ 应恢复显示: Driver Version: 535.288.01
    Reading package lists... Done
    Building dependency tree... Done
    Reading state information... Done
    The following packages were automatically installed and are no longer required:
        dctrl-tools dkms libatomic1:i386 libdrm-amdgpu1:i386 libdrm-intel1:i386 libdrm-nouveau2:i386 libdrm-radeon1:i386 libdrm2:i386 libedit2:i386 libelf1:i386 libgl1:i386 libgl1-mesa-dri:i386
        libglapi-mesa:i386 libglvnd0:i386 libglx-mesa0:i386 libglx0:i386 libllvm15:i386 libnvidia-cfg1-545 libnvidia-common-545 libnvidia-compute-545:i386 libnvidia-decode-545
        libnvidia-decode-545:i386 libnvidia-egl-wayland1 libnvidia-egl-wayland1:i386 libnvidia-encode-545 libnvidia-encode-545:i386 libnvidia-extra-545 libnvidia-fbc1-545
        libnvidia-fbc1-545:i386 libnvidia-gl-545 libnvidia-gl-545:i386 libpciaccess0:i386 libsensors5:i386 libwayland-client0:i386 libwayland-server0:i386 libx11-xcb1:i386 libxcb-dri2-0:i386
        libxcb-dri3-0:i386 libxcb-glx0:i386 libxcb-present0:i386 libxcb-randr0:i386 libxcb-sync1:i386 libxcb-xfixes0:i386 libxshmfence1:i386 libxxf86vm1:i386 linux-headers-6.8.0-94-generic
        linux-hwe-6.8-headers-6.8.0-94 linux-hwe-6.8-tools-6.8.0-94 linux-image-6.8.0-94-generic linux-modules-6.8.0-94-generic linux-modules-extra-6.8.0-94-generic
        linux-objects-nvidia-535-6.8.0-94-generic linux-signatures-nvidia-6.8.0-94-generic linux-tools-6.8.0-94-generic nvidia-compute-utils-545 nvidia-firmware-535-535.288.01
        nvidia-firmware-545-545.29.06 nvidia-kernel-common-545 nvidia-settings nvidia-utils-545 screen-resolution-extra xserver-xorg-video-nvidia-545
    Use 'sudo apt autoremove' to remove them.
    The following packages will be REMOVED:
        nvidia-dkms-545 nvidia-driver-545 nvidia-kernel-source-545
    0 upgraded, 0 newly installed, 3 to remove and 105 not upgraded.
    2 not fully installed or removed.
    After this operation, 75,4 MB disk space will be freed.
    (Reading database ... 645079 files and directories currently installed.)
    Removing nvidia-driver-545 (545.29.06-0ubuntu0.22.04.2) ...
    Removing nvidia-dkms-545 (545.29.06-0ubuntu0.22.04.2) ...
    Removing all DKMS Modules
    Done.
    INFO:Disable nvidia
    DEBUG:Parsing /usr/share/ubuntu-drivers-common/quirks/lenovo_thinkpad
    DEBUG:Parsing /usr/share/ubuntu-drivers-common/quirks/put_your_quirks_here
    DEBUG:Parsing /usr/share/ubuntu-drivers-common/quirks/dell_latitude
    update-initramfs: deferring update (trigger activated)
    Removing nvidia-kernel-source-545 (545.29.06-0ubuntu0.22.04.2) ...
    Processing triggers for initramfs-tools (0.140ubuntu13.5) ...
    update-initramfs: Generating /boot/initrd.img-6.8.0-117-generic
    Error: GDBus.Error:org.freedesktop.systemd1.UnitMasked: Unit packagekit.service is masked.
    Reading package lists... Done
    Building dependency tree... Done
    Reading state information... Done
    The following packages were automatically installed and are no longer required:
        linux-headers-6.8.0-94-generic linux-hwe-6.8-headers-6.8.0-94 linux-hwe-6.8-tools-6.8.0-94 linux-image-6.8.0-94-generic linux-modules-6.8.0-94-generic
        linux-modules-extra-6.8.0-94-generic linux-objects-nvidia-535-6.8.0-94-generic linux-signatures-nvidia-6.8.0-94-generic linux-tools-6.8.0-94-generic nvidia-firmware-535-535.288.01
        nvidia-firmware-545-545.29.06
    Use 'sudo apt autoremove' to remove them.
    The following additional packages will be installed:
        libnvidia-cfg1-535 libnvidia-common-535 libnvidia-compute-535 libnvidia-compute-535:i386 libnvidia-decode-535 libnvidia-decode-535:i386 libnvidia-encode-535 libnvidia-encode-535:i386
        libnvidia-extra-535 libnvidia-fbc1-535 libnvidia-fbc1-535:i386 libnvidia-gl-535 libnvidia-gl-535:i386 nvidia-compute-utils-535 nvidia-dkms-535 nvidia-firmware-535-535.309.01
        nvidia-kernel-common-535 nvidia-kernel-source-535 nvidia-utils-535 xserver-xorg-video-nvidia-535
    The following packages will be REMOVED:
        libnvidia-cfg1-545 libnvidia-common-545 libnvidia-compute-545 libnvidia-compute-545:i386 libnvidia-decode-545 libnvidia-decode-545:i386 libnvidia-encode-545 libnvidia-encode-545:i386
        libnvidia-extra-545 libnvidia-fbc1-545 libnvidia-fbc1-545:i386 libnvidia-gl-545 libnvidia-gl-545:i386 nvidia-compute-utils-545 nvidia-kernel-common-545 nvidia-utils-545
        xserver-xorg-video-nvidia-545
    The following NEW packages will be installed:
        libnvidia-cfg1-535 libnvidia-common-535 libnvidia-compute-535 libnvidia-compute-535:i386 libnvidia-decode-535 libnvidia-decode-535:i386 libnvidia-encode-535 libnvidia-encode-535:i386
        libnvidia-extra-535 libnvidia-fbc1-535 libnvidia-fbc1-535:i386 libnvidia-gl-535 libnvidia-gl-535:i386 nvidia-compute-utils-535 nvidia-dkms-535 nvidia-driver-535
        nvidia-firmware-535-535.309.01 nvidia-kernel-common-535 nvidia-kernel-source-535 nvidia-utils-535 xserver-xorg-video-nvidia-535
    0 upgraded, 21 newly installed, 17 to remove and 105 not upgraded.
    Need to get 405 MB of archives.
    After this operation, 184 MB of additional disk space will be used.
    Get:1 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 libnvidia-cfg1-535 amd64 535.309.01-0ubuntu0.22.04.1 [111 kB]
    Get:2 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 libnvidia-common-535 all 535.309.01-0ubuntu0.22.04.1 [17,1 kB]
    Get:3 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted i386 libnvidia-compute-535 i386 535.309.01-0ubuntu0.22.04.1 [40,7 MB]
    Get:4 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 libnvidia-compute-535 amd64 535.309.01-0ubuntu0.22.04.1 [40,7 MB]
    Get:5 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted i386 libnvidia-decode-535 i386 535.309.01-0ubuntu0.22.04.1 [2.216 kB]
    Get:6 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 libnvidia-decode-535 amd64 535.309.01-0ubuntu0.22.04.1 [1.895 kB]
    Get:7 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 libnvidia-encode-535 amd64 535.309.01-0ubuntu0.22.04.1 [99,5 kB]
    Get:8 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted i386 libnvidia-encode-535 i386 535.309.01-0ubuntu0.22.04.1 [109 kB]
    Get:9 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 libnvidia-extra-535 amd64 535.309.01-0ubuntu0.22.04.1 [74,0 kB]
    Get:10 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted i386 libnvidia-fbc1-535 i386 535.309.01-0ubuntu0.22.04.1 [63,1 kB]
    Get:11 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 libnvidia-fbc1-535 amd64 535.309.01-0ubuntu0.22.04.1 [57,6 kB]
    Get:12 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted i386 libnvidia-gl-535 i386 535.309.01-0ubuntu0.22.04.1 [35,5 MB]
    Get:13 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 libnvidia-gl-535 amd64 535.309.01-0ubuntu0.22.04.1 [195 MB]
    Get:14 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 nvidia-compute-utils-535 amd64 535.309.01-0ubuntu0.22.04.1 [125 kB]
    Get:15 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 nvidia-kernel-source-535 amd64 535.309.01-0ubuntu0.22.04.1 [45,3 MB]
    Get:16 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 nvidia-firmware-535-535.309.01 amd64 535.309.01-0ubuntu0.22.04.1 [39,7 MB]
    Get:17 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 nvidia-kernel-common-535 amd64 535.309.01-0ubuntu0.22.04.1 [212 kB]
    Get:18 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 nvidia-dkms-535 amd64 535.309.01-0ubuntu0.22.04.1 [37,5 kB]
    Get:19 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 nvidia-utils-535 amd64 535.309.01-0ubuntu0.22.04.1 [416 kB]
    Get:20 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 xserver-xorg-video-nvidia-535 amd64 535.309.01-0ubuntu0.22.04.1 [1.600 kB]
    Get:21 http://de.archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 nvidia-driver-535 amd64 535.309.01-0ubuntu0.22.04.1 [490 kB]
    Fetched 405 MB in 4s (90,3 MB/s)            
    (Reading database ... 644518 files and directories currently installed.)
    Removing xserver-xorg-video-nvidia-545 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-cfg1-545:amd64 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-gl-545:amd64 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-gl-545:i386 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-common-545 (545.29.06-0ubuntu0.22.04.2) ...
    Removing nvidia-utils-545 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-encode-545:amd64 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-decode-545:amd64 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-encode-545:i386 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-decode-545:i386 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-compute-545:i386 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-extra-545:amd64 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-fbc1-545:i386 (545.29.06-0ubuntu0.22.04.2) ...
    Removing libnvidia-fbc1-545:amd64 (545.29.06-0ubuntu0.22.04.2) ...
    Removing nvidia-compute-utils-545 (545.29.06-0ubuntu0.22.04.2) ...
    Removing nvidia-kernel-common-545 (545.29.06-0ubuntu0.22.04.2) ...
    update-initramfs: deferring update (trigger activated)
    Removing libnvidia-compute-545:amd64 (545.29.06-0ubuntu0.22.04.2) ...
    Selecting previously unselected package libnvidia-cfg1-535:amd64.
    (Reading database ... 644334 files and directories currently installed.)
    Preparing to unpack .../00-libnvidia-cfg1-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking libnvidia-cfg1-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-common-535.
    Preparing to unpack .../01-libnvidia-common-535_535.309.01-0ubuntu0.22.04.1_all.deb ...
    Unpacking libnvidia-common-535 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-compute-535:i386.
    Preparing to unpack .../02-libnvidia-compute-535_535.309.01-0ubuntu0.22.04.1_i386.deb ...
    Unpacking libnvidia-compute-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-compute-535:amd64.
    Preparing to unpack .../03-libnvidia-compute-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking libnvidia-compute-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-decode-535:i386.
    Preparing to unpack .../04-libnvidia-decode-535_535.309.01-0ubuntu0.22.04.1_i386.deb ...
    Unpacking libnvidia-decode-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-decode-535:amd64.
    Preparing to unpack .../05-libnvidia-decode-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking libnvidia-decode-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-encode-535:amd64.
    Preparing to unpack .../06-libnvidia-encode-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking libnvidia-encode-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-encode-535:i386.
    Preparing to unpack .../07-libnvidia-encode-535_535.309.01-0ubuntu0.22.04.1_i386.deb ...
    Unpacking libnvidia-encode-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-extra-535:amd64.
    Preparing to unpack .../08-libnvidia-extra-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking libnvidia-extra-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-fbc1-535:i386.
    Preparing to unpack .../09-libnvidia-fbc1-535_535.309.01-0ubuntu0.22.04.1_i386.deb ...
    Unpacking libnvidia-fbc1-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-fbc1-535:amd64.
    Preparing to unpack .../10-libnvidia-fbc1-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking libnvidia-fbc1-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-gl-535:amd64.
    Preparing to unpack .../11-libnvidia-gl-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking libnvidia-gl-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package libnvidia-gl-535:i386.
    Preparing to unpack .../12-libnvidia-gl-535_535.309.01-0ubuntu0.22.04.1_i386.deb ...
    Unpacking libnvidia-gl-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package nvidia-compute-utils-535.
    Preparing to unpack .../13-nvidia-compute-utils-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking nvidia-compute-utils-535 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package nvidia-kernel-source-535.
    Preparing to unpack .../14-nvidia-kernel-source-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking nvidia-kernel-source-535 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package nvidia-firmware-535-535.309.01.
    Preparing to unpack .../15-nvidia-firmware-535-535.309.01_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking nvidia-firmware-535-535.309.01 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package nvidia-kernel-common-535.
    Preparing to unpack .../16-nvidia-kernel-common-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking nvidia-kernel-common-535 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package nvidia-dkms-535.
    Preparing to unpack .../17-nvidia-dkms-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking nvidia-dkms-535 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package nvidia-utils-535.
    Preparing to unpack .../18-nvidia-utils-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking nvidia-utils-535 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package xserver-xorg-video-nvidia-535.
    Preparing to unpack .../19-xserver-xorg-video-nvidia-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking xserver-xorg-video-nvidia-535 (535.309.01-0ubuntu0.22.04.1) ...
    Selecting previously unselected package nvidia-driver-535.
    Preparing to unpack .../20-nvidia-driver-535_535.309.01-0ubuntu0.22.04.1_amd64.deb ...
    Unpacking nvidia-driver-535 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up nvidia-firmware-535-535.309.01 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-fbc1-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-fbc1-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up nvidia-kernel-common-535 (535.309.01-0ubuntu0.22.04.1) ...
    Installing new version of config file /etc/modprobe.d/nvidia-graphics-drivers-kms.conf ...
    update-initramfs: deferring update (trigger activated)
    update-initramfs: Generating /boot/initrd.img-6.8.0-110-generic
    Setting up libnvidia-common-535 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-extra-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-cfg1-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up nvidia-kernel-source-535 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-gl-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-gl-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-compute-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-compute-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up nvidia-utils-535 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up nvidia-compute-utils-535 (535.309.01-0ubuntu0.22.04.1) ...
    Warning: The home dir /nonexistent you specified can't be accessed: No such file or directory
    Adding system user `nvidia-persistenced' (UID 129) ...
    Adding new group `nvidia-persistenced' (GID 137) ...
    Adding new user `nvidia-persistenced' (UID 129) with group `nvidia-persistenced' ...
    Not creating home directory `/nonexistent'.
    Setting up nvidia-dkms-535 (535.309.01-0ubuntu0.22.04.1) ...
    update-initramfs: deferring update (trigger activated)
    update-initramfs: Generating /boot/initrd.img-6.8.0-110-generic
    INFO:Enable nvidia
    DEBUG:Parsing /usr/share/ubuntu-drivers-common/quirks/lenovo_thinkpad
    DEBUG:Parsing /usr/share/ubuntu-drivers-common/quirks/put_your_quirks_here
    DEBUG:Parsing /usr/share/ubuntu-drivers-common/quirks/dell_latitude
    Loading new nvidia-535.309.01 DKMS files...
    Building for 6.8.0-110-generic 6.8.0-117-generic
    Building for architecture x86_64
    Building initial module for 6.8.0-110-generic
    Secure Boot not enabled on this system.
    Done.

    nvidia.ko:
    Running module version sanity check.
     - Original module
         - No original module exists within this kernel
     - Installation
         - Installing to /lib/modules/6.8.0-110-generic/updates/dkms/

    nvidia-modeset.ko:
    Running module version sanity check.
     - Original module
         - No original module exists within this kernel
     - Installation
         - Installing to /lib/modules/6.8.0-110-generic/updates/dkms/

    nvidia-drm.ko:
    Running module version sanity check.
     - Original module
         - No original module exists within this kernel
     - Installation
         - Installing to /lib/modules/6.8.0-110-generic/updates/dkms/

    nvidia-uvm.ko:
    Running module version sanity check.
     - Original module
         - No original module exists within this kernel
     - Installation
         - Installing to /lib/modules/6.8.0-110-generic/updates/dkms/

    nvidia-peermem.ko:
    Running module version sanity check.
     - Original module
         - No original module exists within this kernel
     - Installation
         - Installing to /lib/modules/6.8.0-110-generic/updates/dkms/

    depmod...
    Module build for kernel 6.8.0-117-generic was skipped since the
    kernel headers for this kernel does not seem to be installed.
    Setting up libnvidia-decode-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-decode-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up xserver-xorg-video-nvidia-535 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-encode-535:amd64 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up libnvidia-encode-535:i386 (535.309.01-0ubuntu0.22.04.1) ...
    Setting up nvidia-driver-535 (535.309.01-0ubuntu0.22.04.1) ...
    Processing triggers for libc-bin (2.35-0ubuntu3.13) ...
    Processing triggers for man-db (2.10.2-1) ...
    Processing triggers for initramfs-tools (0.140ubuntu13.5) ...
    update-initramfs: Generating /boot/initrd.img-6.8.0-117-generic
    Error: GDBus.Error:org.freedesktop.systemd1.UnitMasked: Unit packagekit.service is masked.
    Failed to initialize NVML: Driver/library version mismatch
    NVML library version: 535.309

merge_methylation_results (Data_Tam_DNAseq_2026_An6_BG5)

🤔 合并 vs. 分开:细菌甲基化结果管理策略

简短回答:建议采用 “原始结果分开 + 分析视图合并” 的混合策略,兼顾可重复性与分析便利性。


📊 两种策略对比

维度 🔹 分开保存 🔹 合并结果 🔹 混合策略 (推荐)
可重复性 ✅ 优秀 (参数/版本独立追踪) ❌ 较差 (合并后难追溯) ✅ 优秀 (保留原始 + 生成合并视图)
质量控制 ✅ 独立评估每种修饰质量 ❌ 混合后难定位问题源 ✅ 先独立QC,再合并高质量结果
可视化 (IGV) ❌ 需加载多个轨道 ✅ 单轨道显示所有修饰 ✅ 生成合并 bigWig + 保留原始
统计分析 ❌ 需手动关联同位置不同修饰 ✅ 直接比较 6mA/4mC 密度 ✅ 提供统一 TSV + 保留原始细节
存储效率 ❌ 部分文件重复 (如 BAM) ✅ 仅存储最终结果 ⚠️ 略多存储,但可清理中间文件
协作共享 ❌ 需说明多个目录结构 ✅ 单文件易分享 ✅ 提供”发布包” + 原始数据可选
重新分析 ✅ 仅重跑需更新的修饰类型 ❌ 需重新合并 ✅ 独立重跑 + 增量更新合并视图

🎯 推荐:混合策略工作流

graph LR
    A[POD5 输入] --> B[Run 1: 6mA];
    A --> C[Run 2: 4mC_5mC];

    B --> D[methylome_out_6mA/];
    C --> E[methylome_out_4mC/];

    D --> F[✅ 保留: 原始 bedMethyl/BAM/日志];
    E --> F;

    F --> G[🔧 合并脚本];
    G --> H[📦 发布包/];

    H --> I[merged_An6.all_mods.bedMethyl.gz];
    H --> J[merged_An6.all_mods.bigWig];
    H --> K[merged_An6.analysis_ready.tsv];
    H --> L[QC_summary.md];

    style F fill:#E8F5E9,stroke:#4CAF50
    style H fill:#FFF3E0,stroke:#FF9800

🛠️ 实用脚本:merge_methylation_results.py

#!/usr/bin/env python3
"""
🔗 merge_methylation_results.py
合并多轮 methylong 运行结果 (6mA + 4mC)

功能:
- 保留原始输出目录 (可重复性)
- 生成合并的 bedMethyl/bigWig (可视化)
- 创建分析就绪的 TSV (统计)
- 生成合并质量报告

用法:
    python .py \
      --sample An6 \
      --mods 6mA 4mC_5mC \
      --input-dirs methylome_out_6mA methylome_out_4mC \
      --output-dir merged_results/An6 \
      --reference /path/to/assembly.fasta
"""

import argparse
import gzip
import logging
import os
import sys
from pathlib import Path
from collections import defaultdict

import pandas as pd
import pyfaidx
import pyBigWig

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(message)s',
    handlers=[
        logging.FileHandler('merge_results.log'),
        logging.StreamHandler(sys.stdout)
    ]
)
logger = logging.getLogger(__name__)

def parse_bedmethyl(filepath: str, mod_label: str) -> pd.DataFrame:
    """解析 bedMethyl 文件,添加修饰类型标签"""
    logger.info(f"📥 解析 {filepath} ({mod_label})")

    records = []
    open_func = gzip.open if filepath.endswith('.gz') else open

    with open_func(filepath, 'rt') as f:
        for line in f:
            if line.startswith('#') or not line.strip():
                continue
            fields = line.strip().split('\t')
            if len(fields) < 12:
                continue

            chrom, start, end, name, score, strand = fields[0:6]
            coverage = int(fields[10])
            mod_level = float(fields[11]) if len(fields) > 11 else 0

            # 提取具体修饰类型 (从 name 或根据输入标签)
            if '6mA' in name or mod_label == '6mA':
                mod_type = '6mA'
            elif '4mC' in name or (mod_label == '4mC_5mC' and '4mC' in name):
                mod_type = '4mC'
            elif '5mC' in name or (mod_label == '4mC_5mC' and '5mC' in name):
                mod_type = '5mC'
            else:
                mod_type = mod_label.split('_')[0]  # 回退策略

            records.append({
                'chrom': chrom,
                'start': int(start),
                'end': int(end),
                'strand': strand,
                'mod_type': mod_type,
                'mod_level': mod_level,
                'coverage': coverage,
                'score': int(score),
                'source_run': mod_label,  # 追踪来源
                'original_name': name
            })

    df = pd.DataFrame(records)
    logger.info(f"✅ 提取 {len(df)} 个 {mod_label} 位点")
    return df

def merge_bedmethyl(dfs: list[pd.DataFrame], output_path: str):
    """合并多个 DataFrame 为统一 bedMethyl 格式"""
    logger.info(f"🔗 合并 {len(dfs)} 个修饰类型...")

    # 合并并排序
    merged = pd.concat(dfs, ignore_index=True)
    merged = merged.sort_values(['chrom', 'start', 'mod_type'])

    # 重建 bedMethyl 格式 (12 列)
    with gzip.open(output_path, 'wt') as f:
        for _, row in merged.iterrows():
            # name 字段: 包含修饰类型 + 概率 (便于过滤)
            name_field = f"mod_{row['mod_type']}_{row['mod_level']:.2f}"

            fields = [
                row['chrom'], str(row['start']), str(row['end']),
                name_field, str(row['score']), row['strand'],
                str(row['start']), str(row['end']), "0,0,0",
                "1", str(row['coverage']), str(row['mod_level'])
            ]
            f.write('\t'.join(fields) + '\n')

    logger.info(f"💾 保存合并 bedMethyl: {output_path} ({len(merged)} 位点)")

def create_bigwig(merged_df: pd.DataFrame, reference_fasta: str, output_path: str):
    """生成合并的 bigWig 文件 (用于 IGV 可视化)"""
    logger.info(f"🎨 生成 bigWig: {output_path}")

    # 读取染色体长度
    fasta = pyfaidx.Fasta(reference_fasta, as_raw=True)
    chrom_sizes = [(name, len(seq)) for name, seq in fasta.items()]
    fasta.close()

    # 创建 bigWig
    bw = pyBigWig.open(output_path, "w")
    bw.addHeader(chrom_sizes)

    # 按染色体和修饰类型分组写入
    for chrom, chrom_df in merged_df.groupby('chrom'):
        if chrom not in dict(chrom_sizes):
            continue

        # 为每种修饰类型创建独立轨道 (可选: 或合并为单轨道)
        for mod_type in chrom_df['mod_type'].unique():
            mod_df = chrom_df[chrom_df['mod_type'] == mod_type]

            # 创建 intervals: (start, end, value)
            intervals = list(zip(
                mod_df['start'].tolist(),
                mod_df['end'].tolist(),
                mod_df['mod_level'].tolist()
            ))

            if intervals:
                track_name = f"{chrom}_{mod_type}"
                bw.addEntries(
                    [chrom] * len(intervals),
                    [s for s, e, v in intervals],
                    ends=[e for s, e, v in intervals],
                    values=[v for s, e, v in intervals],
                    span=None, strand=None
                )

    bw.close()
    logger.info(f"✅ bigWig 创建完成")

def create_analysis_tsv(merged_df: pd.DataFrame, output_path: str):
    """生成分析就绪的 TSV (便于 R/Python 统计)"""
    logger.info(f"📊 创建分析 TSV: {output_path}")

    # 添加衍生特征
    analysis_df = merged_df.copy()
    analysis_df['position'] = analysis_df['start']  # 简化: 用 start 代表位置
    analysis_df['log_mod_level'] = analysis_df['mod_level'].apply(
        lambda x: -1 if x == 0 else x  # 避免 log(0)
    )

    # 保存
    analysis_df.to_csv(output_path, sep='\t', index=False, compression='gzip')
    logger.info(f"✅ 保存 {len(analysis_df)} 行分析数据")

def generate_qc_summary(merged_df: pd.DataFrame, output_path: str):
    """生成合并质量报告"""
    logger.info(f"📋 生成质量报告: {output_path}")

    summary = {
        'total_sites': len(merged_df),
        'by_mod_type': merged_df['mod_type'].value_counts().to_dict(),
        'by_chromosome': merged_df.groupby('chrom').size().to_dict(),
        'coverage_stats': merged_df['coverage'].describe().to_dict(),
        'mod_level_stats': merged_df['mod_level'].describe().to_dict(),
        'high_confidence_sites': len(merged_df[
            (merged_df['coverage'] >= 10) & 
            (merged_df['mod_level'] >= 0.8)
        ])
    }

    with open(output_path, 'w') as f:
        f.write("# 甲基化结果合并质量报告\n")
        f.write(f"总位点数: {summary['total_sites']:,}\n\n")

        f.write("## 按修饰类型分布\n")
        for mod, count in summary['by_mod_type'].items():
            pct = count / summary['total_sites'] * 100
            f.write(f"- {mod}: {count:,} ({pct:.1f}%)\n")

        f.write(f"\n## 高质量位点 (覆盖度≥10×, 修饰水平≥0.8)\n")
        f.write(f"{summary['high_confidence_sites']:,} ({summary['high_confidence_sites']/summary['total_sites']*100:.1f}%)\n")

    logger.info(f"✅ 质量报告保存")

def main():
    parser = argparse.ArgumentParser(description='合并 methylong 多修饰结果')
    parser.add_argument('--sample', required=True, help='样本名称 (如: An6)')
    parser.add_argument('--mods', nargs='+', required=True, 
                       help='修饰类型标签 (如: 6mA 4mC_5mC)')
    parser.add_argument('--input-dirs', nargs='+', required=True,
                       help='对应修饰的 methylong 输出目录')
    parser.add_argument('--output-dir', required=True, help='合并结果输出目录')
    parser.add_argument('--reference', required=True, help='参考基因组 FASTA')
    parser.add_argument('--min-coverage', type=int, default=5, 
                       help='最小覆盖度过滤 (默认: 5)')
    parser.add_argument('--min-mod-level', type=float, default=0.0,
                       help='最小修饰水平过滤 (默认: 0.0)')
    args = parser.parse_args()

    # 创建输出目录
    outdir = Path(args.output_dir)
    outdir.mkdir(parents=True, exist_ok=True)

    # 1. 解析各修饰类型结果
    dfs = []
    for mod_label, input_dir in zip(args.mods, args.input_dirs):
        bedmethyl_path = Path(input_dir) / 'methylation_calls' / f"{args.sample}.{mod_label.split('_')[0]}.bedMethyl.gz"

        if not bedmethyl_path.exists():
            # 尝试其他命名变体
            alt_path = Path(input_dir) / 'methylation_calls' / f"{args.sample}.bedMethyl.gz"
            if alt_path.exists():
                bedmethyl_path = alt_path
            else:
                logger.warning(f"⚠️ 未找到 bedMethyl: {bedmethyl_path}")
                continue

        df = parse_bedmethyl(str(bedmethyl_path), mod_label)

        # 应用质控过滤
        before = len(df)
        df = df[
            (df['coverage'] >= args.min_coverage) &
            (df['mod_level'] >= args.min_mod_level)
        ]
        if before > len(df):
            logger.info(f"   🔍 过滤后: {before} → {len(df)} 位点")

        dfs.append(df)

    if not dfs:
        logger.error("❌ 无有效数据可合并")
        return 1

    # 2. 合并 bedMethyl
    merged_bed = outdir / f"{args.sample}.all_mods.bedMethyl.gz"
    merge_bedmethyl(dfs, str(merged_bed))

    # 3. 生成 bigWig (可选,需要 pyBigWig)
    try:
        merged_df = pd.concat(dfs, ignore_index=True)
        merged_bw = outdir / f"{args.sample}.all_mods.bigWig"
        create_bigwig(merged_df, args.reference, str(merged_bw))
    except Exception as e:
        logger.warning(f"⚠️ bigWig 生成失败: {e}")

    # 4. 创建分析 TSV
    analysis_tsv = outdir / f"{args.sample}.analysis_ready.tsv.gz"
    create_analysis_tsv(pd.concat(dfs, ignore_index=True), str(analysis_tsv))

    # 5. 生成质量报告
    qc_report = outdir / f"{args.sample}.merge_QC.md"
    generate_qc_summary(pd.concat(dfs, ignore_index=True), str(qc_report))

    logger.info(f"\n🎉 合并完成!")
    logger.info(f"📁 输出目录: {outdir}")
    logger.info(f"📄 快速查看: cat {qc_report}")

    return 0

if __name__ == '__main__':
    sys.exit(main())

🚀 一键运行脚本:run_merge.sh

#!/bin/bash
#===============================================================================
# 🔗 run_merge.sh - 合并 6mA + 4mC 甲基化结果
#===============================================================================
set -euo pipefail

SAMPLE="An6"  # 或 BG5
REFERENCE="/mnt/md1/DATA/Data_Tam_DNAseq_2026_An6_BG5/bacass_out/${SAMPLE}/unicycler/assembly.fasta"
OUTPUT_DIR="merged_results/${SAMPLE}"

echo "🔗 合并 ${SAMPLE} 甲基化结果..."

python3 merge_methylation_results.py \
  --sample "${SAMPLE}" \
  --mods 6mA 4mC_5mC \
  --input-dirs methylome_out_6mA methylome_out_4mC \
  --output-dir "${OUTPUT_DIR}" \
  --reference "${REFERENCE}" \
  --min-coverage 10 \
  --min-mod-level 0.7

echo ""
echo "✅ 合并完成! 输出文件:"
ls -lh "${OUTPUT_DIR}/"

echo ""
echo "🎨 IGV 可视化:"
echo "   1. 加载参考基因组: ${REFERENCE}"
echo "   2. 加载轨道: ${OUTPUT_DIR}/${SAMPLE}.all_mods.bigWig"
echo "   3. 按修饰类型过滤: 右键轨道 → Filter by name → mod_6mA / mod_4mC"

echo ""
echo "📊 快速统计:"
zcat "${OUTPUT_DIR}/${SAMPLE}.analysis_ready.tsv.gz" | \
  awk -F'\t' 'NR>1 {count[$7]++} END {for (m in count) print m": "count[m]}' | sort -k2 -nr

📋 文件组织建议

project/
├── methylome_out_6mA/          # 🔹 原始 6mA 结果 (保留!)
│   ├── methylation_calls/
│   ├── motifs/
│   └── pipeline_info/
├── methylome_out_4mC/          # 🔹 原始 4mC 结果 (保留!)
│   └── ...
├── merged_results/             # 🔹 合并分析视图 (用于发表/共享)
│   ├── An6/
│   │   ├── An6.all_mods.bedMethyl.gz    # 🧬 合并 bedMethyl
│   │   ├── An6.all_mods.bigWig          # 🎨 IGV 轨道
│   │   ├── An6.analysis_ready.tsv.gz    # 📊 统计输入
│   │   └── An6.merge_QC.md              # 📋 质量报告
│   └── BG5/ (同上)
└── scripts/
    ├── merge_methylation_results.py
    └── run_merge.sh

💡 最佳实践:在论文 Methods 中注明: “6mA and 4mC were called separately using nf-core/methylong v2.0.0 with Dorado models 6mA and 4mC_5mC, respectively. Results were merged post-hoc for visualization and integrated analysis while preserving original outputs for reproducibility.”


🇩🇪 德语总结(Deutsch)

Empfohlene Strategie:

  • 🔹 Original-Outputs trennen (für Reproduzierbarkeit & QC)
  • 🔹 Merge-Views generieren (für Visualisierung & Statistik)

📁 Verzeichnis-Struktur:

methylome_out_6mA/     ← Original 6mA (behalten!)
methylome_out_4mC/     ← Original 4mC (behalten!)
merged_results/An6/    ← Analyse-ready Dateien (teilen/publishen)

🔧 Merge-Skript:

  • Erstellt: merged.bedMethyl.gz, merged.bigWig, analysis.tsv
  • Behält: mod_type, coverage, source_run Metadaten
  • Generiert: QC-Bericht für Methods-Sektion

⚠️ 注意事项

问题 解决方案
bedMethyl name 字段冲突 合并脚本自动重命名为 mod_{type}_{prob} 格式
bigWig 轨道过多 按修饰类型分组写入,IGV 中可折叠/筛选
统计时重复计数 analysis TSV 含 source_run 列,便于追溯/去重
磁盘空间 合并后可归档原始目录: tar -czf methylome_out_6mA.tar.gz methylome_out_6mA/

🎯 针对您项目的具体建议

# 1. 先完成 6mA 单轮运行 (验证流程)
nextflow run nf-core/methylong ... --dorado_modification 6mA ...

# 2. 验证 6mA 结果质量
zcat methylome_out_6mA/motifs/An6_6mA_motifs.tsv | head -5
# 应看到 GATC 等细菌基序 → 说明流程正常

# 3. 如需 4mC,运行第二轮
nextflow run nf-core/methylong ... --dorado_modification 4mC_5mC ...

# 4. 合并结果 (仅当两轮都成功)
./run_merge.sh  # 使用上方脚本

# 5. 发表准备
# - 主图: merged.bigWig in IGV (截图)
# - 附表: analysis_ready.tsv.gz (完整位点)
# - Methods: 注明分开调用 + 合并策略

需要我帮您:

  • 🔄 适配合并脚本到您的具体文件命名规范?
  • 📊 添加 6mA/4mC 共定位分析 (同一基因组位置同时存在两种修饰)?
  • 🎨 生成发表级 Figure 代码 (ggplot2 + ggbio 轨道图)?

祝您分析顺利,期待您的重要发现!🦠🧬✨