Genomic analysis (Data_Tam_DNAseq_2026_An7_An22_Acinetobacter_sp)

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

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

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

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

Microbial bioinformatics

Microbial bioinformatics uses computational tools to analyze genomes, track evolution, and study functions in microorganisms, including bacteria and viruses.