Individual files
| File | Module | Description | Rows | Size | Download |
|---|---|---|---|---|---|
| mlst_all.tsv | MLST | One row per sample: ST, status, identity, coverage, allele profile | 60,182 | 331.6 KB | ⬇ Download |
| spa_all.tsv | spa | spa type, repeat pattern, count, confidence, status | 60,182 | 356.1 KB | ⬇ Download |
| sccmec_cge_all.tsv | SCCmec | SCCmecFinder output — type, MRSA status, confidence, evidence | 60,182 | 524.1 KB | ⬇ Download |
| sccmec_rpet_all.tsv | SCCmec | sccmec output — type, subtype, mec class, ccr complexes, genes | 60,182 | 246.6 KB | ⬇ Download |
| capsule_all.tsv | Capsule | cap5/cap8 serotype, completeness, scores, gene list | 60,182 | 210.2 KB | ⬇ Download |
| agr_all.tsv | agr | agr type, group, match score, canonical agrD | 60,182 | 194.2 KB | ⬇ Download |
| metadata.tsv | Metadata | Country, isolation source, host, collection year, strain, assembly level — one row per accession | 134,299 | 3.9 MB | ⬇ Download |
| amr_all.tsv | AMR | One row per sample×gene: gene, num hits, identities | 351,938 | 1.2 MB | ⬇ Download |
| amr_gene_freq.tsv | AMR | One row per gene: num genomes, prevalence %, occurrences | 223 | 1.7 KB | ⬇ Download |
| vfdb_all.tsv | Virulence | One row per sample×gene: gene, num hits, identities | 7,422,630 | 24.6 MB | ⬇ Download |
| vfdb_gene_freq.tsv | Virulence | One row per gene: num genomes, prevalence %, occurrences | 142 | 1.3 KB | ⬇ Download |
| plasmid_all.tsv | Plasmid | One row per sample×replicon: gene, num hits, identities | 225,244 | 929.8 KB | ⬇ Download |
| plasmid_gene_freq.tsv | Plasmid | One row per replicon: num genomes, prevalence %, occurrences | 214 | 2.8 KB | ⬇ Download |
| cc.txt | Lineages | GCA accession → clonal complex → MLST ST | 17,972 | 117.7 KB | ⬇ Download |
| cc_analysis.json | Lineages | Aggregated stats: top CCs, top STs, coverage | 939 | 1.5 KB | ⬇ Download |
📖 How to use these files
All files are gzipped TSV. Decompress locally then open in pandas, R, or any tool:
gunzip mlst_all.tsv.gz head -5 mlst_all.tsv
Or read directly from Python without extracting:
pd.read_csv('mlst_all.tsv.gz', sep='\t')— pandas (auto-decompresses)read.delim('mlst_all.tsv.gz', sep='\t')— R
🧬 Long-format gene files
amr_all.tsv, vfdb_all.tsv, plasmid_all.tsv are long format — one row per present (sample, gene). To pivot to wide:
df.pivot_table(index='accession', columns='gene',
values='num_hits', fill_value=0)
📎 Related resources
- Source genomes: NCBI Assembly database
- Download accessions via
datasets download genome accession --inputfile list.txt - Typing pipeline: StaphScope on GitHub