| --- |
| license: mit |
| tags: |
| - genomics |
| - bioinformatics |
| - benchmark |
| - grading |
| - synonyms |
| - databases |
| - ncbi-gene |
| - ncbi-taxonomy |
| - mirbase |
| - hmdb |
| - card |
| pretty_name: OmicsBench Grader Databases |
| size_categories: |
| - 100M<n<1B |
| --- |
| |
| # OmicsBench Grader Databases |
|
|
| Synonym databases for deterministic grading in [OmicsBench](https://github.com/AfterQuery/OmicsBench), a realism-first benchmark for AI coding agents in computational biology. |
|
|
| OmicsBench ships with dozens of prebuilt graders for highly specific biological outputs. This dataset powers the synonym-aware matching behind those graders, so that *TP53* and *tumor protein p53* are recognized as the same gene, *hsa-miR-21-5p* and *MIMAT0000076* as the same miRNA, and *E. coli* and *Escherichia coli* as the same organism. |
|
|
| ## Format |
|
|
| One read-only SQLite file, `grader_databases.sqlite` (~22 GB), with five tables -- one per source database. Lookup columns are pre-lowercased at build time (full Unicode `str.lower()`) and covered by composite indexes, so runtime queries are direct indexed seeks and process memory is O(query result) rather than O(database size). |
|
|
| | Table | Columns | Rows | Source | Description | |
| |---|---|---|---|---| |
| | `ncbi_gene` | `organism_lower name_lower gene_id` | ~219M | [NCBI Gene](https://www.ncbi.nlm.nih.gov/gene/) | Gene symbol, synonym, description, Entrez GeneID, and Ensembl ID resolution scoped by organism | |
| | `ncbi_taxonomy` | `name_lower canonical_name` | ~3.2M | [NCBI Taxonomy](https://www.ncbi.nlm.nih.gov/taxonomy) | Scientific name, common name, abbreviation, and synonym resolution for all taxa | |
| | `hmdb` | `name_lower accession` | ~1.5M | [HMDB](https://hmdb.ca/) | Metabolite name, synonym, IUPAC name, and HMDB accession resolution | |
| | `mirbase` | `name_lower accession` | ~157K | [miRBase](https://mirbase.org/) | miRNA precursor and mature name/accession resolution, including deprecated entries folded into live replacements | |
| | `card` | `name_lower aro_accession` | ~19K | [CARD](https://card.mcmaster.ca/) | Antimicrobial resistance gene name, synonym, and ARO accession resolution | |
|
|
| Three of the tables (`ncbi_taxonomy`, `hmdb`, `mirbase`) use `WITHOUT ROWID` with `PRIMARY KEY(name_lower)` and are built via `INSERT OR IGNORE` so the first occurrence of any lowercased name wins. The other two (`ncbi_gene`, `card`) are 1:N and allow multiple rows per lookup key -- `gene_match` and `amr_match` do set-intersection at query time. |
|
|
| ## Usage |
|
|
| Install OmicsBench and call the match functions directly; downloading and caching happens on first use. |
|
|
| ```python |
| from omicsbench.grading import ( |
| gene_match, |
| taxonomy_match, |
| mirna_match, |
| metabolite_match, |
| amr_match, |
| ) |
| |
| gene_match("TP53", "tumor protein p53", organism="Homo sapiens") # True |
| taxonomy_match("E. coli", "Escherichia coli") # True |
| mirna_match("hsa-miR-21-5p", "MIMAT0000076") # True |
| metabolite_match("Dextrose", "D-Glucose") # True |
| amr_match("mecA", "PBP2A") # True |
| ``` |
|
|
| Each match function is a thin wrapper over one indexed SQL query plus a case-insensitive string-equality fallback (used when either side is absent from the database, so misses don't crash on out-of-distribution inputs). The public API signatures, return types, and fallback semantics are unchanged from the previous TSV-backed release -- consumers upgrade by bumping their `omicsbench` package version. |
|
|
| Each lookup module opens a thread-local read-only connection on first use with read-tuning PRAGMAs (`query_only=1`, `cache_size=-20000`, `temp_store=MEMORY`, `mmap_size=268435456`), so the dataset works correctly under multi-threaded consumers like the OmicsBench async orchestrator. SQLite supports unlimited concurrent readers via shared file locks. |
|
|
| ## Direct access |
|
|
| If you want to query the tables yourself without installing OmicsBench, open the file read-only: |
|
|
| ```python |
| import sqlite3 |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download( |
| repo_id="AfterQuery/OmicsBench-grader-databases", |
| filename="grader_databases.sqlite", |
| repo_type="dataset", |
| ) |
| conn = sqlite3.connect(f"file:{path}?mode=ro", uri=True) |
| |
| rows = conn.execute( |
| "SELECT gene_id FROM ncbi_gene " |
| "WHERE organism_lower = ? AND name_lower = ?", |
| ("homo sapiens", "tp53"), |
| ).fetchall() |
| ``` |
|
|
| All lookup columns are pre-lowercased; lowercase your query inputs in Python before binding. The table schemas are stable across releases: any schema change is a breaking revision of this dataset. |
|
|
| ## Provenance and licensing |
|
|
| The five source databases retain their upstream licenses. OmicsBench packages them into a single SQLite file for efficient runtime access; see the [OmicsBench GitHub repository](https://github.com/AfterQuery/OmicsBench) for the full ETL pipeline under `scripts/databases/` and its step-by-step runbook in `scripts/databases/README.md`. |
|
|
| Rebuilds ship as new revisions on this dataset whenever an upstream source publishes a new release. |
|
|
