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Linked challenge: Build an NCBI Gene Synonym Drift and Nomenclature Auditor
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77
Sections
11
Linked challenge
Build an NCBI Gene Synonym Drift and Nomenclature Auditor
Prompt source
Original prompt text with formatting preserved for inspection.
77 lines
11 sections
No variables
1 code block
## Build
Track gene symbol synonyms and catalog changes from NCBI Datasets gene metadata reports. Harmonize historical aliases into canonical identifiers mapped to FHIR Terminology bindings.
## User
Bioinformatics Data Curator
## Workflow
1. Parse gene data reports extracted from NCBI Datasets JSON packages using Pydantic schemas.
2. Extract current official gene symbols, historical synonyms, and cross-referenced taxonomy tags.
3. Reconcile obsolete aliases against canonical FHIR Coding datatypes with human-readable narratives.
4. Output structured audit reports highlighting deprecated symbols and recommended canonical mappings.
## Interface
Tabular browser interface showing obsolete aliases alongside canonical FHIR ConceptMap entries.
## Data
Ingest synthetic NCBI gene-service reports representing human genes A2M and ACRV1 from local mock files.
Starter examples below are synthetic. They are not downloaded records.
## Source
[NCBI Datasets gene download](https://www.ncbi.nlm.nih.gov/datasets/docs/v2/how-tos/genes/download-gene-data-package/)
Utilizes NCBI Datasets symbol query pattern 'datasets download gene symbol ACRV1 A2M --taxon human' and resulting metadata reports.
## Tools
pydantic-ai, biopython, streamlit.
Enforces strict data model boundaries on multi-field NCBI metadata reports and nested FHIR terminology objects.
General purpose hosted reasoning model or local LLM via Ollama for alias disambiguation.
## Deliverables
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.
## Acceptance
- Extracts all listed synonyms from NCBI gene data report JSON without data loss.
- Maps uncurated symbols to valid FHIR Coding objects including system, code, and display fields.
- Flags ambiguous historical aliases shared across multiple distinct Gene IDs.
## Starter Examples
```json
[
{
"input": {
"gene_id": 2,
"current_symbol": "A2M",
"taxname": "Homo sapiens",
"synonyms": [
"A2MD",
"CPAMD5",
"FWP007"
]
},
"expected_output": {
"canonical_symbol": "A2M",
"gene_id": 2,
"synonyms_found": 3,
"fhir_coding": {
"system": "https://www.ncbi.nlm.nih.gov/gene",
"code": "2",
"display": "A2M"
},
"status": "harmonized"
}
},
{
"input": {
"gene_id": 9999,
"current_symbol": "UNKNOWN",
"taxname": "Homo sapiens",
"synonyms": []
},
"expected_output": {
"canonical_symbol": "UNKNOWN",
"gene_id": 9999,
"synonyms_found": 0,
"fhir_coding": {
"system": "https://www.ncbi.nlm.nih.gov/gene",
"code": "9999",
"display": "UNKNOWN"
},
"status": "unresolved_synonyms"
}
}
]
```
## Safety
Research data harmonization only; does not inform patient diagnostic reports or medication administration.
Use mock external writes. Require human review before any real deployment.Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.