mims-harvard Gene Expression & Omics Data Retrieval
Retrieve gene expression experiments and multi-omics datasets with disambiguation and quality assessment.
IMPORTANT: Always use English terms in tool calls. Respond in the user's language.
LOOK UP DON'T GUESS: Never assume which datasets exist or their accessions. Always search to confirm.
Domain Reasoning
Before retrieving, determine: organism, tissue, experimental design (case-control/time-series/dose-response). These affect which database to search and how to interpret results. RNA-seq provides wider dynamic range; microarray has extensive legacy data. Prioritize experiments with >=3 biological replicates, complete annotations, and both raw+processed data.
Workflow
Phase 0: Clarify (if ambiguous) → Phase 1: Disambiguate → Phase 2: Search & Retrieve → Phase 3: Report
Phase 0: Clarification (When Needed)
Ask ONLY if: gene name ambiguous, tissue/condition unclear, organism not specified. Skip for: specific accessions (E-MTAB-, E-GEOD-, S-BSST*), clear disease/tissue+organism, explicit platform requests.
Phase 1: Query Disambiguation
Resolve official gene symbol (HGNC for human, MGI for mouse). Note common aliases for search expansion.
| User Query Type | Search Strategy |
|---|---|
| Specific accession | Direct retrieval |
| Gene + condition | "[gene] [condition]" + species filter |
| Disease only | "[disease]" + species filter |
| Technology-specific | Add platform keywords |
Phase 2: Data Retrieval (Internal)
Search silently. Do NOT narrate the process.
# ArrayExpress search
result = tu.tools.arrayexpress_search_experiments(keywords="[gene/disease]", species="[species]", limit=20)
# Get experiment details, samples, files
details = tu.tools.arrayexpress_get_experiment(accession=accession)
samples = tu.tools.arrayexpress_get_experiment_samples(accession=accession)
files = tu.tools.arrayexpress_get_experiment_files(accession=accession)
# BioStudies for multi-omics
biostudies = tu.tools.biostudies_search(query="[keywords]", limit=10)
study = tu.tools.biostudies_get_study(accession=study_accession)
study_files = tu.tools.biostudies_get_study_files(accession=study_accession)
Fallback Chains
| Primary | Fallback |
|---|---|
| ArrayExpress search | BioStudies search |
| arrayexpress_get_experiment | biostudies_get_study |
| arrayexpress_get_experiment_files | Note "Files unavailable" |
Phase 3: Report Dataset Profile
Present as a Dataset Search Report. Hide search process. Include:
- Search Summary: query, databases searched, result count
- Top Experiments (per experiment):
- Accession, organism, type (RNA-seq/microarray), platform, sample count, date
- Description, experimental design (conditions, replicates, tissue)
- Sample groups table, data files table
- Quality assessment (●●●/●●○/●○○)
- Multi-Omics Studies (from BioStudies): accession, type, data types included
- Summary Table: all experiments ranked
- Recommendations: best dataset for user's purpose, integration notes
- Data Access: download links, database URLs
Data Quality Tiers
| Tier | Symbol | Criteria |
|---|---|---|
| High | ●●● | >=3 bio replicates, complete metadata, processed data available |
| Medium | ●●○ | 2-3 replicates OR some metadata gaps |
| Low | ●○○ | No replicates, sparse metadata, or access issues |
| Caution | ○○○ | Single sample, no replication, outdated platform |
Reasoning Framework
Dataset quality: Prioritize >=3 biological replicates, complete annotations, both raw+processed data. Single-replicate experiments can inform but not be sole evidence.
Platform comparison: RNA-seq = wider dynamic range, novel transcripts. Microarray = probe-limited but extensive legacy data. Cross-platform combining requires batch correction.
Metadata scoring: Rate 0-5 on: (1) sample annotations, (2) design documented, (3) pipeline described, (4) raw data deposited, (5) publication linked. Score <=2 warrants caution.
GEO vs ArrayExpress: GEO has broader coverage (older studies); ArrayExpress enforces stricter metadata. BioStudies captures multi-omics. Search both.
Synthesis Questions
- Does the dataset have sufficient replication and metadata for the intended analysis?
- Are there batch effects or confounding variables?
- Do multiple datasets show concordant patterns, and can they be integrated?
Error Handling
| Error | Response |
|---|---|
| "No experiments found" | Broaden keywords, remove species filter, try synonyms |
| "Accession not found" | Verify format, check if withdrawn |
| "Files not available" | Note: "Data files restricted by submitter" |
| "API timeout" | Retry once, note "(metadata retrieval incomplete)" |
Tool Reference
ArrayExpress: arrayexpress_search_experiments (search), arrayexpress_get_experiment (metadata), arrayexpress_get_experiment_files (downloads), arrayexpress_get_experiment_samples (annotations)
BioStudies: biostudies_search (search), biostudies_get_study (metadata+sections), biostudies_get_study_files (files)
Additional Sources:
GEO_search_rnaseq_datasets/geo_search_datasets-- GEO (largest RNA-seq repo)OmicsDI_search_datasets-- cross-repository aggregation (GEO+ArrayExpress+PRIDE+MassIVE)GTEx_get_expression_summary-- baseline tissue expression (54 normal tissues, param:gene_symbol)ENAPortal_search_studies-- sequencing studies (param:querywithdescription="...")CxGDisc_search_datasets-- single-cell datasets (needs exact disease ontology terms)PubMed_search_articles-- dataset discovery via publications
Search Parameters
ArrayExpress: keywords (free text), species (scientific name), array (platform filter), limit
BioStudies: query (free text), limit