Gene Expression Data Retrieval
FreeEfficiently access and assess gene expression datasets.
Free · Opens the source repo
What Gene Expression Data Retrieval does
The Gene Expression Data Retrieval skill enables users to efficiently retrieve gene expression and multi-omics datasets from prominent databases such as ArrayExpress and BioStudies. This skill is particularly useful for researchers and bioinformaticians who need to find RNA-seq and microarray datasets based on specific organisms, tissues, or experimental conditions. It provides a structured approach to dataset retrieval, ensuring that users can compare studies across different designs, such as case-control or time-series, while also assessing the quality of datasets before downloading.
The process begins with clarification of user queries to resolve ambiguities regarding gene names, tissues, or organisms. Once the query is clarified, the skill disambiguates gene symbols using official databases like HGNC for humans or MGI for mice. This ensures that searches are accurate and comprehensive. The retrieval phase operates silently, searching through ArrayExpress and BioStudies to gather relevant datasets without unnecessary narration, which helps maintain focus on the results.
After retrieval, the skill generates a Dataset Search Report that summarizes the findings. This report includes essential details such as accession numbers, organism types, experimental designs, and quality assessments. Users can easily compare datasets and receive recommendations for the best datasets suited to their research needs. The skill emphasizes the importance of quality by prioritizing datasets with sufficient biological replicates and comprehensive metadata, which is critical for robust analysis in omics research.
Overall, this skill is designed for those who require reliable access to gene expression data, offering a systematic approach to dataset discovery and evaluation. It is particularly beneficial for academic researchers, data scientists, and anyone involved in genomic studies looking to streamline their data retrieval processes.
When to use it
Use this skill when you need to find and evaluate gene expression datasets for specific organisms or experimental conditions.
When not to use it
This skill may not be suitable for users looking for datasets outside of gene expression or multi-omics, or those who require non-English search capabilities.
What you can build with it
Finding RNA-seq Datasets
Use this skill to locate RNA-seq datasets by specifying the organism and experimental conditions.
Assessing Dataset Quality
Generate a report to evaluate the quality of retrieved datasets before downloading for analysis.
Comparing Multi-Omics Studies
Retrieve and compare multi-omics datasets from BioStudies to explore integrated data across studies.
How to install Gene Expression Data Retrieval
View source1. Install with the skills CLI
npx skills add mims-harvard/tooluniverse/tooluniverse-expression-data-retrieval --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by mims-harvardGene 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
Frequently asked questions about Gene Expression Data Retrieval
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