Tooluniverse Expression Data Retrieval

Gene Expression & Omics Data Retrieval

Retrieve gene expression experiments and multi-omics datasets with proper disambiguation and quality assessment.

IMPORTANT: Always use English terms in tool calls (gene names, tissue names, condition descriptions), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.

Workflow Overview

Phase 0: Clarify Query (if ambiguous)
    ↓
Phase 1: Disambiguate Gene/Condition
    ↓
Phase 2: Search & Retrieve (Internal)
    ↓
Phase 3: Report Dataset Profile

Phase 0: Clarification (When Needed)

Ask the user ONLY if:

• Gene name is ambiguous (e.g., "p53" → TP53 or MDM2 studies?)
• Tissue/condition unclear for comparative studies
• Organism not specified for non-human research

Skip clarification for:

• Specific accession numbers (E-MTAB-, E-GEOD-, S-BSST*)
• Clear disease/tissue + organism combinations
• Explicit platform requests (RNA-seq, microarray)

Phase 1: Query Disambiguation

1.1 Gene Name Resolution

If searching by gene, first resolve official identifiers:

from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()

# For gene-focused searches, resolve official symbol first
# This helps construct better search queries
# Example: "p53" → "TP53" (official HGNC symbol)

Gene Disambiguation Checklist:

• Official gene symbol identified (HGNC for human, MGI for mouse)
• Common aliases noted for search expansion
• Species confirmed

1.2 Construct Search Strategy

Phase 2: Data Retrieval (Internal)

Search silently. Do NOT narrate the process.

2.1 Search Experiments

# ArrayExpress search
result = tu.tools.arrayexpress_search_experiments(
    keywords="[gene/disease] [condition]",
    species="[species]",
    limit=20
)

# BioStudies for multi-omics
biostudies_result = tu.tools.biostudies_search_studies(
    query="[keywords]",
    limit=10
)

2.2 Get Experiment Details

For top results, retrieve full metadata:

# Get details for each relevant experiment
details = tu.tools.arrayexpress_get_experiment_details(
    accession=accession
)

# Get sample information
samples = tu.tools.arrayexpress_get_experiment_samples(
    accession=accession
)

# Get available files
files = tu.tools.arrayexpress_get_experiment_files(
    accession=accession
)

2.3 BioStudies Retrieval

# Multi-omics study details
study_details = tu.tools.biostudies_get_study_details(
    accession=study_accession
)

# Study structure
sections = tu.tools.biostudies_get_study_sections(
    accession=study_accession
)

# Available files
files = tu.tools.biostudies_get_study_files(
    accession=study_accession
)

Fallback Chains

Phase 3: Report Dataset Profile

Output Structure

Present as a Dataset Search Report. Hide search process.

# Expression Data: [Query Topic]

**Search Summary**
- Query: [gene/disease] in [species]
- Databases: ArrayExpress, BioStudies
- Results: [N] relevant experiments found

**Data Quality Overview**: [assessment based on criteria below]

---

## Top Experiments

### 1. [E-MTAB-XXXX]: [Title]

| Attribute | Value |
|-----------|-------|
| **Accession** | [accession with link] |
| **Organism** | [species] |
| **Experiment Type** | RNA-seq / Microarray |
| **Platform** | [specific platform] |
| **Samples** | [N] samples |
| **Release Date** | [date] |

**Description**: [Brief description from metadata]

**Experimental Design**:
- Conditions: [treatment vs control, etc.]
- Replicates: [N biological, M technical]
- Tissue/Cell type: [if specified]

**Sample Groups**:
| Group | Samples | Description |
|-------|---------|-------------|
| Control | [N] | [description] |
| Treatment | [N] | [description] |

**Data Files Available**:
| File | Type | Size |
|------|------|------|
| [filename] | Processed data | [size] |
| [filename] | Raw data | [size] |
| [filename] | Sample metadata | [size] |

**Quality Assessment**: ●●● High / ●●○ Medium / ●○○ Low
- Sample size: [adequate/limited]
- Replication: [yes/no]
- Metadata completeness: [complete/partial]

---

### 2. [E-GEOD-XXXXX]: [Title]
[Same structure as above]

---

## Multi-Omics Studies (from BioStudies)

### [S-BSST-XXXXX]: [Title]

| Attribute | Value |
|-----------|-------|
| **Accession** | [accession] |
| **Study Type** | [proteomics/metabolomics/integrated] |
| **Organism** | [species] |
| **Samples** | [N] |

**Data Types Included**:
- [ ] Transcriptomics
- [ ] Proteomics
- [ ] Metabolomics
- [ ] Other: [specify]

---

## Summary Table

| Accession | Type | Samples | Platform | Quality |
|-----------|------|---------|----------|---------|
| [E-MTAB-X] | RNA-seq | [N] | Illumina | ●●● |
| [E-GEOD-X] | Microarray | [N] | Affymetrix | ●●○ |

---

## Recommendations

**For [specific analysis type]**:
- Best experiment: [accession] - [reason]
- Alternative: [accession] - [reason]

**Data Integration Notes**:
- Platform compatibility: [notes on combining datasets]
- Batch considerations: [if applicable]

---

## Data Access

### Direct Download Links
- [E-MTAB-XXXX processed data](link)
- [E-MTAB-XXXX raw data](link)

### Database Links
- ArrayExpress: https://www.ebi.ac.uk/arrayexpress/experiments/[accession]
- BioStudies: https://www.ebi.ac.uk/biostudies/studies/[accession]

Retrieved: [date]

Data Quality Tiers

Assessment criteria for expression experiments:

Include assessment rationale:

**Quality**: ●●● High
- ✓ 4 biological replicates per condition
- ✓ Complete sample annotations
- ✓ Processed and raw data available
- ✓ Recent RNA-seq platform

Completeness Checklist

Every dataset report MUST include:

Per Experiment (Required)

• Accession number with database link
• Organism
• Experiment type (RNA-seq/microarray/etc.)
• Sample count
• Brief description
• Quality assessment

Search Summary (Required)

• Query parameters stated
• Number of results
• Databases searched

Recommendations (Required)

• Best dataset for user's purpose (or "No suitable data found")
• Data access notes

Include Even If Empty

• Multi-omics studies section (or "No multi-omics studies found")
• Data integration notes (or "Single-platform data, no integration needed")

Common Use Cases

Disease Gene Expression

User: "Find breast cancer RNA-seq data"

result = tu.tools.arrayexpress_search_experiments(
    keywords="breast cancer RNA-seq",
    species="Homo sapiens",
    limit=20
)

→ Report top experiments with quality assessment

Gene-Specific Studies

User: "Find TP53 expression experiments in mouse"

result = tu.tools.arrayexpress_search_experiments(
    keywords="TP53 p53",  # Include aliases
    species="Mus musculus",
    limit=15
)

→ Report experiments studying this gene

Specific Accession Lookup

User: "Get details for E-MTAB-5214" → Single experiment profile with all details and files

Multi-Omics Integration

User: "Find proteomics and transcriptomics studies for liver disease" → Search both ArrayExpress and BioStudies, note integration potential

Error Handling

Tool Reference

ArrayExpress (Gene Expression)

BioStudies (Multi-Omics)

Search Parameters Reference

ArrayExpress

BioStudies