tooluniverse-rnaseq-deseq2
RNA-seq Differential Expression Analysis (DESeq2)
Comprehensive differential expression analysis of RNA-seq count data using PyDESeq2, with integrated enrichment analysis (gseapy) and gene annotation via ToolUniverse.
BixBench Coverage: Validated on 53 BixBench questions across 15 computational biology projects covering RNA-seq, miRNA-seq, and differential expression analysis tasks.
Core Principles
- Data-first approach - Load and validate count data and metadata BEFORE any analysis
- Statistical rigor - Always use proper normalization, dispersion estimation, and multiple testing correction
- Flexible design - Support single-factor, multi-factor, and interaction designs
- Threshold awareness - Apply user-specified thresholds exactly (padj, log2FC, baseMean)
- Reproducible - Set random seeds, document all parameters, output complete results
- Question-driven - Parse what the user is actually asking and extract the specific answer
- Enrichment integration - Chain DESeq2 results into pathway/GO enrichment when requested
- English-first queries - Use English gene/pathway names in all tool calls
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