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

1. Data-first approach - Load and validate count data and metadata BEFORE any analysis
2. Statistical rigor - Always use proper normalization, dispersion estimation, and multiple testing correction
3. Flexible design - Support single-factor, multi-factor, and interaction designs
4. Threshold awareness - Apply user-specified thresholds exactly (padj, log2FC, baseMean)
5. Reproducible - Set random seeds, document all parameters, output complete results
6. Question-driven - Parse what the user is actually asking and extract the specific answer
7. Enrichment integration - Chain DESeq2 results into pathway/GO enrichment when requested
8. English-first queries - Use English gene/pathway names in all tool calls