Omics Integration

RNA-seq Analysis

Upload DESeq2, edgeR, or limma results. GaiaLab extracts your significant genes and runs the full biomedical intelligence pipeline — pathways, drugs, hypotheses, and survival — automatically.

DESeq2 .csv edgeR .tsv limma .txt .rnk Seurat avg_log2FC

Upload expression results

Accepts any CSV/TSV with gene symbols, log2 fold-change, and adjusted p-value columns. Column names are detected automatically.

📂
Drop your file here or click to browse
.csv · .tsv · .txt · .rnk — max 50 MB
What GaiaLab does with your file ⬇ Download example DESeq2 CSV
  1. 1Extracts significant DE genes using your thresholds below (default: |log₂FC| ≥ 1.0, padj ≤ 0.05)
  2. 2Runs the full analysis pipeline on those genes — identical to submitting them on the homepage
  3. 3Returns: ranked drug candidates, KEGG/Reactome pathway enrichment, mechanistic hypotheses, evidence ledger (54 databases), and ClinicalTrials.gov concordance for your DE signature
Typical yield: a DESeq2 result with 20,000 genes at default thresholds yields 200–500 significant DE genes; GaiaLab takes the top 50 by |log₂FC| for the pipeline.

Significant genes

Genelog₂FCpadjDirection

How it works

1
Upload results
Drop any DESeq2, edgeR, or limma output. Column names are detected automatically.
2
Filter DEGs
Set your logFC and padj thresholds. Preview the significant gene list before submitting.
3
Full analysis
GaiaLab runs 60+ database queries — pathways, drugs, hypotheses, survival — on your DEG list.
4
RNA-seq overlay
Results include your expression direction context — upregulated vs. downregulated gene annotations.

Expected column names (auto-detected)

Gene symbol
gene · gene_name · gene_symbol · Hugo_Symbol · hgnc_symbol · external_gene_name
Log₂ fold-change
log2FoldChange · logFC · avg_log2FC · log2fc · LFC
Adjusted p-value
padj · adj.P.Val · FDR · q_value · p.adjust · BH