Getting Started

From gene expression data to publication-ready results in three steps.

Step 1

Upload Your Data

Upload a CSV or TSV file with gene expression data. Column headers become your sample names — we auto-detect groups from naming patterns like WT_rep1, KO_rep2.

Step 2

Configure & Run

Select which sample groups to compare (e.g., Treatment vs Control). Choose Express for a quick look or Enhanced for the full analysis with pathways and publication plots.

Step 3

Explore Results

Interactive volcano plots, heatmaps, pathway enrichment, and prioritized gene lists. Download everything as publication-quality figures or filtered data tables.

What File Do I Need?

A CSV or TSV with gene identifiers in the first column and expression values in all other columns. Here's an example:

gene_idWT_rep1WT_rep2KO_rep1KO_rep2
BRCA115421389423398
TP538921910287568834
MYC31228718471923
GAPDH45210448934510244567

Column headers = sample names. The platform groups samples by shared prefixes (WT, KO).

or use Ensembl IDs — we convert automatically
gene_idWT_rep1WT_rep2KO_rep1KO_rep2
ENSG0000001204815421389423398
ENSG000001415108921910287568834
ENSG0000013699731228718471923

Use one format or the other — don't mix gene symbols and Ensembl IDs in the same file.

Accepted

  • CSV or TSV files
  • Raw counts, TPM, FPKM, or normalized values
  • Output from STAR, featureCounts, HTSeq, or similar tools
  • Gene symbols (BRCA1) or Ensembl IDs (ENSG00000012048)
  • Human or mouse genes (both species supported)
  • Any number of samples (2+ per group recommended)

Not Accepted

  • FASTQ or raw sequencing files (align first with STAR, Salmon, etc.)
  • Matrices not in gene x sample format (e.g., sample x gene)
  • Non-human, non-mouse organisms (more species coming soon)
  • Pre-computed statistics (fold changes, p-values)

What You'll Get Back

Every analysis produces interactive visualizations and downloadable data.

Volcano Plot

Visualize significant genes by fold change vs statistical significance. Click any point to see gene details.

PCA Plot

See how your samples cluster by overall expression profile. Quickly spot outliers or batch effects.

Heatmap

Top differentially expressed genes across all samples with hierarchical clustering. Publication-ready.

Pathway Analysis

GO biological process and KEGG pathway enrichment. Understand which biological systems are affected.

Gene Prioritization

Prairie's scoring algorithm ranks your genes by druggability, novelty, and statistical confidence. Find actionable targets fast.

Data Tables

Full differential expression results with filtering, sorting, and export. Download as CSV for your own analysis.

Express vs Enhanced

Choose the depth of analysis that fits your needs.

FeatureExpress~30 secondsEnhanced2-5 minutes
Differential expression
Volcano + PCA plots
Heatmap--
Pathway analysis (GO + KEGG)--
Gene prioritization scoring--
Publication-quality figures--