Cohort-aware QC for omics pipelines

QC reports that learn from your previous runs.

Goodomics is a free, open source project for turning bioinformatics pipeline outputs into interactive reports with cohort comparisons, thresholds, outlier detection, and versioned QC context.

View on GitHub See a demo

Goodomics report interface showing run status, cohort comparison, sample outliers, UMAP plot, failing metrics, and report versions

Built for how you work

Local computer

Run privately on your machine. Your data never leaves your environment.

No internet required
Zero configuration
Perfect for sensitive projects
Works offline

$ goodomics report ./results --local

Any platform or cloud

Plug Goodomics into the workflows and infrastructure you already use.

Nextflow, Snakemake, WDL, shell, or notebooks
AWS, Google Cloud, Azure, HPC, or Slurm
Workflow-friendly config
Custom scripts and REST API

$ goodomics report ./results --cli

Your database

Self-host Goodomics and keep full control of your data and run history.

SQLite or Postgres
Local web UI and API
Portable run history
Team patterns can come later

$ goodomics server start --db postgres://...

From metrics to QC decisions

1. Detect

Auto-discover metrics from 100+ tools.

2. Compare

Benchmark every run against the right context.

3. Decide

Apply thresholds and review what matters.

4. Document

Versioned reports you can share and cite.

Features

Simple enough to try today, structured enough to keep

Start with a folder

Point Goodomics at pipeline outputs, MultiQC files, traces, logs, and custom metric tables. No account or database required for the first report.

Compare against previous runs

Add a local or team database to compare new runs with trusted cohorts, assay baselines, pipeline versions, and batches.

Make thresholds explicit

Turn notebook-era QC knowledge into reusable warning and failure rules that can move with your pipeline.

Versioned QC context

Record which report template, cohort, and QC policy were active, so a decision still makes sense later.

Custom metrics welcome

Bring assay-specific metrics, internal scores, and evolving R&D checks without building a custom web app.

Local-first

Run locally, inside Docker, or on your own server. The project should work before there is any hosted service around it.

Example workflow

Start with a report. Add context when your team needs history.

Scanner mode gets adoption. Database-backed cohorts, thresholds, and report versions create durable QC decisions your team can reproduce later.

goodomics report ./results --out report.html

goodomics ingest ./results \
  --project rnaseq-core \
  --report rnaseq-qc@v3 \
  --cohort production-rnaseq-hg38@2026-05

Free and open source

Goodomics is intended to stay free and open source. I am exploring paid hosting options with extra functionality for teams that want operated infrastructure, collaboration features, and less setup. If that sounds useful, reach out.

Reach out