In active development. Not ready for production use. Follow progress on GitHub.

Make your omics data visible and queryable.

Bring your omics data into one visual, structured context. Track samples, metrics, and policies; build insights and reports; and query it with SQL, APIs, or AI agents.

See a demo View on GitHub

Free and open source Deploy wherever your data already lives

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

Product interface mockup

How Goodomics works

Goodomics works like MultiQC: point it at the outputs you already produce and it automatically discovers standard bioinformatics files and metrics. Goodomics structures that run context, preserves the underlying evidence, and makes it available through reports, APIs, SQL, and AI agents.

Goodomics workflow showing MultiQC, Nextflow, Snakemake, notebooks, and omics files flowing into metadata, analytics, and file storage, then out to the dashboard, HTML reports, API and SQL access, and AI agents through MCP.

Built for how you work

Goodomics sits after your computational workflows. Start locally with a folder of outputs, or deploy it alongside your team’s existing cloud and pipeline infrastructure.

Run it where your data lives

Local computer

Laptop running a Goodomics report locally

Minimal setup required
Works offline in your environment
Start with one folder of outputs

Your infrastructure

Server and database representing a team Goodomics deployment

Deploy in your cloud, on-prem, or HPC
Use your own storage and database
Share context through the web UI, API, and MCP

Bring the outputs you already have

Workflow outputs

Nextflow, Snakemake, WDL, shell scripts, and notebooks

Existing platforms

Complement the systems that already run and manage your workflows

Custom outputs

MultiQC reports

Computational notebook with code and plots

Notebooks

Output files

Bring reports, exploratory analyses, and the files your pipelines already produce

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