About
An open, deploy-anywhere context layer for omics computational work.
Built by Charlie Murphy, PhD
I have 10 years of experience in cancer genomics and software development. Goodomics comes from seeing computational results and the context needed to understand them disappear into folders, notebooks, one-off reports, and disconnected systems.
Goodomics is an early-stage, open-source Python project for making omics computational outputs visible, structured, and queryable. It sits after the systems that run the work and turns the results they produce into context that people (and AI agents) can understand later.
The adoption path is deliberately small: point Goodomics at a folder of outputs from Nextflow, Snakemake, MultiQC, notebooks, an existing platform, or a custom pipeline and generate a useful report. No account or database should be required for that first step.
When a team needs history, Goodomics adds durable context around runs, samples, metrics, files, cohorts, report versions, QC policies, and review provenance. That context can be explored through reports, the dashboard, SQL, APIs, and AI agents, while deterministic policies and human review remain the trust layer.
Goodomics is designed to run locally or in your cloud, server, HPC, or on-prem infrastructure. It complements the systems that run computational work rather than replacing them, and is intended to remain free and open source at its core.