Goodomics documentation¶
Goodomics is an open, deploy-anywhere context layer for omics computational work. Start with a folder of pipeline outputs, generate a clear QC report, and add durable run context over time.
Goodomics is useful in a few modes:
- Standalone report generation for local or pipeline outputs.
- Lightweight Python SDK instrumentation.
- Local database mode for runs, samples, metrics, files, and reports.
- API, dashboard, and MCP access for browsing and querying run context.
Initial adoption path
The first Goodomics workflow should stay simple: point it at outputs, generate a report, and add persistent context only when you need it.
Documentation map¶
- Getting started: install Goodomics and generate the first standalone report.
- CLI: run reports and ingest outputs from the command line.
- Python SDK: record runs, samples, metrics, and files from Python code.
- Custom parsers: ingest lab-specific tables, dataframes, and notebook objects without writing a full plugin.
- Server: run the optional FastAPI, MCP, database, and dashboard server.
- Dashboard: browse runs and edit reports and insights.
- Reports and insights: define portable report layouts for the CLI and dashboard.
Trust boundary¶
Goodomics helps preserve, query, compare, and review omics computational outputs. It should not be treated as a LIMS, workflow orchestrator, broad biological interpretation engine, giant data lake, or blackbox AI decision-maker.
Agents and AI tools can query, summarize, compare, draft, and suggest against Goodomics context. Deterministic storage, provenance, policies, and human review remain the trust layer.