gget

Querying genomic databases for gene information and sequence retrieval is unified through a single CLI and Python interface. The tool provides access to Ensembl, UniProt, and NCBI, enabling rapid gene discovery and data integration without juggling multiple tools.

Enrichment analysis and expression data retrieval can be performed directly within a Python script. The interface supports end-to-end workflows including gene discovery, expression correlation analyses, and disease associations for reproducible research outputs.

Large genomic database queries are supported with built-in caching and rate limiting. This ensures reproducible outputs and handles extensive multi-step workflows for end-to-end research tasks without overwhelming external APIs.

The best way to retrieve structural data from PDB and AlphaFold is using the unified Python or CLI interface. It enables rapid sequence fetch and structural data access alongside reference and disease association queries within reproducible scripts.

Pandas is required as a dependency to analyze genomic expression and correlation data. Combined with the core gget package, it handles large query outputs and facilitates data integration for downstream enrichment workflows.

Unified genomic database access streamlines querying cBioPortal, COSMIC, and OpenTargets compared to using individual platforms. It enables rapid gene characterization, disease association retrieval, and multi-step workflows within a single reproducible environment.