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gget

Query 20+ bioinformatics databases from CLI and Python.

1|Updated Apr 29, 2026
Search Tags:#python#cli#bioinformatics#databases#enrichment#opentargets#gget
One-click install
npx skills add https://github.com/cultivarium/hermes-starter-kit --skill gget-cultivarium
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gget
Source: https://github.com/cultivarium/hermes-starter-kit/tree/main/skills/gget
Command: npx skills add https://github.com/cultivarium/hermes-starter-kit --skill gget-cultivarium

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, gget, and includes scripts (resource) and references (resource) components.

What problem does it solve?

gget provides a single, consistent interface to query 20+ bioinformatics databases from the command line and Python, eliminating the need to juggle multiple tools and APIs.

Core Features & Use Cases

  • Unified access: CLI and Python APIs for gene search, reference retrieval, sequence handling, enrichment analyses, and disease/drug associations.
  • End-to-end workflows: Build pipelines from discovery to interpretation, including expression data, correlated genes, and enrichment contexts across databases.
  • Use Case: Quickly investigate a gene like ACE2 by pulling metadata, sequences, tissue expression, and enrichment all in one workflow.

Quick Start

Install gget and run a simple query such as gget search ACE2 to begin exploring gene information.

Frequently Asked Questions about gget

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query multiple bioinformatics databases from a single Python or CLI interface?▼

You can query 20+ bioinformatics databases using a single, consistent Python or CLI interface to retrieve gene metadata, sequences, expression data, and disease associations without managing multiple APIs.

What's the best way to build an end-to-end bioinformatics workflow from gene discovery to enrichment analysis?▼

Build end-to-end bioinformatics workflows by chaining modular cross-database queries for gene search, sequence retrieval, tissue expression, and enrichment analysis within a single Python or CLI environment.

Can I retrieve tissue expression data and disease associations for a specific gene like ACE2?▼

Yes, you can investigate a gene like ACE2 by pulling its metadata, sequences, tissue expression, and disease associations across multiple databases in one unified query workflow.

Do I need pandas installed to use this bioinformatics database query tool?▼

Yes, pandas is a required dependency, alongside the core package, to structure and handle the data returned from querying the 20+ integrated bioinformatics databases.

Does this unified bioinformatics database tool support optional setups for AlphaFold and cellxgene?▼

The tool supports optional setups for AlphaFold, cellxgene, elm, and OpenAI gpt, allowing extended structural and expression analysis alongside standard database queries.

How does local caching and versioned releases ensure reproducibility for bioinformatics queries?▼

Reproducibility for bioinformatics queries is ensured through versioned releases and local caching, which maintain consistent query results across different pipeline executions and database updates.

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