gget

Query over 20 genomic databases for gene information and sequence analysis.

8|Updated Nov 19, 2025
One-click install
npx skills add https://github.com/sanand0/scientific-research --skill gget-sanand0
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gget
Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/gget
Command: npx skills add https://github.com/sanand0/scientific-research --skill gget-sanand0

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines complex bioinformatics data retrieval and analysis, saving researchers significant time and effort in querying diverse genomic databases.

Core Features & Use Cases

  • Multi-Database Access: Unified interface to over 20 genomic databases (Ensembl, UniProt, AlphaFold, etc.).
  • Sequence Analysis: Perform BLAST, BLAT, alignment, and structure prediction.
  • Expression & Disease Data: Query single-cell data, enrichment analysis, and disease associations.
  • Use Case: Quickly find gene information, retrieve protein sequences, predict their 3D structure, and identify associated diseases, all from a single command.

Quick Start

Use gget to search for human gene information related to 'GABA receptor'.

Frequently Asked Questions about gget

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

FAQPage Schema
How do I query multiple genomic databases for gene information and protein sequences?

You can query genomic databases for gene information and protein sequences by using a unified command-line and Python interface that retrieves data directly from Ensembl, UniProt, and NCBI. This approach streamlines sequence analysis across over 20 databases.

Can I predict protein 3D structure and retrieve sequences in the same workflow?

Yes, you can predict protein 3D structure and retrieve sequences in the same workflow by integrating AlphaFold and RCSB PDB queries with sequence analysis methods. This unified toolkit fetches structural predictions alongside raw sequence data.

What's the best way to find disease associations from gene expression data?

The best way to find disease associations from gene expression data is to query integrated databases like OpenTargets, cBioPortal, and COSMIC alongside single-cell expression data from CELLxGENE. This links expression patterns directly to disease lookups.

Does this bioinformatics toolkit support single-cell gene expression querying?

Yes, this bioinformatics toolkit supports single-cell gene expression querying by integrating directly with the CELLxGENE and ARCHS4 databases. You can retrieve specific expression profiles and perform enrichment analysis from the command line.

How do I perform sequence alignment and BLAST searches across genomic databases?

You can perform sequence alignment and BLAST searches by utilizing the built-in sequence analysis methods that interface with NCBI and Ensembl. This allows rapid sequence querying and alignment directly from your Python environment or terminal.