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gget

Query 20+ bioinformatics databases and run sequence analysis from command line or Python.

Updated Mar 10, 2026
Search Tags:#bioinformatics#protein structure#genomics#sequence analysis#data retrieval#gene expression
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill gget-felixboehm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gget
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/gget
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill gget-felixboehm

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 by providing a unified command-line and Python interface to over 20 biological databases and tools.

Core Features & Use Cases

  • Unified Access: Query gene information, sequences, structures, expression data, and more from diverse sources like Ensembl, UniProt, NCBI, and PDB.
  • Sequence Analysis: Perform BLAST, BLAT, alignment (MUSCLE), and local alignment (DIAMOND).
  • Structure Prediction: Predict protein structures using AlphaFold.
  • Expression & Disease Data: Analyze single-cell data (cellxgene), gene expression (ARCHS4), and disease associations (OpenTargets).
  • Use Case: Quickly find the Ensembl ID for a gene, retrieve its protein sequence, predict its 3D structure, and identify associated diseases, all within a single workflow.

Quick Start

Use gget to find information for the human gene TP53.

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 for gene expression and protein structure data in one workflow?▼

You can query over 20 bioinformatics databases like Ensembl, UniProt, and PDB using a unified command-line and Python interface. This allows you to retrieve gene information, sequences, and structures, and perform functional enrichment within a single workflow.

What's the best way to retrieve a protein sequence and predict its 3D structure?▼

Retrieve protein sequences from sources like UniProt and predict 3D structures using integrated AlphaFold. This provides unified access to sequence retrieval and structure prediction tools for rapid proteomic data exploration.

Can I perform BLAST sequence analysis and local alignment using a single Python interface?▼

Yes, you can perform BLAST, BLAT, MUSCLE alignment, and DIAMOND local alignment. The unified interface streamlines sequence analysis by integrating these tools directly into your Python workflow.

Does gget support single-cell data analysis and disease association retrieval?▼

Yes, it supports single-cell data analysis through cellxgene and retrieves disease associations via OpenTargets. You can also analyze gene expression data using ARCHS4 for comprehensive transcriptomic exploration.

How do I find the Ensembl ID for a gene and identify its associated diseases?▼

Query Ensembl to find the gene ID, retrieve its sequence, and use OpenTargets to identify associated diseases. This unified workflow facilitates rapid exploration of genomic and disease data.

What are the limitations of using a unified interface for genomic and transcriptomic data retrieval?▼

The unified interface queries over 20 databases, including Ensembl, NCBI, and PDB, but is limited by the query capabilities and data availability of those external sources. Complex analyses like AlphaFold structure prediction depend on external tool integrations.

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