How does gget ensure data accuracy?

The tool is automatically tested on a biweekly basis to ensure compatibility with the evolving structures of the 20+ databases it queries.

What databases can gget access through Claude Code?

gget provides unified access to over 20 major databases, including Ensembl, UniProt, NCBI, RCSB PDB, ARCHS4, and CZ CELLxGENE.

Is gget available for both command-line and Python?

Yes, every gget module is designed to function identically as a command-line tool and as a function within a Python environment.

Does gget support sequence alignment?

Absolutely. It supports BLAST, BLAT, Muscle5, and DIAMOND for various local and multiple sequence alignment tasks.

Can I use this skill for protein structure prediction?

Yes, gget includes built-in modules for AlphaFold2 structure prediction and querying structural metadata from the Protein Data Bank (PDB).

gget Bioinformatics Toolkit

Name: gget Bioinformatics Toolkit
Author: Microck

byMicrock

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Ciencia de Datos y ML

Provides unified access to 20+ genomic databases and analysis methods for rapid bioinformatics research and sequence analysis.

Acerca de

gget is a comprehensive CLI and Python toolkit designed to streamline bioinformatics workflows by providing a consistent interface for over 20 genomic databases. It enables researchers and developers to perform complex tasks like gene searches, sequence retrieval, protein structure prediction via AlphaFold, and multiple sequence alignment without managing multiple disparate APIs. Whether querying Ensembl for reference genomes or exploring single-cell RNA-seq data from CELLxGENE, gget simplifies high-performance biological data acquisition and analysis into a unified environment directly accessible through Claude Code.

Características Principales

High-speed sequence alignment capabilities using BLAST, BLAT, and Muscle5
81 GitHub stars
Dual-mode functionality as both a command-line tool and a Python library
Comprehensive gene metadata retrieval including tissue expression and disease associations
Integrated 3D protein structure prediction using simplified AlphaFold2 workflows
Unified access to 20+ genomic and proteomic databases including Ensembl, UniProt, and NCBI

Casos de Uso

Analyzing gene expression correlations and single-cell RNA-seq data across diverse tissue types
Automating the retrieval of reference genomes and gene annotations for large-scale genomic studies
Predicting protein structures and linear motifs for functional analysis or drug discovery research

Acerca de

Características Principales

High-speed sequence alignment capabilities using BLAST, BLAT, and Muscle5
81 GitHub stars
Dual-mode functionality as both a command-line tool and a Python library
Comprehensive gene metadata retrieval including tissue expression and disease associations
Integrated 3D protein structure prediction using simplified AlphaFold2 workflows
Unified access to 20+ genomic and proteomic databases including Ensembl, UniProt, and NCBI

Casos de Uso

Analyzing gene expression correlations and single-cell RNA-seq data across diverse tissue types
Automating the retrieval of reference genomes and gene annotations for large-scale genomic studies
Predicting protein structures and linear motifs for functional analysis or drug discovery research