Does this skill support protein structure prediction?

Yes, gget includes a module for AlphaFold2 that allows for 3D protein structure prediction and multimer modeling directly from sequences.

What databases can gget query?

gget provides access to over 20 databases including Ensembl, UniProt, NCBI, AlphaFold, PDB, ARCHS4, Enrichr, OpenTargets, and COSMIC.

Is gget suitable for large-scale BLAST processing?

While gget is preferred for quick BLAST searches, it recommends using Biopython for advanced batch processing or Bioservices for complex multi-database integration.

Can I use gget in both CLI and Python environments?

Absolutely. Every gget module is designed to work as both a command-line tool and a native Python function, returning JSON/CSV or DataFrames respectively.

How often are the database connectors updated?

The gget modules are automatically tested on a biweekly basis and updated whenever database structures change to ensure reliable data retrieval.

Bioinformatics Query Toolkit (gget)

Name: Bioinformatics Query Toolkit (gget)
Author: x-cmd

byx-cmd

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Ciencia de Datos y ML

Provides unified, high-speed access to 20+ genomic databases and protein structure prediction tools directly from the command line.

gget is a comprehensive CLI and Python-integrated toolkit designed to streamline bioinformatics workflows by providing a consistent interface for querying major databases like Ensembl, UniProt, and the PDB. It enables researchers and developers to retrieve genomic sequences, perform complex sequence alignments using BLAST or DIAMOND, predict 3D protein structures via AlphaFold, and analyze single-cell RNA-seq data. This skill is essential for automating biological data retrieval and integrating multi-database genomic information into AI-assisted coding pipelines, significantly reducing the time spent navigating disparate web portals.

Características Principales

018 GitHub stars

02Comprehensive gene metadata retrieval and automated reference genome downloads

03Single-cell RNA-seq data querying via CZ CELLxGENE Discover Census

04Automated 3D protein structure prediction using simplified AlphaFold2

05Unified access to 20+ genomic databases including Ensembl, UniProt, and NCBI

06Rapid sequence alignment with integrated BLAST, BLAT, and DIAMOND support

Casos de Uso

01Performing rapid protein-to-protein alignments and structural analysis within research scripts

02Integrating tissue-specific expression data and protein motifs into biological data science projects

03Automating the retrieval of FASTA sequences and reference genomes for gene lists

Características Principales

018 GitHub stars

02Comprehensive gene metadata retrieval and automated reference genome downloads

03Single-cell RNA-seq data querying via CZ CELLxGENE Discover Census

04Automated 3D protein structure prediction using simplified AlphaFold2

05Unified access to 20+ genomic databases including Ensembl, UniProt, and NCBI

06Rapid sequence alignment with integrated BLAST, BLAT, and DIAMOND support

Casos de Uso

01Performing rapid protein-to-protein alignments and structural analysis within research scripts

02Integrating tissue-specific expression data and protein motifs into biological data science projects

03Automating the retrieval of FASTA sequences and reference genomes for gene lists