gget

gget - Fast Genomic Database Query Tool

Skills Overview

gget is a command-line bioinformatics tool and a Python package that provides unified access to 20+ genome databases and analysis methods. It supports gene information queries, sequence analysis, protein structure prediction, downloading viral sequences, gene set enrichment analysis, and disease association data retrieval. It is suitable for interactive exploration and simple query scenarios.

Use Cases

1. Gene Discovery and Sequence Analysis

When you need to quickly look up gene information, obtain gene sequences, perform sequence alignment, or predict protein structures, gget offers a concise command-line interface and Python API. You can search genes, retrieve detailed information, download FASTA sequences, run BLAST searches, and even use AlphaFold to predict protein structures—everything is done through a unified gget <module> pattern.

2. Gene Expression and Disease Association Analysis

Researchers can use gget to quickly query gene expression patterns across different tissues, find related genes, perform functional enrichment analysis, or retrieve disease and drug association data. This is especially useful for understanding gene functions, identifying potential drug targets, or analyzing cancer genomics data. It supports multiple databases, including ARCHS4, Enrichr, OpenTargets, and cBioPortal.

3. Single-Cell Data and Viral Genomics Research

gget supports querying the CZI CELLxGENE Discover single-cell database to obtain gene expression data in specific cell types. It can also download viral nucleotide sequences and related metadata from NCBI Virus. These capabilities enable researchers to quickly access modern single-cell datasets and viral genomics resources without complex data processing workflows.

Core Features

1. Unified Multi-Database Query Interface

gget integrates more than twenty bioinformatics databases, including Ensembl, UniProt, NCBI, RCSB PDB, OpenTargets, ARCHS4, Bgee, and COSMIC. Through a consistent command-line interface and Python API, it provides multiple data types such as gene information, sequence data, protein structures, expression profiles, and disease associations. Most modules support both command-line and Python function calls, returning JSON/CSV format (CLI) or DataFrames/dictionaries (Python), making it easy to integrate into different workflows.

2. Sequence Analysis and Structure Prediction

It provides a complete sequence analysis toolchain, including BLAST/BLAT sequence alignment, MUSCLE multiple sequence alignment, DIAMOND fast protein alignment, protein structure prediction, and linear motif-based predictions. In particular, the AlphaFold module can predict protein 3D structures, supporting both monomer and multimer modeling, and can generate visualized results. These tools help researchers understand sequence similarity, predict molecular functions, and identify structural features.

3. Expression Data and Disease Association Retrieval

It supports querying ARCHS4 gene correlations and tissue expression data, the CELLxGENE single-cell database, Enrichr enrichment analysis, and OpenTargets disease and drug associations. The cBioPortal module can generate cancer genomics heatmaps, while the COSMIC module queries the somatic mutation database. These features help researchers quickly obtain gene expression patterns, functional annotations, disease phenotypes, and therapy target information, accelerating the generation and validation of research hypotheses.

Frequently Asked Questions

What is the difference between gget and biopython? When should I use which?

gget is designed for fast querying and interactive exploration. It provides a simple command-line interface to access multiple bioinformatics databases, making it well-suited for quickly retrieving gene information, performing basic sequence analysis, and exploring data. biopython is better for batch processing, advanced BLAST analyses, and complex workflows, offering more low-level control and a richer bioinformatics functionality stack. For multi-database Python workflows, bioservices may be a better choice.

Which Python versions does gget support? How do I install it?

gget requires Python ≥ 3.8. Python 3.9 or 3.10 is recommended to ensure compatibility with scientific computing dependencies. For installation, it is recommended to use a virtual environment:
uv venv .venv && source .venv/bin/activate && uv pip install "gget==0.30.5".
Some modules (e.g., alphafold, cellxgene, elm) require additional setup, and running gget setup <module> downloads dependencies. For reproducibility, it is recommended to pin versions:
gget==0.30.5 (as of 2026-06-07).

How do I use gget to predict protein structure?

First, you need to set up dependencies: gget setup alphafold (about 4GB of model parameters to download). Then you can use gget alphafold <sequence> to predict a monomer structure, or provide a FASTA file to predict a multimer. In Python, gget.alphafold("sequence", plot=True) can produce visualization results. To improve multimer accuracy, it is recommended to use the -mr 20 parameter to increase the number of recycling iterations. The prediction outputs include PDB structure files, JSON alignment error data, and optional 3D visualizations.