gget

Overview

gget is a command-line bioinformatics tool and Python package providing unified access to 20+ genomic databases and analysis methods. Query gene information, sequence analysis, protein structures, expression data, and disease associations through a consistent interface. All gget modules work both as command-line tools and as Python functions.

Important: The databases queried by gget are continuously updated, which sometimes changes their structure. gget modules are tested automatically on a biweekly basis and updated to match new database structures when necessary.

Installation

Install gget in a clean virtual environment to avoid conflicts:

# Install (or upgrade) into a clean environment
uv pip install --upgrade gget

# In Python/Jupyter
import gget

Quick Start

Basic usage pattern for all modules:

# Command-line
gget <module> [arguments] [options]

# Python
gget.module(arguments, options)

Most modules return:

• Command-line: JSON (default) or CSV with -csv flag
• Python: DataFrame or dictionary

Common flags across modules:

• -o/--out: Save results to file
• -q/--quiet: Suppress progress information
• -csv: Return CSV format (command-line only)

Module Catalog

Pick a module, then see references/module_examples.md for worked CLI + Python examples and references/module_reference.md for the full parameter table.

Module	Purpose	Queried source
ref	Reference genome download links/metadata	Ensembl
search	Find genes by name/description	Ensembl
info	Gene/transcript metadata (~1000 IDs max)	Ensembl, UniProt, NCBI
seq	Nucleotide/amino-acid sequences (FASTA)	Ensembl
blast	BLAST against standard databases	NCBI BLAST
blat	Genomic position of a sequence	UCSC BLAT
muscle	Multiple sequence alignment	Muscle5 (local)
diamond	Fast local protein/translated alignment	DIAMOND (local)
pdb	Experimental protein structures + metadata	RCSB PDB
alphafold	Predict 3D protein structure (setup req.)	AlphaFold2 (local)
elm	Eukaryotic linear motifs (setup req.)	ELM
archs4	Correlated genes / tissue expression	ARCHS4
cellxgene	Single-cell RNA-seq (setup req.)	CZ CELLxGENE Census
enrichr	Ontology/pathway enrichment	Enrichr
bgee	Orthologs and expression	Bgee
opentargets	Disease/drug associations	OpenTargets
cbio	Cancer genomics heatmaps	cBioPortal
cosmic	Somatic cancer mutations (license/account)	COSMIC
mutate	Generate mutated sequences	local
gpt	Natural-language text generation (setup req.)	OpenAI API
setup	Install third-party deps for a module	local

Setup-required modules (gget setup <module> before first use): alphafold (~4GB params, needs uv pip install openmm first), cellxgene, elm, gpt.

Routing

• Quick interactive lookup (gene info, BLAST, one structure, one enrichment) → use gget directly; see references/module_examples.md.
• Batch processing / advanced BLAST → use the biopython skill.
• Multi-database Python workflows → use the bioservices skill.
• Chaining several gget modules into a pipeline → see references/workflows.md and the ready-made scripts/ (gene_analysis, batch_sequence_analysis, enrichment_pipeline).

Best Practices (essentials)

• Use --limit to bound large queries; save with -o/--out for reproducibility.
• Gene symbols are case-sensitive in cellxgene ('PAX7' vs 'Pax7').
• Run gget setup before first use of alphafold, cellxgene, elm, gpt.
• Process max ~1000 Ensembl IDs at once with gget info.
• Database structures change; keep gget updated: uv pip install --upgrade gget.
• Use virtual environments to avoid dependency conflicts.

Output Formats

• Command-line: JSON default; -csv for CSV; FASTA (seq, mutate); PDB (pdb, alphafold); PNG (cbio plot).
• Python: DataFrame/dict default; json=True for JSON; save=True or out="filename" to write; AnnData for cellxgene.

References

• references/module_examples.md — worked CLI + Python examples for every module
• references/module_reference.md — full parameter tables for all modules
• references/database_info.md — queried databases and their update frequencies
• references/workflows.md — extended multi-module workflow examples

For additional help:

• Official documentation: https://pachterlab.github.io/gget/
• GitHub issues: https://github.com/pachterlab/gget/issues
• Citation: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836