Alterlab Gget

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.

Project home: development moved to the scverse organisation (github.com/scverse/gget); the manual stays at pachterlab.github.io/gget.

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:

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# 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:

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# 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.

cbio is exposed in Python as gget.cbio_search() and gget.cbio_plot().

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

When to Use This Skill

• 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.

Does NOT Trigger

• 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

Part of the AlterLab Academic Skills suite.