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, viral sequences, expression data, disease associations, and mouse tissue/cell specificity metrics through a consistent interface. Most 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. Guidance here targets gget 0.30.8 (reviewed 2026-09-30). For reproducible work, pin gget==0.30.8; for broken upstream database adapters, update gget after checking release notes.

Installation

Install gget in a clean virtual environment to avoid conflicts:

# Reproducible install targeting this skill
uv venv --python 3.13 .venv
source .venv/bin/activate
uv pip install "gget==0.30.8"

import gget

Quick Start

Inspect module-specific syntax before querying:

gget info --help
gget pdb --help

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)

Python argument names generally match long CLI options without leading dashes. For example, --census_version becomes census_version=.... Use gget <module> --help for CLI syntax and inspect.signature(gget.<module>) for Python; CLI flags such as --download may have no Python equivalent.

Module Categories

gget exposes 24 modules in six categories. Parameters, CLI and Python examples, and return shapes for every one are in references/module_catalog.md; fuller per-parameter documentation is in references/module_reference.md.

Several modules need a one-time gget setup before first use (elm, cellxgene, cbio; legacy alphafold/gpt), and cosmic prompts for COSMIC credentials to download its database.

Common Workflows

Worked multi-module pipelines — gene characterization, structural comparison, expression and enrichment analysis, disease and drug association, orthology comparison, and reference-file preparation for kallisto or alignment — are in references/common_workflows.md, with longer versions in references/workflows.md.

Best Practices

Data Retrieval

• Use --limit where supported; it is often a local cap, not a complete pagination mechanism
• Save results with -o/--out for reproducibility
• Check database versions/releases for consistency across analyses
• Use --quiet in production scripts to reduce output

Sequence Analysis

• For BLAST/BLAT, start with default parameters, then adjust sensitivity
• Use gget diamond with --threads for faster local alignment
• Save DIAMOND databases with --diamond_db; gget still requires the reference input
• For multiple sequence alignment, use -s5/--super5 for large datasets

Expression and Disease Data

• Gene symbols are case-sensitive in cellxgene (e.g., 'PAX7' vs 'Pax7')
• Install optional dependencies for cellxgene, elm, and cbio with gget setup <module>
• For enrichment, record the full library name and release, not only a shortcut: the adapter maps shortcuts to specific dated libraries, and non-human/mouse species need full species-specific names. For human/mouse, supply the tested-gene universe through background_list when appropriate; custom backgrounds are not supported for the other species. Report mapped/unmapped query and background counts and adjusted p-values, so identifier loss and selection bias are visible.
• Cache cBioPortal data with -dd to avoid repeated downloads
• Open Targets filters are local and applied after limit; expression fetches only the first page (at most 3000 rows). Other paginated resources use the server default page. Do not treat results as exhaustive.
• gget.info returns IDs in its index; preserve that index when saving CSV. ARCHS4 tissue labels use id, correlations use pearson_correlation; Enrichr uses path_name and adj_p_val.
• gget.search matches descriptions and synonyms; select an exact gene_name and reject ambiguity instead of taking the first result.
• Census meta_only=True returns cells, not datasets, and ignores the gene filter. Scope by dataset/cell metadata and pin a dated Census release.

Structures and residue annotations

• Prefer gget.pdb(..., resource="mmcif") for explicit structure format; the PDB default can fall back to mmCIF.
• Use gget.g2p for existing residue annotations and isoform/structure maps; specify accessions for exact isoform identity.
• alphafold and gpt are deprecated and no longer maintained upstream. Existing examples are legacy/illustrative, not tested prediction or generation workflows.

Viral Data

• Use restrictive filters with gget virus before requesting broad viral datasets
• Keep command_summary.txt with downstream results for reproducibility and recovery after partial downloads
• Use --baseline and --merge-results to resume interrupted viral metadata/sequence downloads