/gget

name: gget
description: "Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices."
license: BSD-2-Clause license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python >=3.8 and gget 0.30.5-compatible APIs. Optional setup modules may install scientific dependencies that lag the newest Python releases; use Python 3.9 or 3.10 if `gget setup cellxgene` or `gget setup alphafold` fails.
metadata:
  version: "1.5"
  skill-author: K-Dense Inc.

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 0.30.5 (PyPI current as of 2026-06-07). For reproducible work, pin `gget==0.30.5`; 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 .venv
source .venv/bin/activate
uv pip install "gget==0.30.5"

# 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)

Python argument names generally match long CLI options without leading dashes. For example, `--census_version` becomes `census_version=...`. Use `gget <module> --help` for the exact current signature.

Module Categories

gget exposes 23 modules in six categories. Parameters, CLI and Python examples, and return shapes for every one are in [references/module_catalog.md](references/module_catalog.md); fuller per-parameter documentation is in [references/module_reference.md](references/module_reference.md).

| Category | Modules | | --- | --- | | 1. Reference & gene information | `ref` (Ensembl reference downloads), `search` (gene search), `info` (gene/transcript detail), `seq` (nucleotide and protein sequences) | | 2. Sequence analysis & alignment | `blast`, `blat`, `muscle` (multiple alignment), `diamond` (local alignment) | | 3. Structural & protein analysis | `pdb` (structures and metadata), `alphafold` (structure prediction), `elm` (linear motifs) | | 4. Expression & disease data | `archs4` (correlation, tissue expression), `cellxgene` (single-cell), `enrichr` (enrichment), `bgee` (orthology and expression), `opentargets` (disease and drug), `cbio` (cancer genomics), `cosmic` (mutations) | | 5. Viral & mouse specificity | `virus` (viral sequences), `8cube` (mouse specificity and expression) | | 6. Additional tools | `mutate` (mutated sequences), `gpt` (text generation), `setup` (install module dependencies) |

Several modules need a one-time `gget setup` before first use (`alphafold`, `elm`, `cellxgene`), 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](references/common_workflows.md), with longer versions in [references/workflows.md](references/workflows.md).

Best Practices

Data Retrieval

• Use `--limit` to control result sizes for large queries
• 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` for repeated queries
• 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')
• Run `gget setup` before first use of alphafold, cellxgene, elm, gpt
• For enrichment analysis, use database shortcuts for convenience
• Cache cBioPortal data with `-dd` to avoid repeated downloads
• For OpenTargets, inspect returned column names before writing filters; gget 0.30.5 follows the newer OpenTargets API schema

Structure Prediction

• AlphaFold multimer predictions: use `-mr 20` for higher accuracy
• Use `-r` flag for AMBER relaxation of final structures
• Visualize results in Python with `plot=True`
• Check PDB database first before running AlphaFold predictions

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

Error Handling

• Database structures change; when an adapter breaks, check upstream release notes and pin the newer fixed version explicitly
• Pin the known-good version for reproducible environments: `uv