gget

gget

Unified CLI and Python access to 20+ genomic databases. All modules work as both command-line tools and Python functions.

Installation

uv pip install --upgrade gget

Some modules require setup: gget setup alphafold|cellxgene|elm|gpt

Quick Start

# CLI: gget <module> [arguments]
gget search -s human BRCA1
gget info ENSG00000012048
gget seq ENSG00000012048 -t   # protein sequence

# Python: gget.module(arguments)
import gget
gget.search(["BRCA1"], species="homo_sapiens")
gget.info(["ENSG00000012048"])

Common flags: -o (save to file), -csv (CSV output), -q (quiet)

Supporting Files

• module_reference.md - Complete parameter reference for all 20+ modules
• database_info.md - Database descriptions and update frequencies
• workflows.md - Extended workflow examples

Scripts

• scripts/gene_analysis.py - Gene discovery to sequence analysis pipeline
• scripts/enrichment_pipeline.py - Gene list enrichment workflow
• scripts/batch_sequence_analysis.py - Batch BLAST/alignment processing

Module Overview

Reference & Gene Information

Module	What it does	Example
ref	Download reference genomes (Ensembl)	gget ref -w gtf -d human
search	Find genes by name/description	gget search -s human "GABA receptor"
info	Gene/transcript metadata (Ensembl+UniProt+NCBI)	gget info ENSG00000012048
seq	Nucleotide/protein sequences	gget seq -t ENSG00000012048

Sequence Analysis

Module	What it does	Example
blast	NCBI BLAST searches	gget blast MKWMFK... -db swissprot
blat	UCSC BLAT genomic mapping	gget blat ATCGATCG -a human
muscle	Multiple sequence alignment	gget muscle sequences.fasta
diamond	Fast local alignment	gget diamond query.fa -ref ref.fa

Structure & Protein

Module	What it does	Example
pdb	Query Protein Data Bank	gget pdb 7S7U
alphafold	Predict 3D structure (setup required)	gget alphafold MKWMFK...
elm	Eukaryotic linear motifs (setup required)	gget elm LIAQSIGQASFV

Expression & Disease

Module	What it does	Example
archs4	Correlated genes / tissue expression	gget archs4 -w tissue ACE2
cellxgene	Single-cell RNA-seq data (setup required)	gget cellxgene --gene ACE2 --tissue lung
enrichr	GO/pathway enrichment analysis	gget enrichr -db ontology ACE2 AGT
bgee	Orthologs / expression across species	gget bgee ENSG00000169194
opentargets	Disease & drug associations	gget opentargets ENSG00000169194
cbio	Cancer genomics (cBioPortal)	gget cbio search breast
cosmic	Somatic mutations (requires account)	gget cosmic EGFR

Other

Module	What it does
mutate	Generate mutated sequences from annotations
setup	Install module-specific dependencies

Key Workflows

Gene Discovery → Sequence Analysis

# Search → info → sequence → BLAST
results = gget.search(["GABA", "receptor"], species="homo_sapiens")
info = gget.info(results["ensembl_id"].tolist()[:5])
sequences = gget.seq(results["ensembl_id"].tolist()[:5], translate=True)
blast_hits = gget.blast(my_sequence, database="swissprot", limit=10)

Expression & Enrichment

# Tissue expression → correlated genes → enrichment
tissue_expr = gget.archs4("ACE2", which="tissue")
correlated = gget.archs4("ACE2", which="correlation")
enrichment = gget.enrichr(correlated["gene_symbol"].tolist()[:50], database="ontology", plot=True)

Enrichr Database Shortcuts

Shortcut	Database
pathway	KEGG_2021_Human
transcription	ChEA_2016
ontology	GO_Biological_Process_2021
diseases_drugs	GWAS_Catalog_2019
celltypes	PanglaoDB_Augmented_2021

Single-Cell Data

# Gene symbols are case-sensitive: 'PAX7' (human), 'Pax7' (mouse)
adata = gget.cellxgene(gene=["ACE2", "ABCA1"], tissue="lung", cell_type="epithelial cell")
# Filters: disease, development_stage, sex, assay, donor_id, ethnicity

Comparative Genomics

orthologs = gget.bgee("ENSG00000169194", type="orthologs")
human_seq = gget.seq("ENSG00000169194", translate=True)
alignment = gget.muscle([human_seq, mouse_seq])

Best Practices

• Use --limit to control result sizes
• Save results with -o for reproducibility
• Process max ~1000 Ensembl IDs at once with gget info
• Use gget diamond with --threads for faster local alignment; save DB with --diamond_db
• For gget muscle, use -s5 (Super5) for large datasets
• AlphaFold multimer: use -mr 20 for accuracy, -r for AMBER relaxation
• Update regularly: uv pip install --upgrade gget (databases change structure)

Attribution

Adapted from K-Dense-AI/claude-scientific-skills (BSD-2-Clause). Citation: Luebbert & Pachter (2023) Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836