gget Skill | Agent Skills

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

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