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 0.30.5 (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:

Common flags across modules:

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

1. Reference & Gene Information

gget ref - Reference Genome Downloads

Retrieve download links and metadata for Ensembl reference genomes.

Parameters:

Examples:

# List available species
gget ref --list_species

# Get all reference files for human
gget ref homo_sapiens

# Download GTF and cDNA files for mouse
gget ref -w gtf,cdna -d mouse

# Python
gget.ref("homo_sapiens")
gget.ref("mus_musculus", which=["gtf", "cdna"], download=True)

gget search - Gene Search

Locate genes by name, description, and Ensembl synonyms across species.

Parameters:

Returns: ensembl_id, gene_name, ensembl_description, ext_ref_description, biotype, URL

Examples:

# Search for GABA-related genes in human
gget search -s human gaba gamma-aminobutyric

# Find specific gene, require all terms
gget search -s mouse -ao and pax7 transcription

# Python
gget.search(["gaba", "gamma-aminobutyric"], species="homo_sapiens")

gget info - Gene/Transcript Information

Retrieve comprehensive gene and transcript metadata from Ensembl, UniProt, and NCBI.

Parameters:

Returns: UniProt ID, NCBI gene ID, primary gene name, synonyms, protein names, descriptions, biotype, canonical transcript

Examples:

# Get info for multiple genes
gget info ENSG00000034713 ENSG00000104853 ENSG00000170296

# Include PDB IDs
gget info ENSG00000034713 -pdb

# Python
gget.info(["ENSG00000034713", "ENSG00000104853"], pdb=True)

gget seq - Sequence Retrieval

Fetch nucleotide or amino acid sequences for genes and transcripts.

Parameters:

Returns: FASTA format sequences

Examples:

# Get nucleotide sequences
gget seq ENSG00000034713 ENSG00000104853

# Get all protein isoforms
gget seq -t -iso ENSG00000034713

# Python
gget.seq(["ENSG00000034713"], translate=True, isoforms=True)

2. Sequence Analysis & Alignment

gget blast - BLAST Searches

BLAST nucleotide or amino acid sequences against standard databases.

Parameters:

Examples:

# BLAST protein sequence
gget blast MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR

# BLAST from file with specific database
gget blast sequence.fasta -db swissprot -l 10

# Python
gget.blast("MKWMFK...", database="swissprot", limit=10)

gget blat - BLAT Searches

Locate genomic positions of sequences using UCSC BLAT.

Parameters:

Returns: genome, query size, alignment positions, matches, mismatches, alignment percentage

Examples:

# Find genomic location in human
gget blat ATCGATCGATCGATCG

# Search in different assembly
gget blat -a mm39 ATCGATCGATCGATCG

# Python
gget.blat("ATCGATCGATCGATCG", assembly="mouse")

gget muscle - Multiple Sequence Alignment

Align multiple nucleotide or amino acid sequences using Muscle5.

Parameters:

Returns: Aligned sequences in ClustalW format or aligned FASTA (.afa)

Examples:

# Align sequences from file
gget muscle sequences.fasta -o aligned.afa

# Use Super5 for large dataset
gget muscle large_dataset.fasta -s5

# Python
gget.muscle("sequences.fasta", save=True)

gget diamond - Local Sequence Alignment

Perform fast local protein alignment or translated nucleotide-to-protein alignment using DIAMOND.

Parameters:

Returns: Identity percentage, sequence lengths, match positions, gap openings, E-values, bit scores

Examples:

# Align against reference
gget diamond GGETISAWESQME -ref reference.fasta -t 4

# Translate nucleotide query against amino acid reference
gget diamond query_nt.fasta -ref proteins.fasta --translated

# Python
gget.diamond("GGETISAWESQME", reference="reference.fasta", threads=4)
gget.diamond("ATGGGC...", reference="proteins.fasta", translated=True)

3. Structural & Protein Analysis

gget pdb - Protein Structures

Query RCSB Protein Data Bank for structure and metadata.

Parameters:

Returns: PDB format (structures) or JSON (metadata)

Examples:

# Download PDB structure
gget pdb 7S7U -o 7S7U.pdb

# Get metadata
gget pdb 7S7U -r entry

# Python
gget.pdb("7S7U", save=True)

gget alphafold - Protein Structure Prediction

Predict 3D protein structures using simplified AlphaFold2.

Setup Required:

# Installs modified third-party dependencies and downloads model parameters
gget setup alphafold

Parameters:

Returns: PDB structure file, JSON alignment error data, optional 3D visualization

Examples:

# Predict single protein structure
gget alphafold MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR

# Predict multimer with higher accuracy
gget alphafold sequence1.fasta -mr 20 -r

# Python with visualization
gget.alphafold("MKWMFK...", plot=True, show_sidechains=True)

# Multimer prediction
gget.alphafold(["sequence1", "sequence2"], multimer_recycles=20)

gget elm - Eukaryotic Linear Motifs

Predict Eukaryotic Linear Motifs in protein sequences.

Setup Required:

gget setup elm

Parameters:

Returns: Two outputs:

Examples:

# Predict motifs from sequence
gget elm LIAQSIGQASFV -o results

# Use UniProt accession with expanded info
gget elm --uniprot Q02410 -e

# Python
ortholog_df, regex_df = gget.elm("LIAQSIGQASFV")

4. Expression & Disease Data

gget archs4 - Gene Correlation & Tissue Expression

Query ARCHS4 database for correlated genes or tissue expression data.

Parameters:

Returns:

Examples:

# Get correlated genes
gget archs4 ACE2

# Get tissue expression
gget archs4 -w tissue ACE2

# Python
gget.archs4("ACE2", which="tissue")

gget cellxgene - Single-Cell RNA-seq Data

Query CZ CELLxGENE Discover Census for single-cell data.

Setup Required:

gget setup cellxgene

Parameters:

Returns: AnnData object with count matrices and metadata (or metadata-only dataframes)

Examples:

# Get single-cell data for specific genes and cell types
gget cellxgene --gene ACE2 ABCA1 --tissue lung --cell_type "mucus secreting cell" -o lung_data.h5ad

# Metadata only
gget cellxgene --gene PAX7 --tissue muscle --meta_only -o metadata.csv

# Python
adata = gget.cellxgene(gene=["ACE2", "ABCA1"], tissue="lung", cell_type="mucus secreting cell")

gget enrichr - Enrichment Analysis

Perform ontology enrichment analysis on gene lists using Enrichr.

Parameters:

Database Shortcuts:

Examples:

# Enrichment analysis for ontology
gget enrichr -db ontology ACE2 AGT AGTR1

# Save KEGG pathways
gget enrichr -db pathway ACE2 AGT AGTR1 -ko ./kegg_images/

# Python with plot
gget.enrichr(["ACE2", "AGT", "AGTR1"], database="ontology", plot=True)

gget bgee - Orthology & Expression

Retrieve orthology and gene expression data from Bgee database.

Parameters:

Returns:

Examples:

# Get orthologs
gget bgee ENSG00000169194

# Get expression data
gget bgee ENSG00000169194 -t expression

# Multiple genes
gget bgee ENSBTAG00000047356 ENSBTAG00000018317 -t expression

# Python
gget.bgee("ENSG00000169194", type="orthologs")

gget opentargets - Disease & Drug Associations

Retrieve disease and drug associations from OpenTargets.

Parameters:

Current notes:

Examples:

# Get associated diseases
gget opentargets ENSG00000169194 -r diseases -l 5

# Get associated drugs
gget opentargets ENSG00000169194 -r drugs -l 10

# Filter interactions by returned column names
gget opentargets ENSG00000169194 -r interactions --filters protein_a_id=P35225 --filters gene_b_id=ENSG00000077238

# Python
gget.opentargets("ENSG00000169194", resource="diseases", limit=5)
gget.opentargets(
    "ENSG00000169194",
    resource="interactions",
    filters={"protein_a_id": "P35225", "gene_b_id": "ENSG00000077238"},
)

gget cbio - cBioPortal Cancer Genomics

Plot cancer genomics heatmaps using cBioPortal data.

Two subcommands:

search - Find study IDs:

gget cbio search breast lung

plot - Generate heatmaps:

Parameters:

Examples:

# Search for studies
gget cbio search esophag ovary

# Create heatmap
gget cbio plot -s msk_impact_2017 -g AKT1 ALK BRAF -st tissue -vt mutation_occurrences

# Python
gget.cbio_search(["esophag", "ovary"])
gget.cbio_plot(["msk_impact_2017"], ["AKT1", "ALK"], stratification="tissue")

gget cosmic - COSMIC Database

Search COSMIC (Catalogue Of Somatic Mutations In Cancer) database.

Important: License fees apply for commercial use. Requires COSMIC account credentials.
Avoid passing COSMIC credentials directly as CLI arguments on shared systems because command-line arguments can be exposed in shell history, process listings, and logs. Prefer the interactive prompt (gget cosmic --download_cosmic ...) or named environment variables read inside Python.

Parameters:

Database download flags:

Examples:

# First download database; gget prompts for COSMIC email/password
gget cosmic --download_cosmic --cosmic_project cancer

# Then query
gget cosmic EGFR --cosmic_tsv_path "CancerMutationCensus_AllData_Tsv_v101_GRCh37/CancerMutationCensus_AllData_v101_GRCh37.tsv" -l 10

# Python
import os

gget.cosmic(
    searchterm=None,
    download_cosmic=True,
    cosmic_project="cancer",
    email=os.environ["COSMIC_EMAIL"],
    password=os.environ["COSMIC_PASSWORD"],
)
gget.cosmic("EGFR", cosmic_tsv_path="cosmic_data.tsv", limit=10)

5. Viral & Mouse Specificity Data

gget virus - Viral Sequence Downloads

Download viral nucleotide sequences plus linked metadata from INSDC sources via NCBI Virus, with optional GenBank metadata enrichment. Results are saved to an output folder as FASTA, CSV, JSONL, and a command summary file.

Parameters:

Important: Do not use --download_all_accessions without restrictive filters; it can attempt to download the entire Viruses taxonomy and consume substantial time, bandwidth, and disk.

Examples:

# Complete Zika genomes from human hosts
gget virus "Zika virus" --nuc_completeness complete --host human --out zika_data

# SARS-CoV-2 reference genome by accession
gget virus NC_045512.2 --is_accession --is_sars_cov2

# Python
gget.virus(
    "SARS-CoV-2",
    host="human",
    nuc_completeness="complete",
    min_seq_length=29000,
    genbank_metadata=True,
    is_sars_cov2=True,
    outfolder="covid_data",
)

gget 8cube - Mouse Specificity & Expression

Query 8cubeDB for snRNA-seq gene specificity metrics and normalized expression values across mouse strains, tissues, sexes, and individuals.

Subcommands:

Examples:

gget 8cube specificity Acsm2 ENSMUSG00000046623.9
gget 8cube psi_block Acsm2 --analysis_level Kidney --analysis_type "Sex:Celltype"
gget 8cube expression Gjb4 --analysis_level Across_tissues --analysis_type Strain

# Python
from gget import specificity, psi_block, gene_expression

specificity(["Acsm2", "ENSMUSG00000046623.9"])
psi_block(["Acsm2"], analysis_level="Kidney", analysis_type="Sex:Celltype")
gene_expression(["Gjb4"], analysis_level="Across_tissues", analysis_type="Strain")

6. Additional Tools

gget mutate - Generate Mutated Sequences

Generate mutated nucleotide sequences from mutation annotations.

Current scope: gget 0.29.1 simplified mutate to focus on applying standard mutation annotations to supplied nucleotide sequences and returning/saving mutated FASTA records. The broader variant-screening workflow moved upstream to the kvar project.

Parameters:

Returns: Mutated sequences in FASTA format

Examples:

# Single mutation
gget mutate ATCGCTAAGCT -m "c.4G>T"

# Multiple sequences with one mutation per sequence
gget mutate ATCGCTAAGCT TAGCTA -m "c.4G>T" "c.1_3inv" -o mutated.fasta

# Python
gget.mutate("ATCGCTAAGCT", "c.4G>T")
gget.mutate(["ATCGCTAAGCT", "TAGCTA"], ["c.4G>T", "c.1_3inv"], out="mutated.fasta")

gget gpt - OpenAI Text Generation

Generate natural language text using OpenAI's API.

Setup Required:

gget setup gpt

Important: Requires an OpenAI API key. Do not hard-code the key in notebooks, scripts, shell history, or committed files. Prefer a named environment variable such as OPENAI_API_KEY, and set monthly billing limits before use.

Parameters:

Examples:
For CLI usage, gget gpt expects the API key as an argument. Avoid this on shared systems because process arguments can be visible to other users.

# Python
import os

gget.gpt("Explain CRISPR", api_key=os.environ["OPENAI_API_KEY"])

gget setup - Install Dependencies

Install/download third-party dependencies for specific modules.

As of gget 0.29.2, gget setup tries uv pip install first for Python dependencies and falls back to pip install if uv is unavailable or fails.

Parameters:

Modules requiring setup:

Examples:

# Setup AlphaFold
gget setup alphafold

# Setup ELM with custom directory
gget setup elm -o /path/to/elm_data

# Python
gget.setup("alphafold")

Common Workflows

Workflow 1: Gene Discovery to Sequence Analysis

Find and analyze genes of interest:

# 1. Search for genes
results = gget.search(["GABA", "receptor"], species="homo_sapiens")

# 2. Get detailed information
gene_ids = results["ensembl_id"].tolist()
info = gget.info(gene_ids[:5])

# 3. Retrieve sequences
sequences = gget.seq(gene_ids[:5], translate=True)

Workflow 2: Sequence Alignment and Structure

Align sequences and predict structures:

# 1. Align multiple sequences
alignment = gget.muscle("sequences.fasta")

# 2. Find similar sequences
blast_results = gget.blast(my_sequence, database="swissprot", limit=10)

# 3. Predict structure
structure = gget.alphafold(my_sequence, plot=True)

# 4. Find linear motifs
ortholog_df, regex_df = gget.elm(my_sequence)

Workflow 3: Gene Expression and Enrichment

Analyze expression patterns and functional enrichment:

# 1. Get tissue expression
tissue_expr = gget.archs4("ACE2", which="tissue")

# 2. Find correlated genes
correlated = gget.archs4("ACE2", which="correlation")

# 3. Get single-cell data
adata = gget.cellxgene(gene=["ACE2"], tissue="lung", cell_type="epithelial cell")

# 4. Perform enrichment analysis
gene_list = correlated["gene_symbol"].tolist()[:50]
enrichment = gget.enrichr(gene_list, database="ontology", plot=True)

Workflow 4: Disease and Drug Analysis

Investigate disease associations and therapeutic targets:

# 1. Search for genes
genes = gget.search(["breast cancer"], species="homo_sapiens")

# 2. Get disease associations
diseases = gget.opentargets("ENSG00000169194", resource="diseases")

# 3. Get drug associations
drugs = gget.opentargets("ENSG00000169194", resource="drugs")

# 4. Query cancer genomics data
study_ids = gget.cbio_search(["breast"])
gget.cbio_plot(study_ids[:2], ["BRCA1", "BRCA2"], stratification="cancer_type")

# 5. Search COSMIC for mutations
cosmic_results = gget.cosmic("BRCA1", cosmic_tsv_path="cosmic.tsv")

Workflow 5: Comparative Genomics

Compare proteins across species:

# 1. Get orthologs
orthologs = gget.bgee("ENSG00000169194", type="orthologs")

# 2. Get sequences for comparison
human_seq = gget.seq("ENSG00000169194", translate=True)
mouse_seq = gget.seq("ENSMUSG00000026091", translate=True)

# 3. Align sequences
alignment = gget.muscle([human_seq, mouse_seq])

# 4. Compare structures
human_structure = gget.pdb("7S7U")
mouse_structure = gget.alphafold(mouse_seq)

Workflow 6: Building Reference Indices

Prepare reference data for downstream analysis (e.g., kallisto|bustools):

# 1. List available species
gget ref --list_species

# 2. Download reference files
gget ref -w gtf -w cdna -d homo_sapiens

# 3. Build kallisto index
kallisto index -i transcriptome.idx transcriptome.fasta

# 4. Download genome for alignment
gget ref -w dna -d homo_sapiens

Best Practices

Data Retrieval

Sequence Analysis

Expression and Disease Data

Structure Prediction

Viral Data

Error Handling

Output Formats

Command-line

Python

Resources

This skill includes reference documentation for detailed module information:

references/

For additional help: