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, expression data, and disease associations through a consistent interface. All 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. gget modules are tested automatically on a biweekly basis and updated to match new database structures when necessary.

Installation

Install gget in a clean virtual environment to avoid conflicts:

# Using uv (recommended)
uv uv pip install gget
Or using pip

uv pip install --upgrade gget
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)

Module Categories

1. Reference & Gene Information

gget ref - Reference Genome Downloads

Retrieve download links and metadata for Ensembl reference genomes.

Parameters:

species: Genus_species format (e.g., 'homo_sapiens', 'mus_musculus'). Shortcuts: 'human', 'mouse'

-w/--which: Specify return types (gtf, cdna, dna, cds, cdrna, pep). Default: all

-r/--release: Ensembl release number (default: latest)

-l/--list_species: List available vertebrate species

-liv/--list_iv_species: List available invertebrate species

-ftp: Return only FTP links

-d/--download: Download files (requires curl)

Examples:

# List available species
gget ref --list_species
Get all reference files for human

gget ref homo_sapiens
Download only GTF annotation for mouse

gget ref -w gtf -d mouse

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

gget search - Gene Search

Locate genes by name or description across species.

Parameters:

searchwords: One or more search terms (case-insensitive)

-s/--species: Target species (e.g., 'homo_sapiens', 'mouse')

-r/--release: Ensembl release number

-t/--id_type: Return 'gene' (default) or 'transcript'

-ao/--andor: 'or' (default) finds ANY searchword; 'and' requires ALL

-l/--limit: Maximum results to return

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:

ens_ids: One or more Ensembl IDs (also supports WormBase, Flybase IDs). Limit: ~1000 IDs

-n/--ncbi: Disable NCBI data retrieval

-u/--uniprot: Disable UniProt data retrieval

-pdb: Include PDB identifiers (increases runtime)

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:

ens_ids: One or more Ensembl identifiers

-t/--translate: Fetch amino acid sequences instead of nucleotide

-iso/--isoforms: Return all transcript variants (gene IDs only)

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:

sequence: Sequence string or path to FASTA/.txt file

-p/--program: blastn, blastp, blastx, tblastn, tblastx (auto-detected)

-db/--database:

- Nucleotide: nt, refseq_rna, pdbnt
- Protein: nr, swissprot, pdbaa, refseq_protein

-l/--limit: Max hits (default: 50)

-e/--expect: E-value cutoff (default: 10.0)

-lcf/--low_comp_filt: Enable low complexity filtering

-mbo/--megablast_off: Disable MegaBLAST (blastn only)

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:

sequence: Sequence string or path to FASTA/.txt file

-st/--seqtype: 'DNA', 'protein', 'translated%20RNA', 'translated%20DNA' (auto-detected)

-a/--assembly: Target assembly (default: 'human'/hg38; options: 'mouse'/mm39, 'zebrafinch'/taeGut2, etc.)

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:

fasta: Sequences or path to FASTA/.txt file

-s5/--super5: Use Super5 algorithm for faster processing (large datasets)

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 or translated DNA alignment using DIAMOND.

Parameters:

Query: Sequences (string/list) or FASTA file path

--reference: Reference sequences (string/list) or FASTA file path (required)

--sensitivity: fast, mid-sensitive, sensitive, more-sensitive, very-sensitive (default), ultra-sensitive

--threads: CPU threads (default: 1)

--diamond_db: Save database for reuse

--translated: Enable nucleotide-to-amino acid alignment

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

Examples:

# Align against reference
gget diamond GGETISAWESQME -ref reference.fasta --threads 4
Save database for reuse

gget diamond query.fasta -ref ref.fasta --diamond_db my_db.dmnd

# Python
gget.diamond("GGETISAWESQME", reference="reference.fasta", threads=4)

3. Structural & Protein Analysis

gget pdb - Protein Structures

Query RCSB Protein Data Bank for structure and metadata.

Parameters:

pdb_id: PDB identifier (e.g., '7S7U')

-r/--resource: Data type (pdb, entry, pubmed, assembly, entity types)

-i/--identifier: Assembly, entity, or chain ID

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:

# Install OpenMM first
uv pip install openmm
Then setup AlphaFold

gget setup alphafold

Parameters:

sequence: Amino acid sequence (string), multiple sequences (list), or FASTA file. Multiple sequences trigger multimer modeling

-mr/--multimer_recycles: Recycling iterations (default: 3; recommend 20 for accuracy)

-mfm/--multimer_for_monomer: Apply multimer model to single proteins

-r/--relax: AMBER relaxation for top-ranked model

plot: Python-only; generate interactive 3D visualization (default: True)

show_sidechains: Python-only; include side chains (default: True)

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:

sequence: Amino acid sequence or UniProt Acc

-u/--uniprot: Indicates sequence is UniProt Acc

-e/--expand: Include protein names, organisms, references

-s/--sensitivity: DIAMOND alignment sensitivity (default: "very-sensitive")

-t/--threads: Number of threads (default: 1)

Returns: Two outputs:

ortholog_df: Linear motifs from orthologous proteins

regex_df: Motifs directly matched in input sequence

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:

gene: Gene symbol or Ensembl ID (with --ensembl flag)

-w/--which: 'correlation' (default, returns 100 most correlated genes) or 'tissue' (expression atlas)

-s/--species: 'human' (default) or 'mouse' (tissue data only)

-e/--ensembl: Input is Ensembl ID

Returns:

Correlation mode: Gene symbols, Pearson correlation coefficients

Tissue mode: Tissue identifiers, min/Q1/median/Q3/max expression values

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:

--gene (-g): Gene names or Ensembl IDs (case-sensitive! 'PAX7' for human, 'Pax7' for mouse)

--tissue: Tissue type(s)

--cell_type: Specific cell type(s)

--species (-s): 'homo_sapiens' (default) or 'mus_musculus'

--census_version (-cv): Version ("stable", "latest", or dated)

--ensembl (-e): Use Ensembl IDs

--meta_only (-mo): Return metadata only

Additional filters: disease, development_stage, sex, assay, dataset_id, donor_id, ethnicity, suspension_type

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:

genes: Gene symbols or Ensembl IDs

-db/--database: Reference database (supports shortcuts: 'pathway', 'transcription', 'ontology', 'diseases_drugs', 'celltypes')

-s/--species: human (default), mouse, fly, yeast, worm, fish

-bkg_l/--background_list: Background genes for comparison

-ko/--kegg_out: Save KEGG pathway images with highlighted genes

plot: Python-only; generate graphical results

Database Shortcuts:

'pathway' → KEGG_2021_Human

'transcription' → ChEA_2016

'ontology' → GO_Biological_Process_2021

'diseases_drugs' → GWAS_Catalog_2019

'celltypes' → PanglaoDB_Augmented_2021

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:

ens_id: Ensembl gene ID or NCBI gene ID (for non-Ensembl species). Multiple IDs supported when type=expression

-t/--type: 'orthologs' (default) or 'expression'

Returns:

Orthologs mode: Matching genes across species with IDs, names, taxonomic info

Expression mode: Anatomical entities, confidence scores, expression status

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:

Ensembl gene ID (required)

-r/--resource: diseases (default), drugs, tractability, pharmacogenetics, expression, depmap, interactions

-l/--limit: Cap results count

Filter arguments (vary by resource):

- drugs: --filter_disease
- pharmacogenetics: --filter_drug
- expression/depmap: --filter_tissue, --filter_anat_sys, --filter_organ
- interactions: --filter_protein_a, --filter_protein_b, --filter_gene_b

Examples:

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

gget opentargets ENSG00000169194 -r drugs -l 10
Get tissue expression

gget opentargets ENSG00000169194 -r expression --filter_tissue brain

# Python
gget.opentargets("ENSG00000169194", resource="diseases", limit=5)

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:

-s/--study_ids: Space-separated cBioPortal study IDs (required)

-g/--genes: Space-separated gene names or Ensembl IDs (required)

-st/--stratification: Column to organize data (tissue, cancer_type, cancer_type_detailed, study_id, sample)

-vt/--variation_type: Data type (mutation_occurrences, cna_nonbinary, sv_occurrences, cna_occurrences, Consequence)

-f/--filter: Filter by column value (e.g., 'study_id:msk_impact_2017')

-dd/--data_dir: Cache directory (default: ./gget_cbio_cache)

-fd/--figure_dir: Output directory (default: ./gget_cbio_figures)

-dpi: Resolution (default: 100)

-sh/--show: Display plot in window

-nc/--no_confirm: Skip download confirmations

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.

Parameters:

searchterm: Gene name, Ensembl ID, mutation notation, or sample ID

-ctp/--cosmic_tsv_path: Path to downloaded COSMIC TSV file (required for querying)

-l/--limit: Maximum results (default: 100)

Database download flags:

-d/--download_cosmic: Activate download mode

-gm/--gget_mutate: Create version for gget mutate

-cp/--cosmic_project: Database type (cancer, census, cell_line, resistance, genome_screen, targeted_screen)

-cv/--cosmic_version: COSMIC version

-gv/--grch_version: Human reference genome (37 or 38)

--email, --password: COSMIC credentials

Examples:

# First download database
gget cosmic -d --email user@example.com --password xxx -cp cancer
Then query

gget cosmic EGFR -ctp cosmic_data.tsv -l 10

# Python
gget.cosmic("EGFR", cosmic_tsv_path="cosmic_data.tsv", limit=10)

5. Additional Tools

gget mutate - Generate Mutated Sequences

Generate mutated nucleotide sequences from mutation annotations.

Parameters:

sequences: FASTA file path or direct sequence input (string/list)

-m/--mutations: CSV/TSV file or DataFrame with mutation data (required)

-mc/--mut_column: Mutation column name (default: 'mutation')

-sic/--seq_id_column: Sequence ID column (default: 'seq_ID')

-mic/--mut_id_column: Mutation ID column

-k/--k: Length of flanking sequences (default: 30 nucleotides)

Returns: Mutated sequences in FASTA format

Examples:

# Single mutation
gget mutate ATCGCTAAGCT -m "c.4G>T"
Multiple sequences with mutations from file

gget mutate sequences.fasta -m mutations.csv -o mutated.fasta

# Python
import pandas as pd
mutations_df = pd.DataFrame({"seq_ID": ["seq1"], "mutation": ["c.4G>T"]})
gget.mutate(["ATCGCTAAGCT"], mutations=mutations_df)

gget gpt - OpenAI Text Generation

Generate natural language text using OpenAI's API.

Setup Required:

gget setup gpt

Important: Free tier limited to 3 months after account creation. Set monthly billing limits.

Parameters:

prompt: Text input for generation (required)

api_key: OpenAI authentication (required)

Model configuration: temperature, top_p, max_tokens, frequency_penalty, presence_penalty

Default model: gpt-3.5-turbo (configurable)

Examples:

gget gpt "Explain CRISPR" --api_key your_key_here

# Python
gget.gpt("Explain CRISPR", api_key="your_key_here")

gget setup - Install Dependencies

Install/download third-party dependencies for specific modules.

Parameters:

module: Module name requiring dependency installation

-o/--out: Output folder path (elm module only)

Modules requiring setup:

alphafold - Downloads ~4GB of model parameters

cellxgene - Installs cellxgene-census (may not support latest Python)

elm - Downloads local ELM database

gpt - Configures OpenAI integration

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

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

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

Error Handling

Database structures change; update gget regularly: uv pip install --upgrade gget

Process max ~1000 Ensembl IDs at once with gget info

For large-scale analyses, implement rate limiting for API queries

Use virtual environments to avoid dependency conflicts

Output Formats

Command-line

Default: JSON

CSV: Add -csv flag

FASTA: gget seq, gget mutate

PDB: gget pdb, gget alphafold

PNG: gget cbio plot

Python

Default: DataFrame or dictionary

JSON: Add json=True parameter

Save to file: Add save=True or specify out="filename"

AnnData: gget cellxgene

Resources

This skill includes reference documentation for detailed module information:

references/

module_reference.md - Comprehensive parameter reference for all modules

database_info.md - Information about queried databases and their update frequencies

workflows.md - Extended workflow examples and use cases

For additional help:

Official documentation: https://pachterlab.github.io/gget/

GitHub issues: https://github.com/pachterlab/gget/issues

Citation: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836

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