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