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:

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)

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:

species: Genus_species format (e.g., 'homo_sapiens', 'mus_musculus'). Shortcuts: 'human', 'mouse'
-w/--which: Specify return types as comma-separated CLI values or Python list (gtf, cdna, dna, cds, cdrna, pep). Default: all
-r/--release: Ensembl release number (default: latest)
-od/--out_dir: Directory for downloaded files
-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 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:

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
wrap_text: Python-only display helper for wide DataFrames

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 alignment or translated nucleotide-to-protein alignment using DIAMOND.

Parameters:

Query: Sequences (string/list) or FASTA file path
-ref/--reference: Reference sequences (string/list) or FASTA file path (required)
-s/--sensitivity: fast, mid-sensitive, sensitive, more-sensitive, very-sensitive (default), ultra-sensitive
-t/--threads: CPU threads (default: 1)
-db/--diamond_db: Save database for reuse
-x/--translated: Enable nucleotide query to amino acid reference alignment

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:

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:

# Installs modified third-party dependencies and downloads model parameters
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
--filters: Exact-match filters using returned OpenTargets column names; repeat on the CLI or pass a Python dict
-or/--or: CLI-only; combine filters with OR logic instead of the default AND logic

Current notes:

gget 0.30.5 rewrote this module for the newer OpenTargets API; some output column names differ from older releases.
The older --filter_mode argument was removed upstream.

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:

-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. 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:

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, cancer_example, 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 for non-interactive downloads; prefer prompt or Python env vars

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:

virus: Virus taxon name, taxon ID, accession, space-separated accessions, or path to a text file of accessions
-a/--is_accession: Treat virus as accession input
--is_sars_cov2, --is_alphainfluenza: Use optimized cached NCBI datasets paths for SARS-CoV-2 or Influenza A
--host: Host organism name or NCBI taxonomy ID
--nuc_completeness: complete or partial
--min_seq_length, --max_seq_length: Sequence length filters
-g/--genbank_metadata: Fetch detailed GenBank metadata; auto-enabled by some annotation filters
--segment, --vaccine_strain, --annotated, --lab_passaged, --source_database: Common viral metadata filters
--download_all_accessions: Apply filters across all viral accessions
--baseline, --merge-results: Resume or merge with prior metadata from partial/previous runs

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:

gget 8cube specificity <genes...>: Return gene-level psi/zeta specificity statistics
gget 8cube psi_block <genes...> --analysis_level <level> --analysis_type <type>: Return block-level specificity
gget 8cube expression <genes...> --analysis_level <level> --analysis_type <type>: Return mean/variance normalized expression

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:

sequences: FASTA file path or direct nucleotide sequence input (string/list)
-m/--mutations: Mutation string/list, CSV/TSV path, 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 (default: same as mut_column)
-k/--k: Length of flanking sequences (default: 30 nucleotides)
-o/--out: Output FASTA path; without it Python returns a list of mutated sequences

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:

prompt: Text input for generation (required)
api_key: OpenAI authentication (required by the upstream API)
Model configuration: model, temperature, top_p, stop, max_tokens, frequency_penalty, presence_penalty, logit_bias
Default model: gpt-3.5-turbo (upstream default; verify available models in your OpenAI account)

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:

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 require Python 3.9/3.10 if the latest Python is unsupported)
elm - Downloads local ELM database
gpt - Installs/configures OpenAI integration dependencies

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
For OpenTargets, inspect returned column names before writing filters; gget 0.30.5 follows the newer OpenTargets API schema

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

Viral Data

Use restrictive filters with gget virus before requesting broad viral datasets
Keep command_summary.txt with downstream results for reproducibility and recovery after partial downloads
Use --baseline and --merge-results to resume interrupted viral metadata/sequence downloads

Error Handling

Database structures change; when an adapter breaks, check upstream release notes and pin the newer fixed version explicitly
Pin the known-good version for reproducible environments: uv pip install "gget==0.30.5"
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
Keep COSMIC and OpenAI credentials in named environment variables or interactive prompts; do not write real credentials into examples, notebooks, or logs

Output Formats

Command-line

Default: JSON
CSV: Add -csv flag
FASTA: gget seq, gget mutate
PDB: gget pdb, gget alphafold
PNG: gget cbio plot
FASTA/CSV/JSONL folder: gget virus

Python

Default: DataFrame or dictionary
JSON: Add json=True parameter
Save to file: Add save=True or specify out="filename"
AnnData: gget cellxgene
DataFrame/JSON: gget 8cube specificity, psi_block, expression

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

skills/gget

gget

Overview

Installation

Quick Start

Module Categories

1. Reference & Gene Information

gget ref - Reference Genome Downloads

gget search - Gene Search

gget info - Gene/Transcript Information

gget seq - Sequence Retrieval

2. Sequence Analysis & Alignment

gget blast - BLAST Searches

gget blat - BLAT Searches

gget muscle - Multiple Sequence Alignment

gget diamond - Local Sequence Alignment

3. Structural & Protein Analysis

gget pdb - Protein Structures

gget alphafold - Protein Structure Prediction

gget elm - Eukaryotic Linear Motifs

4. Expression & Disease Data

gget archs4 - Gene Correlation & Tissue Expression

gget cellxgene - Single-Cell RNA-seq Data

gget enrichr - Enrichment Analysis

gget bgee - Orthology & Expression

gget opentargets - Disease & Drug Associations

gget cbio - cBioPortal Cancer Genomics

gget cosmic - COSMIC Database

5. Viral & Mouse Specificity Data

gget virus - Viral Sequence Downloads

gget 8cube - Mouse Specificity & Expression

6. Additional Tools

gget mutate - Generate Mutated Sequences

gget gpt - OpenAI Text Generation

gget setup - Install Dependencies

Common Workflows

Workflow 1: Gene Discovery to Sequence Analysis

Workflow 2: Sequence Alignment and Structure

Workflow 3: Gene Expression and Enrichment

Workflow 4: Disease and Drug Analysis

Workflow 5: Comparative Genomics

Workflow 6: Building Reference Indices

Best Practices

Data Retrieval

Sequence Analysis

Expression and Disease Data

Structure Prediction

Viral Data

Error Handling

Output Formats

Command-line

Python

Resources

references/