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 0.30.8 (PyPI current). For reproducible work, pin gget==0.30.8; for broken upstream database adapters, update gget after checking release notes. Note: gget 0.30.7 raised the minimum supported Python version to 3.12 and marked gget alphafold/gget gpt as no longer actively maintained (see their sections below); it also changed gget opentargets's expression resource output columns to match a new upstream Open Targets data model (per-biosample statistics rather than per-tissue RNA values).

Installation

Install gget in a clean virtual environment to avoid conflicts:

# Reproducible install targeting this skill (Python >=3.12 required for 0.30.7+)
uv venv --python 3.12 .venv
source .venv/bin/activate
uv pip install "gget==0.30.8"

# 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, mmcif, entry, pubmed, assembly, entity types). Default pdb. As of gget 0.30.8, large/legacy entries may auto-fall back from PDB to PDBx/mmCIF when the legacy .pdb file is unavailable; use resource="mmcif" to request mmCIF directly.
• -i/--identifier: Assembly, entity, or chain ID

Returns: Structure payload (PDB or mmCIF/CIF depending on resource and fallback) or JSON (metadata)

Examples:

# Download PDB structure (may return/save .cif if RCSB has no legacy .pdb)
gget pdb 7S7U -o 7S7U.pdb

# Request PDBx/mmCIF explicitly (preferred for large entries)
gget pdb 7S7U -r mmcif -o 7S7U.cif

# Get metadata
gget pdb 7S7U -r entry

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

gget alphafold - Protein Structure Prediction

Predict 3D protein structures using simplified AlphaFold2.

Deprecation notice: As of gget 0.30.7, gget alphafold is no longer actively maintained upstream and emits a warning when invoked. It still works and gained a jackhmmer_savedir argument in 0.30.8, but consider gget.pdb/other current structure-prediction tools for new work.

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
• -jhd/--jackhmmer_savedir: Directory for jackhmmer temp files (~2 GB). Added in 0.30.8 so you can redirect the cache off a small home partition. Python: jackhmmer_savedir=
• 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; redirect jackhmmer cache
gget alphafold sequence1.fasta -mr 20 -r -jhd /tmp/jackhmmer_cache

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

# Multimer prediction with custom jackhmmer cache
gget.alphafold(
    ["sequence1", "sequence2"],
    multimer_recycles=20,
    jackhmmer_savedir="/tmp/jackhmmer_cache",
)

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:

1. ortholog_df: Linear motifs from orthologous proteins
2. 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.
• gget 0.30.7/0.30.8 changed the expression resource's output columns again: Open Targets retired the Target.expressions field, so -r expression now returns per-biosample statistics rather than per-tissue RNA values. Inspect returned column names before reusing older expression parsing code.

Examples:

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

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

# Filter interactions by current returned column names (0.30.7+);
# older docs used protein_a_id/gene_b_id — those keys now raise
# missing-filter-key errors. Prefer sourceDatabase / targetB.* after
# inspecting the DataFrame columns for your resource.
gget opentargets ENSG00000169194 -r interactions \
  --filters sourceDatabase=intact \
  --filters targetB.id=ENSG00000077238

# Python
gget.opentargets("ENSG00000169194", resource="diseases", limit=5)
gget.opentargets(
    "ENSG00000169194",
    resource="interactions",
    filters={
        "sourceDatabase": "intact",
        "targetB.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.

Deprecation notice: As of gget 0.30.7, gget gpt is no longer actively maintained upstream and emits a warning when invoked. It is not gget-specific functionality (a thin OpenAI API wrapper), so calling the OpenAI SDK directly is a reasonable current alternative.

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; the API schema has changed across gget 0.30.5 and again in 0.30.7/0.30.8 (notably for the expression resource)

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==0.30.8"
• 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