Gget genomic databases

gget — Unified Genomic Database Access

Overview

gget is a command-line and Python package providing unified access to 20+ genomic databases and analysis methods. Query gene information, sequences, protein structures, expression data, and disease associations through a consistent interface. All modules work as both CLI tools and Python functions, returning DataFrames (Python) or JSON/CSV (CLI).

When to Use

• Looking up gene information (names, IDs, descriptions) across species from Ensembl
• Retrieving nucleotide or protein sequences for Ensembl gene/transcript IDs
• Running BLAST or BLAT searches against standard reference databases
• Predicting protein 3D structures with AlphaFold2 from amino acid sequences
• Performing gene set enrichment analysis (GO, KEGG, disease terms) via Enrichr
• Querying single-cell RNA-seq datasets from CELLxGENE Census
• Finding disease and drug associations for a gene target via OpenTargets
• Downloading Ensembl reference genomes and annotations for a species
• Finding cancer mutations and genomic alterations via cBioPortal or COSMIC
• Getting tissue expression and correlated genes from ARCHS4
• For batch processing or advanced BLAST parameters, use biopython instead
• For programmatic multi-database workflows with rate limiting, use bioservices instead

Prerequisites

• Python packages: gget
• Optional setup: Some modules require gget setup <module> before first use (alphafold, cellxgene, elm, gpt)
• Environment: Clean virtual environment recommended to avoid dependency conflicts
• API notes: gget queries remote databases — rate-limit large batch queries with time.sleep(). Databases update biweekly; keep gget updated. Max ~1000 Ensembl IDs per gget.info() call

pip install gget

# Optional: setup modules that need additional dependencies
gget setup alphafold   # ~4GB model parameters, requires OpenMM
gget setup cellxgene   # cellxgene-census package
gget setup elm         # local ELM database

Quick Start

import gget

# Search for genes by keyword
results = gget.search(["BRCA1", "tumor suppressor"], species="homo_sapiens")
print(f"Found {len(results)} genes")

# Get detailed gene information (Ensembl + UniProt + NCBI)
info = gget.info(["ENSG00000012048"])
print(f"Gene: {info.iloc[0]['primary_gene_name']}")

# Enrichment analysis on a gene list
enrichment = gget.enrichr(["ACE2", "AGT", "AGTR1"], database="ontology")
print(f"Enriched terms: {len(enrichment)}")

Core API

Module 1: Reference & Gene Search (ref, search, info, seq)

Query Ensembl for gene references, search by keywords, retrieve gene metadata, and fetch sequences.

import gget

# Search for genes by keyword
results = gget.search(["BRCA1", "tumor suppressor"], species="homo_sapiens")
print(f"Found {len(results)} genes")
print(results[["ensembl_id", "gene_name", "biotype"]].head())

# Get detailed gene information (Ensembl + UniProt + NCBI)
info = gget.info(["ENSG00000012048", "ENSG00000139618"])
print(f"Gene info columns: {list(info.columns)}")

import gget

# Retrieve sequences
nucleotide_seqs = gget.seq(["ENSG00000012048"])
protein_seqs = gget.seq(["ENSG00000012048"], translate=True, isoforms=True)
print(f"Retrieved {len(protein_seqs)} isoform sequences")

# Download reference genome files (specify release for reproducibility)
ref_links = gget.ref("homo_sapiens", which="gtf", release=112)
print(f"GTF download link: {ref_links}")

Module 2: Sequence Alignment (blast, blat, muscle, diamond)

BLAST/BLAT remote searches, multiple sequence alignment, and fast local alignment.

import gget
import time

# BLAST against SwissProt (remote API — add delay for batch queries)
blast_results = gget.blast(
    "MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR",
    database="swissprot", limit=10
)
print(f"Top hit: {blast_results.iloc[0]['Description']}, E-value: {blast_results.iloc[0]['e-value']}")
time.sleep(2)  # Rate-limit between BLAST queries

# BLAT — find genomic position (UCSC)
blat_results = gget.blat("ATCGATCGATCGATCGATCG", assembly="human")
print(f"Genomic location: chr{blat_results.iloc[0]['chromosome']}:{blat_results.iloc[0]['start']}")

import gget

# Multiple sequence alignment with Muscle5
aligned = gget.muscle("sequences.fasta", save=True)

# Fast local alignment with DIAMOND (local, no rate limit needed)
diamond_results = gget.diamond(
    "GGETISAWESQME",
    reference="reference.fasta",
    sensitivity="very-sensitive",
    threads=4
)
print(f"Alignments found: {len(diamond_results)}")

Module 3: Protein Structure (pdb, alphafold, elm)

Download PDB structures, predict structures with AlphaFold2, find linear motifs.

import gget

# Download PDB structure
pdb_data = gget.pdb("7S7U", save=True)

# Predict structure with AlphaFold2 (requires gget setup alphafold)
structure = gget.alphafold(
    "MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR",
    plot=True, show_sidechains=True
)
print("Structure prediction complete, PDB file saved")

import gget

# Find Eukaryotic Linear Motifs (requires gget setup elm)
ortholog_df, regex_df = gget.elm("LIAQSIGQASFV")
print(f"Ortholog motifs: {len(ortholog_df)}, Regex motifs: {len(regex_df)}")

Module 4: Expression & Correlation (archs4, cellxgene, bgee)

Gene expression, tissue expression, correlated genes, single-cell data.

import gget

# Tissue expression from ARCHS4
tissue_expr = gget.archs4("ACE2", which="tissue")
print(f"Expression across {len(tissue_expr)} tissues")

# Correlated genes from ARCHS4
correlated = gget.archs4("ACE2", which="correlation")
print(f"Top correlated gene: {correlated.iloc[0]['gene_symbol']}")

import gget

# Single-cell data from CELLxGENE (requires gget setup cellxgene)
adata = gget.cellxgene(
    gene=["ACE2", "TMPRSS2"],
    tissue="lung",
    cell_type="epithelial cell",
    census_version="2023-07-25"  # pin version for reproducibility
)
print(f"Cells: {adata.n_obs}, Genes: {adata.n_vars}")

# Orthologs and expression from Bgee
orthologs = gget.bgee("ENSG00000169194", type="orthologs")
print(f"Orthologs in {len(orthologs)} species")

Module 5: Disease & Drug Associations (opentargets, enrichr)

Disease associations, drug targets, enrichment analysis.

import gget

# Disease associations from OpenTargets
diseases = gget.opentargets("ENSG00000169194", resource="diseases", limit=10)
print(f"Associated diseases: {len(diseases)}")

# Drug associations
drugs = gget.opentargets("ENSG00000169194", resource="drugs", limit=10)
print(f"Associated drugs: {len(drugs)}")

# OpenTargets resources: diseases, drugs, tractability, pharmacogenetics,
#   expression, depmap, interactions

import gget

# Enrichment analysis via Enrichr
# Database shortcuts: 'pathway' (KEGG), 'transcription' (ChEA),
#   'ontology' (GO_BP), 'diseases_drugs' (GWAS), 'celltypes' (PanglaoDB)
enrichment = gget.enrichr(
    ["ACE2", "AGT", "AGTR1", "TMPRSS2", "DPP4"],
    database="ontology"
)
print(f"Enriched terms: {len(enrichment)}")
print(enrichment[["Term", "Adjusted P-value"]].head())

Module 6: Cancer Genomics (cbio, cosmic)

Cancer mutations, copy number alterations, and somatic mutation databases.

import gget

# Search cBioPortal studies
studies = gget.cbio_search(["breast", "lung"])
print(f"Studies found: {len(studies)}")

# Plot cancer genomics heatmap
gget.cbio_plot(
    ["msk_impact_2017"],
    ["AKT1", "ALK", "BRAF"],
    stratification="tissue",
    variation_type="mutation_occurrences"
)

import gget

# COSMIC: requires account + local database download
# First-time: gget.cosmic(searchterm="", download_cosmic=True,
#   email="[email protected]", password="xxx", cosmic_project="cancer")
cosmic_results = gget.cosmic("EGFR", cosmic_tsv_path="cosmic_data.tsv", limit=10)
print(f"COSMIC mutations: {len(cosmic_results)}")

Module 7: Mutation Generation & Utilities (mutate, setup)

Generate mutated sequences and manage module dependencies.

import gget
import pandas as pd

# Generate mutated sequences from mutation annotations
mutations_df = pd.DataFrame({
    "seq_ID": ["seq1", "seq1"],
    "mutation": ["c.4G>T", "c.10del"]
})
mutated = gget.mutate(["ATCGCTAAGCTGATCG"], mutations=mutations_df)
print(f"Generated {len(mutated)} mutated sequences")

Key Concepts

Module Overview

gget organizes 20+ modules by domain. Python interface uses gget.<module>():

Domain	Modules	Primary Database
Gene reference	ref, search, info, seq	Ensembl, UniProt, NCBI
Sequence alignment	blast, blat, muscle, diamond	NCBI BLAST, UCSC, local
Protein structure	pdb, alphafold, elm	RCSB PDB, AlphaFold2, ELM
Expression	archs4, cellxgene, bgee	ARCHS4, CZ CELLxGENE, Bgee
Disease/drugs	opentargets, enrichr	OpenTargets, Enrichr
Cancer	cbio, cosmic	cBioPortal, COSMIC
Utilities	mutate, setup, gpt	local / OpenAI

Output Formats

Context	Default Format	Alternatives
Python	DataFrame or dict	json=True for JSON; save=True to file
CLI	JSON	-csv for CSV; -o file to save
Sequences	FASTA (seq, mutate)	--
Structures	PDB file (pdb, alphafold)	JSON alignment error data
Single-cell	AnnData object (cellxgene)	meta_only=True for metadata only
Visualization	PNG (cbio plot)	show=True for interactive display

Enrichr Database Shortcuts

Shortcut	Full Database Name
'pathway'	KEGG_2021_Human
'transcription'	ChEA_2016
'ontology'	GO_Biological_Process_2021
'diseases_drugs'	GWAS_Catalog_2019
'celltypes'	PanglaoDB_Augmented_2021

Custom libraries: pass any Enrichr library name directly (e.g., "Jensen_TISSUES").

OpenTargets Resources

Resource	Description
diseases	Disease associations with evidence scores
drugs	Drug associations and clinical trial data
tractability	Target tractability assessment
pharmacogenetics	Pharmacogenetic variants
expression	Baseline tissue expression
depmap	DepMap gene-disease effects
interactions	Protein-protein interactions

Reproducibility

Pin database versions for consistent results across analyses:

import gget
# Pin Ensembl release
ref = gget.ref("homo_sapiens", release=112)

# Pin CELLxGENE Census version
adata = gget.cellxgene(gene=["ACE2"], census_version="2023-07-25")

# Always record gget version
print(f"gget version: {gget.__version__}")

Common Workflows

Workflow 1: Gene Discovery to Functional Analysis

Goal: Find genes of interest, get their sequences, and perform enrichment analysis.

import gget

# 1. Search for genes
results = gget.search(["GABA", "receptor"], species="homo_sapiens")
gene_ids = results["ensembl_id"].tolist()[:10]

# 2. Get detailed information
info = gget.info(gene_ids)
print(f"Retrieved info for {len(info)} genes")

# 3. Get protein sequences
sequences = gget.seq(gene_ids, translate=True)

# 4. Find correlated genes
correlated = gget.archs4(info.index[0], which="correlation")

# 5. Enrichment analysis on correlated genes
gene_list = correlated["gene_symbol"].tolist()[:50]
enrichment = gget.enrichr(gene_list, database="ontology")
print(f"Top enriched term: {enrichment.iloc[0]['Term']}")

Workflow 2: Target Validation for Drug Discovery

Goal: Investigate a gene's disease associations, druggability, and cancer mutations.

import gget

gene_id = "ENSG00000169194"  # ZBTB16

# 1. Disease associations
diseases = gget.opentargets(gene_id, resource="diseases", limit=20)

# 2. Drug associations
drugs = gget.opentargets(gene_id, resource="drugs")

# 3. Tractability assessment
tractability = gget.opentargets(gene_id, resource="tractability")

# 4. Protein interactions
interactions = gget.opentargets(gene_id, resource="interactions")
print(f"Diseases: {len(diseases)}, Drugs: {len(drugs)}, Interactions: {len(interactions)}")

# 5. Cancer genomics
gget.cbio_plot(["msk_impact_2017"], ["ZBTB16"], stratification="cancer_type")

Workflow 3: Comparative Genomics

Goal: Compare a gene across species using orthologs and sequence alignment.

import gget

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

# 2. Get sequences for human and mouse
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. Get human protein structure from PDB
pdb_structure = gget.pdb("7S7U")
print("Comparative analysis complete")

Key Parameters

Parameter	Module(s)	Default	Range / Options	Effect
species	search, archs4, cellxgene, enrichr	"homo_sapiens"	Any Ensembl species; shortcuts: 'human', 'mouse'	Target organism
limit	blast, opentargets, cosmic	50 / 100	1-1000	Maximum results returned
database	blast, enrichr	varies	blast: nt/nr/swissprot/pdbaa; enrichr: shortcuts or library names	Target database for query
which	ref, archs4	varies	ref: gtf,cdna,dna,cds,pep; archs4: correlation,tissue	Data type to retrieve
translate	seq	False	True/False	Return amino acid instead of nucleotide sequences
resource	opentargets	"diseases"	diseases, drugs, tractability, pharmacogenetics, expression, depmap, interactions	OpenTargets data type
release	ref, search	latest	Integer Ensembl release number	Pin database version for reproducibility
census_version	cellxgene	"stable"	"stable", "latest", date string	Pin CELLxGENE Census version
sensitivity	diamond, elm	"very-sensitive"	fast to ultra-sensitive	Alignment sensitivity vs speed
threads	diamond, elm	1	1-N	CPU threads for alignment
multimer_recycles	alphafold	3	3-20	Higher = more accurate multimer prediction

Best Practices

1. Pin database versions for reproducibility: Use release=112 for Ensembl and census_version="2023-07-25" for CELLxGENE to ensure consistent results across analyses.

2. Rate-limit batch queries: gget queries remote APIs. Add time.sleep(2) between BLAST/BLAT queries in loops. For gget.info(), limit to ~1000 IDs per call.

3. Keep gget updated: Databases change their structure biweekly. Run pip install --upgrade gget regularly to avoid breakage from schema changes.

4. Use Python interface for pipelines, CLI for exploration: Python functions return DataFrames suitable for chaining. CLI with -csv is better for quick one-off lookups.

5. Check PDB before running AlphaFold: gget.pdb() is instant; AlphaFold prediction takes minutes to hours. Always check if the structure already exists in PDB.

6. Use database shortcuts in enrichr: The shortcuts ('pathway', 'ontology', etc.) map to curated Enrichr libraries. For custom analyses, pass any Enrichr library name directly.

7. Cache cBioPortal data for repeated analyses: Use data_dir="./cache" parameter to avoid re-downloading large cancer genomics datasets.

Common Recipes

Recipe: Batch Gene Information Retrieval

When to use: Need information for many genes at once (up to ~1000 IDs per call).

import gget
import time

gene_ids = ["ENSG00000012048", "ENSG00000139618", "ENSG00000141510"]
info = gget.info(gene_ids)
info.to_csv("gene_info_batch.csv")
print(f"Saved info for {len(info)} genes")

# For >1000 genes, batch with rate limiting
all_ids = [f"ENSG{i:011d}" for i in range(2000)]
results = []
for i in range(0, len(all_ids), 500):
    batch = all_ids[i:i+500]
    results.append(gget.info(batch))
    time.sleep(1)

Recipe: Custom Enrichment with Background

When to use: Running enrichment against a custom background gene set.

import gget

# Use specific Enrichr library with background genes
enrichment = gget.enrichr(
    ["ACE2", "AGT", "AGTR1"],
    database="Jensen_TISSUES",
    background_list=["ACE2", "AGT", "AGTR1", "TP53", "BRCA1", "MYC"]
)
print(enrichment[["Term", "Adjusted P-value"]].head())

Recipe: AlphaFold Structure Prediction with Visualization

When to use: Predicting and visualizing protein structures with confidence coloring.

import gget

# Predict with visualization (PAE + 3D structure)
result = gget.alphafold(
    "MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR",
    plot=True,
    show_sidechains=True,
    relax=True  # AMBER relaxation for final structure
)
# Output: PDB file + predicted aligned error (PAE) JSON
# PAE heatmap auto-generated with plot=True

Recipe: Download Reference Genome for RNA-seq Pipeline

When to use: Setting up reference files for RNA-seq alignment pipelines.

# Download GTF and cDNA for human (specific release)
gget ref -w gtf -w cdna -d -r 112 homo_sapiens

# Download genome DNA
gget ref -w dna -d homo_sapiens

Troubleshooting

Problem	Cause	Solution
ModuleNotFoundError: gget	Package not installed	pip install gget in clean virtual environment
gget setup alphafold fails	Python version incompatibility	Use Python 3.8-3.10; check gget --version
Empty BLAST results	Sequence too short or no matches	Try longer sequence, different database, or megablast_off=True
cellxgene gene not found	Case-sensitive gene symbols	Use 'ACE2' for human, 'Ace2' for mouse (exact capitalization required)
gget info timeout	Too many IDs at once	Limit to ~1000 Ensembl IDs per call; batch with time.sleep()
Database structure changed	gget databases update biweekly	pip install --upgrade gget
COSMIC authentication error	Missing or expired credentials	Re-enter email/password; check COSMIC account status
AlphaFold out of memory	Protein too long for GPU memory	Use shorter sequences or split into domains
Different results on re-run	Database updated between runs	Pin versions: release=112 for Ensembl, census_version for CELLxGENE

Bundled Resources

2 reference files provide extended coverage of capabilities from the original 3 reference files and 3 script files:

1. references/module_parameters.md — Consolidates module_reference.md (468 lines). Covers: detailed parameter tables for all 15+ modules with types, defaults, and return value descriptions; CLI vs Python interface differences; setup requirements per module. Relocated inline: most-used module parameters (Core API code blocks), output format summary (Key Concepts table). Omitted: gget gpt module details — trivial OpenAI wrapper, not genomics-specific.

2. references/databases_workflows.md — Consolidates database_info.md (301 lines) and workflows.md (815 lines). Covers: complete database directory with update frequencies and citation info, extended workflow examples (building reference indices, disease-drug pipeline, multi-species comparative analysis), data consistency and reproducibility guidance. Relocated inline: core database overview (Key Concepts table), top 3 workflows (Common Workflows), reproducibility patterns (Key Concepts). Omitted: scripts/ content (3 files, 590 lines total) — thin wrappers around gget API calls for CLI automation; core patterns absorbed into Core API and Common Workflows.

Related Skills

• biopython — advanced BLAST parameters, batch sequence processing, GenBank record parsing
• bioservices — programmatic multi-database queries with built-in rate limiting (UniProt, KEGG, ChEMBL)
• anndata-data-structure — working with AnnData objects returned by gget.cellxgene()
• enrichr — deeper enrichment analysis with custom gene set libraries

References

• gget documentation — official docs and tutorials
• gget GitHub — source code, issues
• Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836