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="user@example.com", password="xxx", cosmic_project="cancer")
cosmic_results = gget.cosmic("EGFR", cosmic_tsv_path="cosmic_data.tsv", limit=10)
print