Gget Genomic Databases

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

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)}")

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}")

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)}")

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)}")

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")

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())

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(f"COSMIC mutations: {len(cosmic_results)}")

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")

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__}")

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']}")

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")

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")