gget
Query genomic databases for gene info and sequences
Also available from: davila7
gget provides unified access to 20+ genomic databases through a simple CLI or Python interface. Query gene information, retrieve sequences, perform BLAST searches, and run enrichment analysis without managing multiple API connections.
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Test it
Using "gget". Search for BRCA1 gene in human
Expected outcome:
- Found: ENSG00000012048 (BRCA1)
- Description: BRCA1 DNA repair associated
- UniProt ID: P38398
- Chromosome: 17q21.31
- Biotype: protein coding
Using "gget". Get tissue expression for ACE2
Expected outcome:
- Kidney: median expression 245.3
- Testis: median expression 189.7
- Small intestine: median expression 156.2
- Bladder: median expression 98.4
Security Audit
SafeThis is legitimate bioinformatics software. All 614 static findings are false positives: markdown code fences were misidentified as Ruby shell execution, hardcoded URLs are public genomic databases (Ensembl, UniProt, NCBI), cryptographic patterns are data integrity checksums, and the critical heuristic is standard bioinformatics behavior (network queries to public APIs + local file operations for results).
Risk Factors
🌐 Network access (2)
Quality Score
What You Can Build
Quick gene lookups
Rapidly retrieve gene information, sequences, and annotations from Ensembl, UniProt, and NCBI during research exploration.
Enrichment analysis
Run gene ontology and pathway enrichment on gene lists to identify biological patterns and functional associations.
Sequence comparison
Perform BLAST searches and retrieve protein structures for sequence analysis and comparison tasks.
Try These Prompts
Use gget to search for the gene TP53 in human and return its Ensembl ID, description, and UniProt ID.
Retrieve the nucleotide and protein sequences for Ensembl gene ENSG00000139618 in FASTA format using gget.
Run a BLAST search using gget to find similar sequences to 'MSEQWKAVLFPLLLAAATSL...'. Use the nr database and return top 5 hits.
Run gget enrichr on the gene list ['TP53', 'BRCA1', 'MDM2', 'CDKN1A'] against KEGG Pathways database and show top 10 enriched terms.
Best Practices
- Keep gget updated to match changing database structures
- Use the -csv flag for CLI output to simplify parsing
- Limit large queries to avoid server timeout errors
Avoid
- Running thousands of queries without rate limiting
- Using unreleased database versions in reproducible workflows
- Skipping result validation when downstream analysis depends on data quality