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gget Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
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ZIP herunterladen Herunterladen... Mehr aus diesem Repository name gget description Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices. license BSD-2-Clause license metadata {"skill-author":"K-Dense Inc."} verified false lastVerifiedAt "2026-02-19T05:29:09.098Z" source builtin trust_score 100 provenance_sha 9628b16034d1ba65
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, expression data, and disease associations through a consistent interface. All 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. gget modules are tested automatically on a biweekly basis and updated to match new database structures when necessary.
Installation
Install gget in a clean virtual environment to avoid conflicts:
uv uv pip install gget
uv pip install --upgrade gget
import gget
Quick Start
Basic usage pattern for all modules:
gget <module> [arguments] [options]
gget.module(arguments, options)
Most modules return:
Command-line : JSON (default) or CSV with -csv flagPython : 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)
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 (gtf, cdna, dna, cds, cdrna, pep). Default: all-r/--release: Ensembl release number (default: latest)-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)
gget ref --list_species
gget ref homo_sapiens
gget ref -w gtf -d mouse
gget.ref("homo_sapiens" )
gget.ref("mus_musculus" , which="gtf" , download=True )
gget search - Gene Search Locate genes by name or description across species.
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 Returns : ensembl_id, gene_name, ensembl_description, ext_ref_description, biotype, URL
gget search -s human gaba gamma-aminobutyric
gget search -s mouse -ao and pax7 transcription
gget.search(["gaba" , "gamma-aminobutyric" ], species="homo_sapiens" )
gget info - Gene/Transcript Information Retrieve comprehensive gene and transcript metadata from Ensembl, UniProt, and NCBI.
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
gget info ENSG00000034713 ENSG00000104853 ENSG00000170296
gget info ENSG00000034713 -pdb
gget.info(["ENSG00000034713" , "ENSG00000104853" ], pdb=True )
gget seq - Sequence Retrieval Fetch nucleotide or amino acid sequences for genes and transcripts.
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
gget seq ENSG00000034713 ENSG00000104853
gget seq -t -iso ENSG00000034713
gget.seq(["ENSG00000034713" ], translate=True , isoforms=True )
2. Sequence Analysis & Alignment
gget blast - BLAST Searches BLAST nucleotide or amino acid sequences against standard databases.
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)
gget blast MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR
gget blast sequence.fasta -db swissprot -l 10
gget.blast("MKWMFK..." , database="swissprot" , limit=10 )
gget blat - BLAT Searches Locate genomic positions of sequences using UCSC BLAT.
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
gget blat ATCGATCGATCGATCG
gget blat -a mm39 ATCGATCGATCGATCG
gget.blat("ATCGATCGATCGATCG" , assembly="mouse" )
gget muscle - Multiple Sequence Alignment Align multiple nucleotide or amino acid sequences using Muscle5.
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)
gget muscle sequences.fasta -o aligned.afa
gget muscle large_dataset.fasta -s5
gget.muscle("sequences.fasta" , save=True )
gget diamond - Local Sequence Alignment Perform fast local protein or translated DNA alignment using DIAMOND.
Query: Sequences (string/list) or FASTA file path
--reference: Reference sequences (string/list) or FASTA file path (required)--sensitivity: fast, mid-sensitive, sensitive, more-sensitive, very-sensitive (default), ultra-sensitive--threads: CPU threads (default: 1)--diamond_db: Save database for reuse--translated: Enable nucleotide-to-amino acid alignment Returns : Identity percentage, sequence lengths, match positions, gap openings, E-values, bit scores
gget diamond GGETISAWESQME -ref reference.fasta --threads 4
gget diamond query.fasta -ref ref.fasta --diamond_db my_db.dmnd
gget.diamond("GGETISAWESQME" , reference="reference.fasta" , threads=4 )
3. Structural & Protein Analysis
gget pdb - Protein Structures Query RCSB Protein Data Bank for structure and metadata.
pdb_id: PDB identifier (e.g., '7S7U')-r/--resource: Data type (pdb, entry, pubmed, assembly, entity types)-i/--identifier: Assembly, entity, or chain ID Returns : PDB format (structures) or JSON (metadata)
gget pdb 7S7U -o 7S7U.pdb
gget pdb 7S7U -r entry
gget.pdb("7S7U" , save=True )
gget alphafold - Protein Structure Prediction Predict 3D protein structures using simplified AlphaFold2.
uv pip install openmm
gget setup alphafold
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 modelplot: 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
gget alphafold MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR
gget alphafold sequence1.fasta -mr 20 -r
gget.alphafold("MKWMFK..." , plot=True , show_sidechains=True )
gget.alphafold(["sequence1" , "sequence2" ], multimer_recycles=20 )
gget elm - Eukaryotic Linear Motifs Predict Eukaryotic Linear Motifs in protein sequences.
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)
ortholog_df : Linear motifs from orthologous proteinsregex_df : Motifs directly matched in input sequence
gget elm LIAQSIGQASFV -o results
gget elm --uniprot Q02410 -e
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.
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
Correlation mode : Gene symbols, Pearson correlation coefficientsTissue mode : Tissue identifiers, min/Q1/median/Q3/max expression values
gget archs4 ACE2
gget archs4 -w tissue ACE2
gget.archs4("ACE2" , which="tissue" )
gget cellxgene - Single-Cell RNA-seq Data Query CZ CELLxGENE Discover Census for single-cell data.
--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 onlyAdditional 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)
gget cellxgene --gene ACE2 ABCA1 --tissue lung --cell_type "mucus secreting cell" -o lung_data.h5ad
gget cellxgene --gene PAX7 --tissue muscle --meta_only -o metadata.csv
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.
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 genesplot: Python-only; generate graphical results
'pathway' → KEGG_2021_Human
'transcription' → ChEA_2016
'ontology' → GO_Biological_Process_2021
'diseases_drugs' → GWAS_Catalog_2019
'celltypes' → PanglaoDB_Augmented_2021
gget enrichr -db ontology ACE2 AGT AGTR1
gget enrichr -db pathway ACE2 AGT AGTR1 -ko ./kegg_images/
gget.enrichr(["ACE2" , "AGT" , "AGTR1" ], database="ontology" , plot=True )
gget bgee - Orthology & Expression Retrieve orthology and gene expression data from Bgee database.
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'
Orthologs mode : Matching genes across species with IDs, names, taxonomic infoExpression mode : Anatomical entities, confidence scores, expression status
gget bgee ENSG00000169194
gget bgee ENSG00000169194 -t expression
gget bgee ENSBTAG00000047356 ENSBTAG00000018317 -t expression
gget.bgee("ENSG00000169194" , type ="orthologs" )
gget opentargets - Disease & Drug Associations Retrieve disease and drug associations from OpenTargets.
Ensembl gene ID (required)
-r/--resource: diseases (default), drugs, tractability, pharmacogenetics, expression, depmap, interactions-l/--limit: Cap results countFilter arguments (vary by resource):
drugs: --filter_disease
pharmacogenetics: --filter_drug
expression/depmap: --filter_tissue, --filter_anat_sys, --filter_organ
interactions: --filter_protein_a, --filter_protein_b, --filter_gene_b
gget opentargets ENSG00000169194 -r diseases -l 5
gget opentargets ENSG00000169194 -r drugs -l 10
gget opentargets ENSG00000169194 -r expression --filter_tissue brain
gget.opentargets("ENSG00000169194" , resource="diseases" , limit=5 )
gget cbio - cBioPortal Cancer Genomics Plot cancer genomics heatmaps using cBioPortal data.
gget cbio search breast lung
plot - Generate heatmaps:
-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
gget cbio search esophag ovary
gget cbio plot -s msk_impact_2017 -g AKT1 ALK BRAF -st tissue -vt mutation_occurrences
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.
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)
-d/--download_cosmic: Activate download mode-gm/--gget_mutate: Create version for gget mutate-cp/--cosmic_project: Database type (cancer, 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
gget cosmic -d --email user@example.com --password xxx -cp cancer
gget cosmic EGFR -ctp cosmic_data.tsv -l 10
gget.cosmic("EGFR" , cosmic_tsv_path="cosmic_data.tsv" , limit=10 )
5. Additional Tools
gget mutate - Generate Mutated Sequences Generate mutated nucleotide sequences from mutation annotations.
sequences: FASTA file path or direct sequence input (string/list)-m/--mutations: CSV/TSV file 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-k/--k: Length of flanking sequences (default: 30 nucleotides) Returns : Mutated sequences in FASTA format
gget mutate ATCGCTAAGCT -m "c.4G>T"
gget mutate sequences.fasta -m mutations.csv -o mutated.fasta
import pandas as pd
mutations_df = pd.DataFrame({"seq_ID" : ["seq1" ], "mutation" : ["c.4G>T" ]})
gget.mutate(["ATCGCTAAGCT" ], mutations=mutations_df)
gget gpt - OpenAI Text Generation Generate natural language text using OpenAI's API.
Important : Free tier limited to 3 months after account creation. Set monthly billing limits.
prompt: Text input for generation (required)api_key: OpenAI authentication (required)Model configuration: temperature, top_p, max_tokens, frequency_penalty, presence_penalty
Default model: gpt-3.5-turbo (configurable)
gget gpt "Explain CRISPR" --api_key your_key_here
gget.gpt("Explain CRISPR" , api_key="your_key_here" )
gget setup - Install Dependencies Install/download third-party dependencies for specific modules.
module: Module name requiring dependency installation-o/--out: Output folder path (elm module only)
alphafold - Downloads ~4GB of model parameterscellxgene - Installs cellxgene-census (may not support latest Python)elm - Downloads local ELM databasegpt - Configures OpenAI integration
gget setup alphafold
gget setup elm -o /path/to/elm_data
Common Workflows
Workflow 1: Gene Discovery to Sequence Analysis Find and analyze genes of interest:
results = gget.search(["GABA" , "receptor" ], species="homo_sapiens" )
gene_ids = results["ensembl_id" ].tolist()
info = gget.info(gene_ids[:5 ])
sequences = gget.seq(gene_ids[:5 ], translate=True )
Workflow 2: Sequence Alignment and Structure Align sequences and predict structures:
alignment = gget.muscle("sequences.fasta" )
blast_results = gget.blast(my_sequence, database="swissprot" , limit=10 )
structure = gget.alphafold(my_sequence, plot=True )
ortholog_df, regex_df = gget.elm(my_sequence)
Workflow 3: Gene Expression and Enrichment Analyze expression patterns and functional enrichment:
tissue_expr = gget.archs4("ACE2" , which="tissue" )
correlated = gget.archs4("ACE2" , which="correlation" )
adata = gget.cellxgene(gene=["ACE2" ], tissue="lung" , cell_type="epithelial cell" )
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:
genes = gget.search(["breast cancer" ], species="homo_sapiens" )
diseases = gget.opentargets("ENSG00000169194" , resource="diseases" )
drugs = gget.opentargets("ENSG00000169194" , resource="drugs" )
study_ids = gget.cbio_search(["breast" ])
gget.cbio_plot(study_ids[:2 ], ["BRCA1" , "BRCA2" ], stratification="cancer_type" )
cosmic_results = gget.cosmic("BRCA1" , cosmic_tsv_path="cosmic.tsv" )
Workflow 5: Comparative Genomics Compare proteins across species:
orthologs = gget.bgee("ENSG00000169194" , type ="orthologs" )
human_seq = gget.seq("ENSG00000169194" , translate=True )
mouse_seq = gget.seq("ENSMUSG00000026091" , translate=True )
alignment = gget.muscle([human_seq, mouse_seq])
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):
gget ref --list_species
gget ref -w gtf -w cdna -d homo_sapiens
kallisto index -i transcriptome.idx transcriptome.fasta
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
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
Error Handling
Database structures change; update gget regularly: uv pip install --upgrade gget
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
Output Formats
Command-line
Default: JSON
CSV: Add -csv flag
FASTA: gget seq, gget mutate
PDB: gget pdb, gget alphafold
PNG: gget cbio plot
Python
Default: DataFrame or dictionary
JSON: Add json=True parameter
Save to file: Add save=True or specify out="filename"
AnnData: gget cellxgene
Resources This skill includes reference documentation for detailed module information:
references/
module_reference.md - Comprehensive parameter reference for all modulesdatabase_info.md - Information about queried databases and their update frequenciesworkflows.md - Extended workflow examples and use cases
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