gget
gget is a free, open-source command-line tool and Python package that enables efficient querying of genomic databases. gget consists of a collection of separate but interoperable modules, each designed to facilitate one type of database querying in a single line of code. gget was developed by Laura Luebbert in the Pachter Lab.
gget is part of the scverse® project and is fiscally sponsored by NumFOCUS. If you like gget and want to support our mission, please consider making a tax-deductible donation.
If you use gget in a publication, please cite*:
Luebbert, L., & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836
Read the article here: https://doi.org/10.1093/bioinformatics/btac836
Installation
uv pip install gget
or
pip install --upgrade gget
Install from source:
git clone https://github.com/scverse/gget.git
cd gget
uv pip install .
For use in Jupyter Lab / Google Colab:
# Python
import gget
🔗 Manual
🪄 Quick start guide
Command line:
# Fetch all Homo sapiens reference and annotation FTPs from the latest Ensembl release
$ gget ref homo_sapiens
# Get Ensembl IDs of human genes with "ace2" or "angiotensin converting enzyme 2" in their name/description
$ gget search -s homo_sapiens 'ace2' 'angiotensin converting enzyme 2'
# Look up gene ENSG00000130234 (ACE2) and its transcript ENST00000252519
$ gget info ENSG00000130234 ENST00000252519
# Fetch the amino acid sequence of the canonical transcript of gene ENSG00000130234
$ gget seq --translate ENSG00000130234
# Quickly find the genomic location of (the start of) that amino acid sequence
$ gget blat MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS
# BLAST (the start of) that amino acid sequence
$ gget blast MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS
# Align multiple nucleotide or amino acid sequences against each other (also accepts path to FASTA file)
$ gget muscle MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS MSSSSWLLLSLVEVTAAQSTIEQQAKTFLDKFHEAEDLFYQSLLAS
# Align one or more amino acid sequences against a reference (containing one or more sequences) (local BLAST) (also accepts paths to FASTA files)
$ gget diamond MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS -ref MSSSSWLLLSLVEVTAAQSTIEQQAKTFLDKFHEAEDLFYQSLLAS
# Use Enrichr for an ontology analysis of a list of genes
$ gget enrichr -db ontology ACE2 AGT AGTR1 ACE AGTRAP AGTR2 ACE3P
# Get the human tissue expression of gene ACE2
$ gget archs4 -w tissue ACE2
# Get the protein structure (in PDB format) of ACE2 as stored in the Protein Data Bank (PDB ID returned by gget info)
$ gget pdb 1R42 -o 1R42.pdb
# Download virus genome datasets from NCBI Virus (e.g., Zika virus sequences)
$ gget virus "Zika virus" --host "Homo sapiens" --nuc_completeness complete
# Find Eukaryotic Linear Motifs (ELMs) in a protein sequence
$ gget setup elm # setup only needs to be run once
$ gget elm -o results MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS
# Fetch a scRNAseq count matrix (AnnData format) based on specified gene(s), tissue(s), and cell type(s) (default species: human)
$ gget setup cellxgene # setup only needs to be run once
$ gget cellxgene --gene ACE2 SLC5A1 --tissue lung --cell_type 'mucus secreting cell' -o example_adata.h5ad
Python (Jupyter Lab / Google Colab):
import gget
gget.ref("homo_sapiens")
gget.search(["ace2", "angiotensin converting enzyme 2"], "homo_sapiens")
gget.info(["ENSG00000130234", "ENST00000252519"])
gget.seq("ENSG00000130234", translate=True)
gget.blat("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS")
gget.blast("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS")
gget.muscle(["MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS", "MSSSSWLLLSLVEVTAAQSTIEQQAKTFLDKFHEAEDLFYQSLLAS"])
gget.diamond("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS", reference="MSSSSWLLLSLVEVTAAQSTIEQQAKTFLDKFHEAEDLFYQSLLAS")
gget.enrichr(["ACE2", "AGT", "AGTR1", "ACE", "AGTRAP", "AGTR2", "ACE3P"], database="ontology", plot=True)
gget.archs4("ACE2", which="tissue")
gget.pdb("1R42", save=True)
gget.virus("Zika virus", host="Homo sapiens", nuc_completeness="complete")
gget.setup("elm") # setup only needs to be run once
ortho_df, regex_df = gget.elm("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS")
gget.setup("cellxgene") # setup only needs to be run once
gget.cellxgene(gene = ["ACE2", "SLC5A1"], tissue = "lung", cell_type = "mucus secreting cell")
Call gget from R using reticulate:
system("pip install gget")
install.packages("reticulate")
library(reticulate)
gget <- import("gget")
gget$ref("homo_sapiens")
gget$search(list("ace2", "angiotensin converting enzyme 2"), "homo_sapiens")
gget$info(list("ENSG00000130234", "ENST00000252519"))
gget$seq("ENSG00000130234", translate=TRUE)
gget$blat("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS")
gget$blast("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS")
gget$muscle(list("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS", "MSSSSWLLLSLVEVTAAQSTIEQQAKTFLDKFHEAEDLFYQSLLAS"), out="out.afa")
gget$diamond("MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLAS", reference="MSSSSWLLLSLVEVTAAQSTIEQQAKTFLDKFHEAEDLFYQSLLAS")
gget$enrichr(list("ACE2", "AGT", "AGTR1", "ACE", "AGTRAP", "AGTR2", "ACE3P"), database="ontology")
gget$archs4("ACE2", which="tissue")
gget$pdb("1R42", save=TRUE)
gget$virus("Zika virus", host="Homo sapiens", nuc_completeness="complete")
More tutorials
2 maintainers
Release files for gget 0.30.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gget-0.30.8.tar.gz | 92.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gget-0.30.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 171.7 MB
Release files / gget-0.30.8.tar.gz
| Download URL | gget-0.30.8.tar.gz |
|---|---|
| Size | 92.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
231e36577bcb83e161396e4e134b565391aa36137e2424969a8a3694526b6cea
|
|
BLAKE2b-256 checksum How to use checksums |
fb930620187ea11deff77d9b806b4c60a116e8cd957df5e7b3e730b6c79dc834
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 28, 2026.
Transparency logRelease files / gget-0.30.8-py3-none-any.whl
| Download URL | gget-0.30.8-py3-none-any.whl |
|---|---|
| Size | 79.7 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
7a1b624389f65bc4a72bfa603448c34738f24f3e52b319393899df36ff9dc598
|
|
BLAKE2b-256 checksum How to use checksums |
c6d825b22e25bdd7745808c3a7e47515c0618b202b083a77b0ac4ba0fba15439
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 28, 2026.
Transparency log
