scientific-pkg-gget

gget

Use this skill when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.

When to Use

• Finding Ensembl IDs, gene metadata, transcript details, or sequences.
• Running quick BLAST or BLAT lookups without building a full local pipeline.
• Fetching reference genome links and annotations from Ensembl.
• Querying protein structure, pathway, cancer, expression, or disease-association modules through a single interface.
• Creating a reproducible first-pass evidence log before moving to heavier tools such as Biopython, Snakemake, Nextflow, BLAST+, or database-specific clients.

Use a dedicated workflow instead of gget when the task requires regulated clinical interpretation, high-throughput production pipelines, or fine-grained control over database versions and local indexes.

Installation

Use a clean Python environment.

python -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip

If uv is available:

Before relying on an older environment, upgrade gget and re-check the module docs. The upstream databases queried by gget change over time.

Basic Patterns

CLI shape:

Python shape:

Common workflow:

1. Identify the species, assembly, gene ID type, and database needed.
2. Check the current module documentation for arguments.
3. Run a small query first.
4. Save output with an explicit filename and date.
5. Record module name, version, arguments, and database assumptions.

Common Modules

Use current upstream docs for exact arguments. These modules are common first choices:

• gget search: find Ensembl IDs from search terms.
• gget info: retrieve metadata for Ensembl, UniProt, or related IDs.
• gget seq: fetch nucleotide or amino-acid sequences.
• gget ref: retrieve reference genome download links.
• gget blast: run a quick BLAST query.
• gget blat: locate a sequence against supported genome assemblies.
• gget muscle: run multiple sequence alignment.
• gget diamond: run local sequence alignment against reference sequences.
• gget alphafold and gget pdb: inspect protein-structure references.
• gget enrichr, gget opentargets, gget archs4, gget bgee, gget cbio, and gget cosmic: explore enrichment, target, expression, cancer, and disease association data.

Do not assume every module supports every Python version or dependency set. Some optional scientific dependencies have narrower version support than the core package.

Quick Examples

Find genes:

Fetch gene metadata:

Fetch a sequence:

Run a small BLAST query:

Python example:

Reproducibility Log

For scientific outputs, include enough metadata to replay the query.

Also record:

• Python version and environment manager.
• Any optional dependency installed through gget setup.
• Database-specific identifiers returned by the query.
• Whether output is JSON, CSV, FASTA, or a DataFrame export.
• Any failures that were resolved by upgrading gget.

Review Checklist

• Did you upgrade or verify the installed gget version?
• Did you check the current upstream module docs before using arguments?
• Is the species or assembly explicit?
• Are identifiers preserved exactly, including Ensembl/UniProt prefixes?
• Is the result labeled as database output rather than clinical interpretation?
• Is the query reproducible from the saved command or Python snippet?
• Are optional dependencies installed in an isolated environment?

References

• gget documentation
• gget updates
• gget GitHub repository
• gget Bioinformatics paper