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scientific-pkg-gget

Use the gget command line or Python package to look up genes, retrieve sequences, compare similar sequences, and explore biological database results. Save outputs and query details to support reproducible first-pass bioinformatics research.

OriginalShow the original description

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs. Use when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.

AI rating90/ 100See how to install

Good for

  • Finding gene identifiers and transcript information
  • Retrieving nucleotide or amino-acid sequences
  • Running quick BLAST or BLAT sequence searches
  • Exploring gene enrichment and disease associations
  • Recording queries and results for reproducible research

Overview

What it does

This skill uses the gget CLI or Python package to query databases for gene and sequence information. It retrieves Ensembl IDs, gene information, transcript details, and nucleotide or amino-acid sequences. It also covers similarity searches with BLAST or BLAT, analysis of functions overrepresented in gene sets, and searches for protein structure, expression, cancer, and disease associations.

When it helps

Use it to investigate a small number of genes or sequences before building a full analysis pipeline. Alongside the results, it records the query date, module, version, species, reference genome assembly, and arguments so you can check the research conditions later.

Example requests

  • “Find the Ensembl ID and gene information for human BRCA1 and save the results.”
  • “Search this amino-acid sequence with BLAST and record the query conditions.”
  • “Investigate enriched functions and disease associations for this gene set.”

Notes

A Python environment and gget are required. Some modules need additional dependencies or specific Python versions. Databases change, so verify versions and arguments before use. Results are not clinical interpretations. Regulated clinical decisions, high-volume production processing, and strict database version control require dedicated procedures.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodQuick gene and sequence lookup is the stated use, with roles explained for modules such as search and seq.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodThe source provides a five-step workflow, CLI and Python examples, output filenames, and a reproducibility log table.
SafetyAre sending data, deleting files, and running commands disclosed?
BPartly lackinggget blast submits sequences for external searches, but handling of submitted sequences and pre-submission confirmation are not explained.
Easy setupDoes it need few tools or API keys, and are they documented?
AGoodVirtual environment setup and pip or uv installation steps are provided, with optional dependencies and Python version constraints noted.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 19, 2026. Treat it as a guide and read the original before installing.

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SKILL.md (original)

Review the instructions the agent will receive before you install.

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 install --upgrade gget
gget --help

If uv is available:

uv venv
. .venv/bin/activate
uv pip install gget

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:

gget <module> [arguments] [options]

Python shape:

import gget

result = gget.search(["BRCA1"], species="human")
print(result)

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:

gget search -s human brca1 dna repair -o brca1-search.json

Fetch gene metadata:

gget info ENSG00000012048 -o brca1-info.json

Fetch a sequence:

gget seq ENSG00000012048 -o brca1-seq.fa

Run a small BLAST query:

gget blast "MEEPQSDPSVEPPLSQETFSDLWKLLPEN" -l 10 -o blast-results.json

Python example:

import gget

genes = gget.search(["BRCA1", "DNA repair"], species="human")
info = gget.info(["ENSG00000012048"])
sequence = gget.seq("ENSG00000012048")

Reproducibility Log

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

| Date | gget version | Module | Query | Species/assembly | Output | Notes |
| --- | --- | --- | --- | --- | --- | --- |
| 2026-05-11 | `gget --version` | search | `BRCA1 DNA repair` | human | `brca1-search.json` | Docs checked before run |

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

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