gget
CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST/batch processing, use biopython. For multi-database integration, use bioservices.
npx skills add majiayu000/claude-skill-registry --skill scientific-pkg-gget --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
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.
How it works
- Install in a clean environment and use either the command-line or Python API:
- CLI:
gget <module> [arguments] [options] - Python:
gget.module(arguments, options)
- CLI:
- For outputs, CLI returns JSON (default) or CSV with
-csv; Python returns a DataFrame or dictionary. - Modules include: ref for reference genomes, search for gene search, info for gene/transcript metadata, seq for sequences, blast/blat/muscle/diamond for various alignments, pdb for PDB structures, alphafold for structure prediction, elm for motif prediction, archs4/cellxgene/enrichr/bgee/opentargets/cbio for expression, enrichment, orthology, disease/drug data, and more.
- Some modules require setup steps (e.g., alphafold prerequisites, elm setup, cellxgene setup).
When to use it
Use gget when you need rapid access to multiple genomic databases and analysis functions from a single interface, especially for quick BLAST searches, gene information lookup, sequence retrieval, structural data, or disease associations across organisms.
What it can touch
- Command-line modules and Python functions across many databases and tools, including Ensembl, UniProt, PDB, AlphaFold data, ARCHS4, Enrichr, OpenTargets, COSMIC, OpenTargets, and more. (Referenced broadly across modules; exact touches depend on the specific module invoked.)
Caveats
- The databases queried are continuously updated, which can change structure; modules are tested biweekly and updated to match changes when necessary. Some actions require additional setup (e.g., OpenMM/OpenMM-based AlphaFold prerequisites) and may need environment-specific configurations.
# 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: ```bash # Using uv (recommended) uv pip install gget # Or using pip pip install --upgrade gget # In Python/Jupyter import gget ``` ## Quick Start Basic usage pattern for all modules: ```bash # Command-line gget <module> [arguments] [options] # Python gget.module(arguments, options) ``` Most modules return: - **Command-line**: JSON (default) or CSV with `-csv` flag - **Python**: DataFrame or dictionary Common flags across modules: - `-o/--out`: Save results to file - `-q/--quiet`: Suppress progress information - `-csv`:
- Overview
- Installation
- Quick Start
- Module Categories
- 1. Reference & Gene Information
- 2. Sequence Analysis & Alignment
- 3. Structural & Protein Analysis
- 4. Expression & Disease Data
- 5. Additional Tools
- Common Workflows
- Workflow 1: Gene Discovery to Sequence Analysis
- Workflow 2: Sequence Alignment and Structure
- Workflow 3: Gene Expression and Enrichment
- Workflow 4: Disease and Drug Analysis
Using uv (recommended) uv pip install gget Or using pip pip install --upgrade gget In Python/Jupyter import gget Command-line gget <module> [arguments] [options] Python List available species
What does the gget skill do?
CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST/batch processing, use biopython. For multi-database integration, use bioservices.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scientific-pkg-gget --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.