Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive
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Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive
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</> SKILL.md
gget.SKILL.md
---name: gget
description: "Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices."
license: BSD-2-Clause license
allowed-tools:
- Read
- Write
- Edit
- Bash
compatibility: Requires Python >=3.8 and gget 0.30.5-compatible APIs. Optional setup modules may install scientific dependencies that lag the newest Python releases; use Python 3.9 or 3.10 if `gget setup cellxgene` or `gget setup alphafold` fails.
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---# 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, viral sequences, expression data, disease associations, and mouse tissue/cell specificity metrics through a consistent interface. Most 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. Guidance here targets gget 0.30.5 (PyPI current as of 2026-06-07). For reproducible work, pin `gget==0.30.5`; for broken upstream database adapters, update gget after checking release notes.
Install gget in a clean virtual environment to avoid conflicts:
```bash
# Reproducible install targeting this skill
uv venv .venv
source .venv/bin/activate
uv pip install "gget==0.30.5"
# 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`: Return CSV format (command-line only)
Python argument names generally match long CLI options without leading dashes. For example, `--census_version` becomes `census_version=...`. Use `gget <module> --help` for the exact current signature.
## 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 as comma-separated CLI values or Python list (gtf, cdna, dna, cds, cdrna, pep). Default: all
- `-r/--release`: Ensembl release number (default: latest)
- `-od/--out_dir`: Directory for downloaded files
- `-l/--list_species`: List available vertebrate species
- `-liv/--list_iv_species`: List available invertebrate species
Avoid passing COSMIC credentials directly as CLI arguments on shared systems because command-line arguments can be exposed in shell history, process listings, and logs. Prefer the interactive prompt (`gget cosmic --download_cosmic ...`) or named environment variables read inside Python.
**Parameters**:
- `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)
**Database download flags**:
- `-d/--download_cosmic`: Activate download mode
- `-gm/--gget_mutate`: Create version for gget mutate
Download viral nucleotide sequences plus linked metadata from INSDC sources via NCBI Virus, with optional GenBank metadata enrichment. Results are saved to an output folder as FASTA, CSV, JSONL, and a command summary file.
**Parameters**:
- `virus`: Virus taxon name, taxon ID, accession, space-separated accessions, or path to a text file of accessions
- `-a/--is_accession`: Treat `virus` as accession input
- `--is_sars_cov2`, `--is_alphainfluenza`: Use optimized cached NCBI datasets paths for SARS-CoV-2 or Influenza A
- `--host`: Host organism name or NCBI taxonomy ID
- `-g/--genbank_metadata`: Fetch detailed GenBank metadata; auto-enabled by some annotation filters
- `--segment`, `--vaccine_strain`, `--annotated`, `--lab_passaged`, `--source_database`: Common viral metadata filters
- `--download_all_accessions`: Apply filters across all viral accessions
- `--baseline`, `--merge-results`: Resume or merge with prior metadata from partial/previous runs
**Important**: Do not use `--download_all_accessions` without restrictive filters; it can attempt to download the entire Viruses taxonomy and consume substantial time, bandwidth, and disk.
**Examples**:
```bash
# Complete Zika genomes from human hosts
gget virus "Zika virus" --nuc_completeness complete --host human --out zika_data
Generate mutated nucleotide sequences from mutation annotations.
**Current scope**: gget 0.29.1 simplified `mutate` to focus on applying standard mutation annotations to supplied nucleotide sequences and returning/saving mutated FASTA records. The broader variant-screening workflow moved upstream to the `kvar` project.
**Parameters**:
- `sequences`: FASTA file path or direct nucleotide sequence input (string/list)
- `-m/--mutations`: Mutation string/list, CSV/TSV path, 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 (default: same as mut_column)
- `-k/--k`: Length of flanking sequences (default: 30 nucleotides)
- `-o/--out`: Output FASTA path; without it Python returns a list of mutated sequences
**Returns**: Mutated sequences in FASTA format
**Examples**:
```bash
# Single mutation
gget mutate ATCGCTAAGCT -m "c.4G>T"
# Multiple sequences with one mutation per sequence
Generate natural language text using OpenAI's API.
**Setup Required**:
```bash
gget setup gpt
```
**Important**: Requires an OpenAI API key. Do not hard-code the key in notebooks, scripts, shell history, or committed files. Prefer a named environment variable such as `OPENAI_API_KEY`, and set monthly billing limits before use.
**Parameters**:
- `prompt`: Text input for generation (required)
- `api_key`: OpenAI authentication (required by the upstream API)
Prepare reference data for downstream analysis (e.g., kallisto|bustools):
```bash
# 1. List available species
gget ref --list_species
# 2. Download reference files
gget ref -w gtf -w cdna -d homo_sapiens
# 3. Build kallisto index
kallisto index -i transcriptome.idx transcriptome.fasta
# 4. Download genome for alignment
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
- For OpenTargets, inspect returned column names before writing filters; gget 0.30.5 follows the newer OpenTargets API schema
### 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
### Viral Data
- Use restrictive filters with `gget virus` before requesting broad viral datasets
- Keep `command_summary.txt` with downstream results for reproducibility and recovery after partial downloads
- Use `--baseline` and `--merge-results` to resume interrupted viral metadata/sequence downloads
### Error Handling
- Database structures change; when an adapter breaks, check upstream release notes and pin the newer fixed version explicitly
- Pin the known-good version for reproducible environments: `uv pip install "gget==0.30.5"`
- 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
- Keep COSMIC and OpenAI credentials in named environment variables or interactive prompts; do not write real credentials into examples, notebooks, or logs
## Output Formats
### Command-line
- Default: JSON
- CSV: Add `-csv` flag
- FASTA: gget seq, gget mutate
- PDB: gget pdb, gget alphafold
- PNG: gget cbio plot
- FASTA/CSV/JSONL folder: gget virus
### Python
- Default: DataFrame or dictionary
- JSON: Add `json=True` parameter
- Save to file: Add `save=True` or specify `out="filename"`
- Citation: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836
