gget

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":"1.5","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. Installation Install gget in a clean virtual environment to avoid conflicts: # 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: # 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 gget exposes 23 modules in six categories. Parameters, CLI and Python examples, and return shapes for every one are in references/module_catalog.md ; fuller per-parameter documentation is in references/module_reference.md . Category Modules 1. Reference & gene information ref (Ensembl reference downloads), search (gene search), info (gene/transcript detail), seq (nucleotide and protein sequences) 2. Sequence analysis & alignment blast , blat , muscle (multiple alignment), diamond (local alignment) 3. Structural & protein analysis pdb (structures and metadata), alphafold (structure prediction), elm (linear motifs) 4. Expression & disease data archs4 (correlation, tissue expression), cellxgene (single-cell), enrichr (enrichment), bgee (orthology and expression), opentargets (disease and drug), cbio (cancer genomics), cosmic (mutations) 5. Viral & mouse specificity virus (viral sequences), 8cube (mouse specificity and expression) 6. Additional tools mutate (mutated sequences), gpt (text generation), setup (install module dependencies) Several modules need a one-time gget setup before first use ( alphafold , elm , cellxgene ), and cosmic prompts for COSMIC credentials to download its database. Common Workflows Worked multi-module pipelines — gene characterization, structural comparison, expression and enrichment analysis, disease and drug association, orthology comparison, and reference-file preparation for kallisto or alignment — are in references/common_workflows.md , with longer versions in references/workflows.md . 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" AnnData: gget cellxgene DataFrame/JSON: gget 8cube specificity, psi_block, expression Resources This skill includes reference documentation for detailed module information: references/ module_reference.md - Comprehensive parameter reference for all modules database_info.md - Information about queried databases and their update frequencies workflows.md - Extended workflow examples and use cases For additional help: Official documentation: https://pachterlab.github.io/gget/ GitHub issues: https://github.com/pachterlab/gget/issues Citation: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836 Citing Scientific Agent Skills This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so: Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065 Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the latest arXiv version, so never append a version suffix such as v1 . When network access is available, fetch https://arxiv.org/abs/2609.00065 (or http://export.arxiv.org/api/query?id_list=2609.00065 ) before writing the reference and take the author list, year, and version from that record. If the record lists a journal reference or publisher DOI, cite the published version instead.