gget – skill Dữ liệu & Khoa học cho agent AI

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 0.30.8 (reviewed 2026-09-30). For reproducible work, pin gget==0.30.8; 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 --python 3.13 .venv
source .venv/bin/activate
uv pip install "gget==0.30.8"

import gget

Quick Start

Inspect module-specific syntax before querying:

gget info --help
gget pdb --help

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 CLI syntax and inspect.signature(gget.<module>) for Python; CLI flags such as --download may have no Python equivalent.

Module Categories

gget exposes 24 modules in six categories. Parameters, CLI and Python examples, and

return shapes for every one are in

[references/module_catalog.md](references/module_catalog.md); fuller per-parameter

documentation is in [references/module_reference.md](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 (PDB/mmCIF structures and metadata), g2p (residue annotations and isoform maps), elm (linear motifs), alphafold (deprecated prediction wrapper) |

| 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 (deprecated text generation), setup (install module dependencies) |

Several modules need a one-time gget setup before first use (elm, cellxgene, cbio; legacy alphafold/gpt), 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](references/common_workflows.md), with longer versions in

[references/workflows.md](references/workflows.md).

Best Practices

Data Retrieval

• Use --limit where supported; it is often a local cap, not a complete pagination mechanism
• 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; gget still requires the reference input
• 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')
• Install optional dependencies for cellxgene, elm, and cbio with gget setup <module>
• For enrichment, record the full library name and release, not only a shortcut:

[the adapter](https://github.com/scverse/gget/blob/main/gget/gget_enrichr.py)

maps shortcuts to specific dated libraries, and non-human/mouse species need

full species-specific names. For human/mouse, supply the tested-gene universe

through background_list when appropriate; custom backgrounds are not

supported for the other species. Report mapped/unmapped query and background

counts and adjusted p-values, so identifier loss and selection bias are visible.

• Cache cBioPortal data with -dd to avoid repeated downloads
• Open Targets filters are local and applied after limit; expression fetches only the first page (at most 3000 rows). Other paginated resources use the server default page. Do not treat results as exhaustive.
• gget.info returns IDs in its index; preserve that index when saving CSV. ARCHS4 tissue labels use id, correlations use pearson_correlation; Enrichr uses path_name and adj_p_val.
• gget.search matches descriptions and synonyms; select an exact gene_name and reject ambiguity instead of taking the first result.
• Census meta_only=True returns cells, not datasets, and ignores the gene filter. Scope by dataset/cell metadata and pin a dated Census release.

Structures and residue annotations

• Prefer gget.pdb(..., resource="mmcif") for explicit structure format; the PDB default can fall back to mmCIF.
• Use gget.g2p for existing residue annotations and isoform/structure maps; specify accessions for exact isoform identity.
• alphafold and gpt are deprecated and no longer maintained upstream. Existing examples are legacy/illustrative, not tested prediction or generation workflows.

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 reviewed version for reproducible environments: uv pip install "gget==0.30.8"
• 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/mmCIF: gget pdb; legacy gget alphafold writes predicted structures
• PNG: gget cbio plot
• FASTA/CSV/JSONL folder: gget virus

Python

• Default: DataFrame or dictionary
• JSON: use json=True only on modules that support it (not g2p)
• Save to file: use only the module-specific save/out parameter; ref(download=True) and muscle(save=True) are invalid Python calls
• AnnData: gget cellxgene
• DataFrame/JSON: gget 8cube specificity, psi_block, expression

Bundled scripts and verification

• scripts/gene_analysis.py TP53: exact-symbol lookup, annotation/sequence export, optional human association queries; mouse tissue expression is routed correctly.
• scripts/enrichment_pipeline.py genes.txt --background tested_genes.txt: saves full dated library names and adjusted p-values. Use repeated --database for species-specific libraries. Failed queries are not negative evidence.
• scripts/batch_sequence_analysis.py proteins.fasta: small exploratory BLAST batches and local alignment; use local tools for large batches. The legacy --predict-structure flag only displays a placeholder.

The 0.30.8 adapters were source-checked, and tests use current response fields. Public reads verified ARCHS4, Open Targets, RCSB, Bgee, G2P, 8cube, Ensembl references/search. Ensembl info/seq failed on the released HTTP REST endpoint in this review; do not interpret that as a missing gene. Authenticated COSMIC, deprecated wrappers, and large downloads were not executed. Extended examples are explicitly illustrative. See [database contracts](references/database_info.md) for transport and completeness limitations.

Resources

This skill includes reference documentation for detailed module information:

references/

• module_reference.md - Selected parameters and Python/CLI differences
• database_info.md - Service contracts, pagination, and verification boundari

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