# Curated Bio Datasets

Guide to accessing curated biological datasets for computational biology.

- Repository: https://github.com/synthetic-sciences/openscience (4321 stars)
- Category: Data and ML
- License: MIT license
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add synthetic-sciences/openscience --skill curated-bio-datasets -g -a claude-code -y`
Codex: `npx skills add synthetic-sciences/openscience --skill curated-bio-datasets -g -a codex -y`
Cursor: `npx skills add synthetic-sciences/openscience --skill curated-bio-datasets -g -a cursor -y`
Gemini CLI: `npx skills add synthetic-sciences/openscience --skill curated-bio-datasets -g -a gemini-cli -y`

## Description

Guide to accessing curated biological datasets for computational biology. COSMIC cancer data, GTEx expression, GWAS catalog, GeneBass exome variants, BioGRID interactions, MSigDB gene sets, DisGeNET disease-gene associations, and GO ontology. For specific database APIs use individual database skills (cosmic-database, gwas-database, etc.).

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Curated Bio-Datasets: Biological Datasets Guide

## Overview

Curated Bio-Datasets provides a comprehensive guide to accessing and working with major curated biological datasets. This skill covers COSMIC cancer genomics data, GTEx tissue expression data, GWAS Catalog SNP-trait associations, GeneBass exome-wide association results, BioGRID protein-protein interaction data, MSigDB gene set collections, DisGeNET disease-gene associations, and Gene Ontology resources. Each section includes download patterns, file formats, parsing code, and integration examples.

## When to Use This Skill

- Downloading and parsing COSMIC cancer gene census data
- Accessing GTEx tissue-level expression data (TPM matrices, eQTLs)
- Querying the GWAS Catalog for SNP-trait associations
- Working with GeneBass exome-wide burden test results
- Building protein-protein interaction networks from BioGRID
- Loading MSigDB gene sets for pathway enrichment analysis
- Querying DisGeNET for disease-gene associations
- Working with Gene Ontology terms and hierarchies

**Related Skills:** For specific database API access use dedicated skills: `cosmic-database`, `gwas-database`, `ensembl-database`, `kegg-database`, `reactome-database`.

## Installation

```bash
uv pip install pandas requests networkx gseapy numpy
```

## Quick Start

```python
import pandas as pd

# Load MSigDB gene sets (GMT format) for enrichment analysis
def parse_gmt(gmt_path):
    gene_sets = {}
    with open(gmt_path) as f:
        for line in f:
            parts = line.strip().split('\t')
            name = parts[0]
            genes = parts[2:]  # Skip description
            gene_sets[name] = genes
    return gene_sets

# Example: run enrichment with gseapy
import gseapy as gp
enr = gp.enrichr(gene_list=['TP53', 'BRCA1', 'ATM', 'CHEK2', 'PTEN'],
                 gene_sets='MSigDB_Hallmark_2020', outdir=None)
print(enr.results[['Term', 'Adjusted P-value', 'Overlap']].head())
```

## Core Capabilities

The rest: https://github.com/synthetic-sciences/openscience/blob/HEAD/backend/cli/skills/biology/curated-bio-datasets/SKILL.md