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

uv pip install pandas requests networkx gseapy numpy

Quick Start

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