| name | curated-bio-datasets |
| 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.). |
| category | biology |
| license | MIT license |
| metadata | {"skill-author":"InkVell Inc."} |
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
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:]
gene_sets[name] = genes
return gene_sets
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
1. COSMIC Cancer Datasets
Access the Catalogue of Somatic Mutations in Cancer.
import pandas as pd
def load_cosmic_census(census_path='cancer_gene_census.csv'):
"""Load and parse COSMIC Cancer Gene Census."""
df = pd.read_csv(census_path)
print(f"Total cancer genes: {len(df)}")
print(f"\nTier distribution:")
print(df['Tier'].value_counts())
print(f"\nRole in Cancer:")
roles = df['Role in Cancer'].str.split(', ').explode()
print(roles.value_counts())
print(f"\nTop mutation types:")
mut_types = df['Mutation Types'].str.split(', ').explode()
print(mut_types.value_counts().head(5))
return df
def filter_census_by_cancer(census_df, cancer_type):
"""Filter census for a specific cancer type."""
mask = census_df['Tumour Types(Somatic)'].str.contains(cancer_type, case=False, na=False)
filtered = census_df[mask]
print(f"Genes associated with '{cancer_type}': {len(filtered)}")
return filtered
def load_cosmic_mutations(mutations_path):
"""Load COSMIC somatic mutation data."""
df = pd.read_csv(mutations_path, sep='\t', low_memory=False)
print(f"Total mutations: {len(df)}")
print(f"Unique genes: {df['Gene name'].nunique()}")
print(f"Mutation types:\n{df['Mutation Description'].value_counts().head()}")
return df
def get_resistance_genes(census_df):
"""Extract genes with known drug resistance mutations."""
mask = census_df['Mutation Types'].str.contains('resistance', case=False, na=False) | \
census_df['Role in Cancer'].str.contains('resistance', case=False, na=False)
return census_df[mask][['Gene Symbol', 'Mutation Types', 'Role in Cancer']]
2. GTEx Tissue Expression
Access the Genotype-Tissue Expression project data.
import pandas as pd
import requests
GTEX_API = 'https://gtexportal.org/api/v2'
def get_gtex_gene_expression(gene_symbol, dataset_id='gtex_v8'):
"""Get median gene expression across tissues from GTEx API."""
url = f"{GTEX_API}/expression/medianGeneExpression"
params = {
'geneSymbol': gene_symbol,
'datasetId': dataset_id,
}
response = requests.get(url, params=params)
if response.status_code != 200:
print(f"Error: {response.status_code}")
return None
data = response.json()
if 'data' not in data:
print("No data returned")
return None
df = pd.DataFrame(data['data'])
df = df.sort_values('median', ascending=False)
print(f"Expression of {gene_symbol} across {len(df)} tissues:")
print(df[['tissueSiteDetailId', 'median']].head(10).to_string(index=False))
return df
def load_gtex_tpm(tpm_path):
"""Load GTEx TPM matrix (GCT format)."""
df = pd.read_csv(tpm_path, sep='\t', skiprows=2)
genes = df['Name']
descriptions = df['Description']
expression = df.iloc[:, 2:]
print(f"Genes: {len(genes)}")
print(f"Samples: {expression.shape[1]}")
return df
3. GWAS Catalog
Access SNP-trait association data.
import pandas as pd
def load_gwas_catalog(catalog_path):
"""Load GWAS Catalog associations."""
df = pd.read_csv(catalog_path, sep='\t', low_memory=False)
print(f"Total associations: {len(df)}")
print(f"Unique traits: {df['DISEASE/TRAIT'].nunique()}")
print(f"Unique SNPs: {df['SNPS'].nunique()}")
return df
def search_gwas_by_trait(catalog_df, trait_keyword, pvalue_threshold=5e-8):
"""Search GWAS catalog for a trait."""
mask = catalog_df['DISEASE/TRAIT'].str.contains(trait_keyword, case=False, na=False)
filtered = catalog_df[mask].copy()
filtered['P-VALUE'] = pd.to_numeric(filtered['P-VALUE'], errors='coerce')
filtered = filtered[filtered['P-VALUE'] < pvalue_threshold]
filtered = filtered.sort_values('P-VALUE')
print(f"Associations for '{trait_keyword}': {len(filtered)}")
if len(filtered) > 0:
print(filtered[['SNPS', 'MAPPED_GENE', 'P-VALUE', 'OR or BETA']].head(10))
return filtered
def search_gwas_by_gene(catalog_df, gene_symbol):
"""Search GWAS catalog for associations near a gene."""
mask = catalog_df['MAPPED_GENE'].str.contains(gene_symbol, case=False, na=False) | \
catalog_df['REPORTED GENE(S)'].str.contains(gene_symbol, case=False, na=False)
results = catalog_df[mask].sort_values('P-VALUE')
print(f"GWAS hits near {gene_symbol}: {len(results)}")
return results
4. GeneBass Exome Data
Access exome-wide association results.
import pandas as pd
def load_genebass_results(results_path, gene=None, pvalue_threshold=2.5e-6):
"""Load GeneBass exome-wide association results.
Args:
results_path: path to GeneBass results TSV
gene: optional gene filter
pvalue_threshold: exome-wide significance threshold
"""
df = pd.read_csv(results_path, sep='\t')
if gene:
df = df[df['gene_symbol'] == gene]
sig = df[df['pvalue'] < pvalue_threshold]
print(f"Total results: {len(df)}")
print(f"Significant (p < {pvalue_threshold}): {len(sig)}")
if len(sig) > 0:
print("\nTop hits:")
print(sig[['gene_symbol', 'annotation', 'pvalue', 'beta']].head(10))
return df
def compare_burden_tests(results_df, gene):
"""Compare burden test results across variant categories."""
gene_data = results_df[results_df['gene_symbol'] == gene]
for ann in ['pLoF', 'missense|LC', 'synonymous']:
subset = gene_data[gene_data['annotation'] == ann]
if len(subset) > 0:
row = subset.iloc[0]
print(f"{ann}: p={row['pvalue']:.2e}, beta={row.get('beta', 'N/A')}")
5. Protein Interaction Networks
Build and analyze networks from BioGRID data.
import pandas as pd
import networkx as nx
def load_biogrid(biogrid_path, organism=9606, experiment_types=None):
"""Load BioGRID protein-protein interaction data.
Args:
biogrid_path: path to BioGRID tab3 file
organism: NCBI taxonomy ID (9606 = human)
experiment_types: list of experiment types to include
"""
cols = ['BioGRID Interaction ID', 'Official Symbol Interactor A',
'Official Symbol Interactor B', 'Organism ID Interactor A',
'Organism ID Interactor B', 'Experimental System',
'Experimental System Type', 'Throughput']
df = pd.read_csv(biogrid_path, sep='\t', usecols=cols, low_memory=False)
df = df[(df['Organism ID Interactor A'] == organism) &
(df['Organism ID Interactor B'] == organism)]
if experiment_types:
df = df[df['Experimental System'].isin(experiment_types)]
print(f"Interactions: {len(df)}")
print(f"Unique proteins: {pd.concat([df['Official Symbol Interactor A'],
df['Official Symbol Interactor B']]).nunique()}")
print(f"\nExperiment types:\n{df['Experimental System'].value_counts().head()}")
return df
def build_ppi_network(biogrid_df, genes_of_interest=None):
"""Build NetworkX graph from BioGRID interactions."""
G = nx.Graph()
for _, row in biogrid_df.iterrows():
a = row['Official Symbol Interactor A']
b = row['Official Symbol Interactor B']
if a != b:
if G.has_edge(a, b):
G[a][b]['weight'] += 1
else:
G.add_edge(a, b, weight=1)
print(f"Network: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges")
if genes_of_interest:
neighbors = set()
for gene in genes_of_interest:
if gene in G:
neighbors.update(G.neighbors(gene))
subgraph_nodes = set(genes_of_interest) | neighbors
subG = G.subgraph(subgraph_nodes)
print(f"Subnetwork: {subG.number_of_nodes()} nodes, {subG.number_of_edges()} edges")
degrees = dict(subG.degree())
hubs = sorted(degrees.items(), key=lambda x: x[1], reverse=True)[:10]
print("Top hubs:")
for gene, deg in hubs:
print(f" {gene}: degree={deg}")
return subG
return G
6. MSigDB Gene Sets
Load and use MSigDB gene set collections.
import pandas as pd
def parse_gmt(gmt_path):
"""Parse GMT (Gene Matrix Transposed) file format."""
gene_sets = {}
with open(gmt_path) as f:
for line in f:
parts = line.strip().split('\t')
name = parts[0]
description = parts[1]
genes = [g for g in parts[2:] if g]
gene_sets[name] = {'description': description, 'genes': genes}
print(f"Loaded {len(gene_sets)} gene sets from {gmt_path}")
sizes = [len(gs['genes']) for gs in gene_sets.values()]
print(f"Gene set sizes: {min(sizes)}-{max(sizes)} (median: {sorted(sizes)[len(sizes)//2]})")
return gene_sets
def enrichment_with_msigdb(gene_list, collection='MSigDB_Hallmark_2020'):
"""Run enrichment analysis using MSigDB via gseapy."""
import gseapy as gp
enr = gp.enrichr(gene_list=gene_list, gene_sets=collection, outdir=None)
results = enr.results[enr.results['Adjusted P-value'] < 0.05]
print(f"Significant pathways (padj < 0.05): {len(results)}")
if len(results) > 0:
print(results[['Term', 'Adjusted P-value', 'Overlap', 'Combined Score']].head(10))
return enr
MSIGDB_COLLECTIONS = {
'H': 'MSigDB_Hallmark_2020',
'C2_CP': 'KEGG_2021_Human',
'C5_GO_BP': 'GO_Biological_Process_2021',
'C5_GO_MF': 'GO_Molecular_Function_2021',
'C5_GO_CC': 'GO_Cellular_Component_2021',
'C7': 'Immunologic_Signature',
}
7. DisGeNET & OMIM
Access disease-gene association databases.
import pandas as pd
import requests
def load_disgenet(disgenet_path):
"""Load DisGeNET disease-gene associations."""
df = pd.read_csv(disgenet_path, sep='\t')
print(f"Total associations: {len(df)}")
print(f"Unique genes: {df['geneSymbol'].nunique()}")
print(f"Unique diseases: {df['diseaseName'].nunique()}")
return df
def search_disgenet_by_gene(disgenet_df, gene_symbol, min_score=0.3):
"""Find diseases associated with a gene."""
results = disgenet_df[disgenet_df['geneSymbol'] == gene_symbol]
results = results[results['score'] >= min_score]
results = results.sort_values('score', ascending=False)
print(f"Diseases associated with {gene_symbol} (score >= {min_score}): {len(results)}")
if len(results) > 0:
print(results[['diseaseName', 'score', 'NofPmids']].head(10))
return results
def search_disgenet_by_disease(disgenet_df, disease_keyword, min_score=0.3):
"""Find genes associated with a disease."""
mask = disgenet_df['diseaseName'].str.contains(disease_keyword, case=False, na=False)
results = disgenet_df[mask]
results = results[results['score'] >= min_score]
results = results.sort_values('score', ascending=False)
print(f"Genes associated with '{disease_keyword}': {len(results)}")
if len(results) > 0:
print(results[['geneSymbol', 'diseaseName', 'score']].head(15))
return results
8. Gene Ontology
Work with GO terms and hierarchies.
import json
import requests
def parse_go_obo(obo_path):
"""Parse Gene Ontology OBO file."""
terms = {}
current = None
with open(obo_path) as f:
for line in f:
line = line.strip()
if line == '[Term]':
current = {}
elif line == '' and current is not None:
if 'id' in current:
terms[current['id']] = current
current = None
elif current is not None and ':' in line:
key, value = line.split(': ', 1)
if key in ('id', 'name', 'namespace', 'def'):
current[key] = value
elif key == 'is_a':
current.setdefault('parents', []).append(value.split(' ! ')[0])
elif key == 'is_obsolete' and value == 'true':
current = None
print(f"GO terms: {len(terms)}")
namespaces = {}
for t in terms.values():
ns = t.get('namespace', 'unknown')
namespaces[ns] = namespaces.get(ns, 0) + 1
print(f"Namespaces: {namespaces}")
return terms
def get_ancestors(terms, go_id, max_depth=10):
"""Get all ancestor terms of a GO term."""
ancestors = set()
queue = [go_id]
depth = 0
while queue and depth < max_depth:
next_queue = []
for term_id in queue:
if term_id in terms:
parents = terms[term_id].get('parents', [])
for parent in parents:
if parent not in ancestors:
ancestors.add(parent)
next_queue.append(parent)
queue = next_queue
depth += 1
return ancestors
def lookup_go_term(go_id):
"""Look up a GO term using the QuickGO API."""
url = f"https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/{go_id}"
response = requests.get(url, headers={'Accept': 'application/json'})
if response.status_code == 200:
data = response.json()
if 'results' in data and len(data['results']) > 0:
term = data['results'][0]
print(f"GO:{go_id}")
print(f" Name: {term.get('name')}")
print(f" Namespace: {term.get('aspect')}")
print(f" Definition: {term.get('definition', {}).get('text', 'N/A')[:200]}")
return term
return None
Typical Workflows
Workflow 1: Download and Filter COSMIC Cancer Gene Census
census = load_cosmic_census('cancer_gene_census.csv')
breast_genes = filter_census_by_cancer(census, 'breast')
print(f"\nBreast cancer genes: {list(breast_genes['Gene Symbol'].head(20))}")
Workflow 2: Build Protein Interaction Network from BioGRID
biogrid = load_biogrid('BIOGRID-ALL-LATEST.tab3.txt',
experiment_types=['Affinity Capture-MS', 'Two-hybrid'])
G = build_ppi_network(biogrid, genes_of_interest=['TP53', 'BRCA1', 'ATM'])
Workflow 3: Run Pathway Enrichment with MSigDB Gene Sets
gene_list = ['TP53', 'BRCA1', 'ATM', 'CHEK2', 'PTEN', 'RB1', 'CDKN2A']
enr = enrichment_with_msigdb(gene_list, collection='MSigDB_Hallmark_2020')
Best Practices
- COSMIC access — requires free registration; respect data license terms; use COSMIC CGC for curated cancer genes, full dataset for comprehensive analysis
- GTEx — use median TPM for tissue comparisons; raw counts for differential analysis; eQTL data for variant interpretation
- GWAS Catalog — apply genome-wide significance threshold (5e-8); check LD with lead SNP; use mapped genes, not just reported genes
- BioGRID — filter by experiment type for quality; "Affinity Capture-MS" and "Two-hybrid" are highest confidence; weight edges by evidence count
- MSigDB — Hallmark gene sets are most interpretable; use C2 (curated) for canonical pathways; C5 (GO) for biological process analysis
- DisGeNET — filter by score (>0.3 for moderate confidence); prioritize curated sources; cross-reference with OMIM for Mendelian diseases
- File sizes — COSMIC full mutation export is ~30GB; GTEx TPM matrix is ~2GB; download and process locally, don't reload repeatedly
Troubleshooting
Problem: COSMIC download requires authentication
Solution: Register for free account at cancer.sanger.ac.uk. Use SFTP or download from the web portal. Academic license is free.
Problem: GTEx API returns empty results
Solution: Check gene symbol is HUGO-approved. Try ENSG ID instead. GTEx v8 uses GRCh38 coordinates.
Problem: BioGRID file too large to load
Solution: Use organism filter during loading. Read in chunks with chunksize parameter. Pre-filter with command-line tools (grep).
Problem: gseapy enrichment returns no results
Solution: Ensure gene symbols match the gene set database (HUGO symbols). Check that gene list has >5 genes. Try different collections.
Resources
