Bio Interaction Databases

Interaction Databases

Query protein-protein interaction (PPI) and gene interaction databases to build and analyze biological networks.

STRING API

Query Interactions

import requests
import pandas as pd
from io import StringIO

STRING_API = 'https://version-12-0.string-db.org/api'

def get_string_interactions(genes, species=9606, score_threshold=400):
    '''Fetch PPI from STRING. species: 9606=human, 10090=mouse, 7227=fly'''
    url = f'{STRING_API}/tsv/network'
    params = {
        'identifiers': '%0d'.join(genes),
        'species': species,
        'required_score': score_threshold,
        'caller_identity': 'bioskills'
    }
    response = requests.get(url, params=params)
    return pd.read_csv(StringIO(response.text), sep='\t')

genes = ['TP53', 'BRCA1', 'MDM2', 'ATM', 'CHEK2', 'CDK2']
interactions = get_string_interactions(genes, score_threshold=700)
interactions[['preferredName_A', 'preferredName_B', 'score']].head()

STRING Confidence Score Tiers

Use 700+ for publication-quality networks. Use 400 for exploratory analysis where recall matters.

Resolve Gene Identifiers

def resolve_string_ids(genes, species=9606):
    '''Map gene names to STRING identifiers'''
    url = f'{STRING_API}/tsv/get_string_ids'
    params = {'identifiers': '%0d'.join(genes), 'species': species, 'caller_identity': 'bioskills'}
    response = requests.get(url, params=params)
    return pd.read_csv(StringIO(response.text), sep='\t')

resolved = resolve_string_ids(['TP53', 'BRCA1', 'MDM2'])
resolved[['queryItem', 'preferredName', 'stringId', 'annotation']]

STRING Functional Enrichment

def get_string_enrichment(genes, species=9606):
    '''Get GO/KEGG enrichment for a gene set via STRING'''
    url = f'{STRING_API}/tsv/enrichment'
    params = {'identifiers': '%0d'.join(genes), 'species': species, 'caller_identity': 'bioskills'}
    response = requests.get(url, params=params)
    df = pd.read_csv(StringIO(response.text), sep='\t')
    return df.sort_values('fdr')

enrichment = get_string_enrichment(genes)
enrichment[enrichment['category'] == 'Process'][['term', 'description', 'fdr', 'number_of_genes']].head(10)

STRING Network Image

def get_string_image(genes, species=9606):
    '''Download STRING network image as PNG'''
    url = f'{STRING_API}/image/network'
    params = {'identifiers': '%0d'.join(genes), 'species': species, 'caller_identity': 'bioskills'}
    response = requests.get(url, params=params)
    with open('string_network.png', 'wb') as f:
        f.write(response.content)

get_string_image(genes)

BioGRID REST API

BIOGRID_API = 'https://webservice.thebiogrid.org/interactions/'

def get_biogrid_interactions(gene, access_key, taxon=9606):
    '''Fetch interactions from BioGRID. Requires free API key from thebiogrid.org'''
    params = {
        'accesskey': access_key,
        'format': 'json',
        'searchNames': True,
        'geneList': gene,
        'taxId': taxon,
        'includeInteractors': True,
        'max': 1000
    }
    response = requests.get(BIOGRID_API, params=params)
    data = response.json()
    rows = [{'gene_a': v['OFFICIAL_SYMBOL_A'], 'gene_b': v['OFFICIAL_SYMBOL_B'],
             'system': v['EXPERIMENTAL_SYSTEM'], 'pubmed': v['PUBMED_ID'],
             'throughput': v['THROUGHPUT']} for v in data.values()]
    return pd.DataFrame(rows)

# biogrid_df = get_biogrid_interactions('TP53', access_key='YOUR_KEY')

Filter BioGRID by Evidence

def filter_biogrid(df, systems=None, low_throughput_only=False):
    '''Filter BioGRID by experimental system or throughput'''
    if low_throughput_only:
        df = df[df['throughput'] == 'Low Throughput']
    if systems:
        df = df[df['system'].isin(systems)]
    return df

# High-confidence physical interactions
# physical_systems = ['Affinity Capture-MS', 'Two-hybrid', 'Co-crystal Structure',
#                     'Reconstituted Complex', 'Affinity Capture-Western']
# filtered = filter_biogrid(biogrid_df, systems=physical_systems, low_throughput_only=True)

IntAct / PSICQUIC

def query_intact(gene, species='human'):
    '''Query IntAct via PSICQUIC REST API'''
    url = f'https://www.ebi.ac.uk/intact/ws/interactors/findInteractor/{gene}'
    params = {'species': species}
    response = requests.get(url, params=params)
    return response.json()

def query_psicquic(query):
    '''Query IntAct PSICQUIC for MITAB-formatted interactions'''
    url = 'https://www.ebi.ac.uk/intact/ws/interactors/list/resolve'
    response = requests.post(url, json={'query': query})
    return response.json()

OmniPath

def get_omnipath_interactions(genes):
    '''Query OmniPath for curated signaling interactions'''
    url = 'https://omnipathdb.org/interactions'
    params = {'genesymbols': 1, 'fields': 'sources,references', 'partners': ','.join(genes)}
    response = requests.get(url, params=params)
    df = pd.read_csv(StringIO(response.text), sep='\t')
    return df

omni = get_omnipath_interactions(['TP53', 'MDM2', 'BRCA1'])
omni[['source_genesymbol', 'target_genesymbol', 'is_directed', 'is_stimulation', 'is_inhibition']].head()

OmniPath Endpoint Reference

Convert to NetworkX

import networkx as nx

def string_to_networkx(interactions_df, score_col='score', min_score=700):
    '''Convert STRING interactions DataFrame to NetworkX graph'''
    filtered = interactions_df[interactions_df[score_col] >= min_score]
    G = nx.Graph()
    for _, row in filtered.iterrows():
        G.add_edge(row['preferredName_A'], row['preferredName_B'],
                   weight=row[score_col] / 1000, score=row[score_col])
    return G

G = string_to_networkx(interactions)
print(f'Network: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')

degree_df = pd.DataFrame(G.degree(), columns=['gene', 'degree']).sort_values('degree', ascending=False)
degree_df.head()

Multi-Database Aggregation

def aggregate_interactions(string_df, omnipath_df):
    '''Combine interactions from multiple databases into a unified network'''
    edges = set()
    for _, row in string_df.iterrows():
        a, b = sorted([row['preferredName_A'], row['preferredName_B']])
        edges.add((a, b, 'STRING', row['score']))
    for _, row in omnipath_df.iterrows():
        a, b = row['source_genesymbol'], row['target_genesymbol']
        edges.add((a, b, 'OmniPath', 1000))

    G = nx.Graph()
    for a, b, source, score in edges:
        if G.has_edge(a, b):
            G[a][b]['sources'].append(source)
            G[a][b]['max_score'] = max(G[a][b]['max_score'], score)
        else:
            G.add_edge(a, b, sources=[source], max_score=score)
    return G

Network Statistics

def network_summary(G):
    '''Compute basic network statistics'''
    stats = {
        'nodes': G.number_of_nodes(),
        'edges': G.number_of_edges(),
        'density': nx.density(G),
        'components': nx.number_connected_components(G),
        'avg_clustering': nx.average_clustering(G),
    }
    if nx.is_connected(G):
        stats['diameter'] = nx.diameter(G)
        stats['avg_path_length'] = nx.average_shortest_path_length(G)
    return stats

network_summary(G)

Related Skills

• database-access/uniprot-access - Protein annotations and cross-references
• pathway-analysis/go-enrichment - Functional enrichment of network genes
• gene-regulatory-networks/coexpression-networks - Co-expression network construction
• data-visualization/network-visualization - Visualize interaction networks