bio-interaction-databases

name	bio-interaction-databases
description	Query protein-protein and gene interaction databases including STRING, BioGRID, and IntAct via their REST APIs and Python clients. Retrieve interaction networks, confidence scores, and functional enrichment. Use when building protein interaction networks, contextualizing gene lists with known interactions, or retrieving pathway-level interaction data.
tool_type	python
primary_tool	STRINGdb

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

Threshold	Tier	Guidance
150	Low	Includes transferred and text-mined; noisy
400	Medium	Default. Balanced sensitivity/specificity
700	High	Recommended for network analysis. Good confidence
900	Highest	Experimentally validated core. Very stringent

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

Endpoint	Content
/interactions	Signaling interactions (directed)
/enzsub	Enzyme-substrate relationships
/complexes	Protein complexes
/annotations	Functional annotations
/intercell	Intercellular communication

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