bio-interaction-databases

name	bio-interaction-databases
description	Query protein-protein and gene interaction databases (STRING, BioGRID, IntAct, SIGNOR, Reactome, HuRI, HuMAP, OmniPath, ConsensusPathDB, DIP). Use when building PPI networks, choosing between physical vs functional vs genetic interactions, signed/directed vs undirected, high-throughput vs curated, picking confidence thresholds, aggregating across resources, or navigating license constraints. Encodes the database decision matrix, STRING v12 channel semantics, OmniPath as meta-database, SIGNOR for signed signaling, and per-resource rate limits.
tool_type	python
primary_tool	requests

Version Compatibility

Reference examples tested with: requests 2.31+, pandas 2.2+, networkx 3.2+; STRING v12.0, BioGRID 4.4+, IntAct (live), SIGNOR 3.0+, OmniPath (live)

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show requests pandas networkx
• API surface: confirm endpoint URLs match each resource's current docs

STRING URL is version-pinned (version-12-0 as of 2024); older URLs (version-11-5) were deprecated 2023. The Cytoscape/Cytoscape.js ecosystem uses different version semantics; check the docs for the targeted version.

Interaction Databases

"Get protein-protein interactions for these genes" -> The choice of database matters more than the choice of API. Different resources index different evidence (physical binding, functional association, genetic interaction, signed signaling), with different curation pipelines (manually curated vs high-throughput vs text-mined), different species coverage, and different licenses.

The decision matrix below is the postdoc-grade view: what question is being asked, and which resource answers it best?

• Python: requests.get() against REST endpoints; pandas for parsing; networkx for graphs
• R: STRINGdb, OmnipathR (mature Bioconductor clients)
• Web: STRING, BioGRID, IntAct, SIGNOR, OmniPath, ConsensusPathDB browsers

Required Setup

import requests
import pandas as pd
import networkx as nx
from io import StringIO

CALLER = 'bioskills-2026'  # STRING + OmniPath accept caller_identity for usage attribution

API key requirements:

• BioGRID: free key required (https://webservice.thebiogrid.org/)
• STRING, IntAct, SIGNOR, OmniPath, Reactome: no key

Decision matrix: which resource for which question?

Question	Best resource	Why
"Build a network around 10 genes"	STRING (medium confidence ~400)	Comprehensive; channels combinable; good viz integration
"Only physically interacting proteins"	IntAct or BioGRID physical	Curated physical interactions; PSI-MI standard
"Signed/directed signaling (phospho, ubiq, etc.)"	SIGNOR	Only major DB with mechanism types and direction
"Functional enrichment based on co-mentioned genes"	STRING functional (default)	Includes textmining channel
"Genetic interactions (synthetic lethality)"	BioGRID genetic	Largest curated genetic interaction set
"High-throughput Y2H interactome"	HuRI	Reference yeast-2-hybrid map of human
"Mass-spec-derived protein complexes"	HuMAP v2 or BioPlex	AP-MS complex maps
"Curated pathways with interactions"	Reactome	Pathway-organized; gold standard for signaling
"Meta-database aggregating 100+ sources"	OmniPath	The modern "one-stop"; pre-aggregated
"Cross-species or non-human"	STRING	Species coverage broadest
"Bacterial interactome"	STRING bacterial	Limited curated alternatives
"Phosphorylation site-specific"	PhosphoSitePlus (commercial license) or SIGNOR	PSP has best PTM coverage but requires license

OmniPath (Türei et al. 2021 Mol Sys Biol 17:e9923) deserves special mention: it aggregates >100 sources into a unified API with provenance tracking. For "give me all available interactions for X" workflows, OmniPath is the modern default.

STRING (v12, channels, confidence)

STRING (Szklarczyk et al. 2023 Nucleic Acids Res 51:D638) aggregates evidence into a combined confidence score. The seven evidence channels:

Channel	What it captures
experiments	Direct experimental evidence (BioGRID, IntAct, etc.)
database	Curated database (Reactome, KEGG)
textmining	Co-mention in PubMed
coexpression	Co-expression across conditions
neighborhood	Genomic neighborhood (prokaryotes mainly)
fusion	Gene fusion across species
cooccurrence	Phylogenetic profile co-occurrence

The default score is the combined-evidence score (0-1000). Confidence tiers:

Threshold	Tier	Use when
150	Low	Exploration; includes weak textmining
400	Medium	Default; balanced sensitivity/specificity
700	High	Publication-quality networks
900	Highest	Experimentally validated core only

Version pinning: STRING URL is https://version-12-0.string-db.org/api/... as of 2024. Older version-11-5 URLs were deprecated 2023 — code using them silently fails. Use the unversioned https://string-db.org/api/... to follow the current release, or pin to a specific version for reproducibility.

caller_identity: STRING requests all programmatic users pass caller_identity=<app-name> for usage attribution. The parameter helps STRING identify and contact heavy automated callers.

BioGRID (physical + genetic, curated + HT)

BioGRID (Oughtred et al. 2021 Protein Sci 30:187) covers physical and genetic interactions across the broadest organism set of any major resource. Requires a free API key (https://webservice.thebiogrid.org/).

Key concepts:

• EXPERIMENTAL_SYSTEM: e.g. "Two-hybrid", "Affinity Capture-MS", "Synthetic Growth Defect"
• THROUGHPUT: "Low Throughput" vs "High Throughput" — the most important quality flag
• EVIDENCE_TYPE: physical vs genetic

For high-confidence physical interactions, filter to physical systems + Low Throughput:

PHYSICAL_LT_SYSTEMS = {
    'Affinity Capture-MS', 'Affinity Capture-Western', 'Affinity Capture-RNA',
    'Co-fractionation', 'Co-purification', 'Reconstituted Complex',
    'Co-crystal Structure', 'Two-hybrid', 'Far Western', 'FRET', 'PCA',
}

IntAct (PSI-MI curated physical)

IntAct (Del Toro et al. 2022 Nucleic Acids Res 50:D648) is the IMEx consortium reference for curated physical interactions in PSI-MI standard format. MINT was folded in ~2014; queries to MINT URLs now redirect to IntAct.

Direct REST API has changed multiple times; the most stable access is via OmniPath (which wraps IntAct) or via the PSICQUIC web services.

SIGNOR (signed signaling)

SIGNOR (Lo Surdo et al. 2023 Nucleic Acids Res 51:D631) is the only major curated database with signed, directed, mechanism-typed interactions. Each edge has:

• direction: A→B
• effect: up-regulates, down-regulates, unknown
• mechanism: phosphorylation, dephosphorylation, ubiquitination, binding, etc.

Essential for any signaling pathway analysis or dynamic modeling. Coverage smaller than STRING/BioGRID but quality is high.

Reactome (curated pathways)

Reactome (Milacic et al. 2024 Nucleic Acids Res 52:D672) is gold-standard for human pathway curation with full interaction reactions. Species-specific (human is by far the most complete).

Reactome ContentService REST: https://reactome.org/ContentService/.

HuRI / HuMAP (human-specific interactomes)

• HuRI (Luck et al. 2020 Nature 580:402): yeast-2-hybrid interactome of human; ~53K binary interactions; biased toward binary high-confidence.
• HuMAP v2 (Drew et al. 2021 Mol Syst Biol 17:e10016): AP-MS-derived complex map; integrates >15,000 proteomic experiments.

Both available for download; HuRI also has a web portal (http://www.interactome-atlas.org/).

OmniPath (meta-database)

OmniPath (Türei et al. 2021 Mol Syst Biol 17:e9923) aggregates 100+ sources with provenance. Key endpoints:

Endpoint	Content
/interactions	Signaling interactions (directed); the most useful for cross-DB consensus
/enzsub	Enzyme-substrate (kinase-substrate, etc.)
/complexes	Protein complexes
/annotations	Functional annotations
/intercell	Intercellular communication

Each interaction includes sources (list of contributing databases) and references (PMIDs). For "give me everything anyone has said about A-B", OmniPath is the answer.

R users: OmnipathR (Bioconductor) is more ergonomic than raw HTTP.

License gotchas (critical for commercial use)

Resource	License	Notes
STRING	Free for all use	Permissive
BioGRID	Free, but registration required for bulk	Academic and commercial
IntAct	CC-BY (PSI-MI)	Permissive
SIGNOR	CC-BY-SA	Share-alike
Reactome	CC-BY	Permissive
HuRI / HuMAP	CC-BY	Permissive
OmniPath	Per-source (mostly permissive)	Check individual sources for commercial use
ConsensusPathDB	Academic only	Cannot use commercially
PhosphoSitePlus	Commercial license required	Best PTM coverage but costly
Pathway Commons	Per-source	Check sources

For commercial pipelines, stick to STRING + BioGRID + IntAct + SIGNOR + Reactome + HuRI/HuMAP + OmniPath with appropriate source attribution. Avoid ConsensusPathDB and PhosphoSitePlus without legal review.

Code patterns

STRING network with confidence threshold and channel inspection

Goal: Build a network of interactions among a gene list at a stated confidence; surface which channels contribute.

Approach: REST /network endpoint; parse channel-specific scores from response columns.

Reference (STRING v12.0):

import requests
import pandas as pd
from io import StringIO

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


def get_string_network(genes, species=9606, threshold=700):
    '''threshold: 150 (low), 400 (medium), 700 (high), 900 (highest).'''
    url = f'{STRING}/tsv/network'
    params = {
        'identifiers': '%0d'.join(genes),
        'species': species,
        'required_score': threshold,
        'caller_identity': 'bioskills-2026',
    }
    r = requests.get(url, params=params); r.raise_for_status()
    df = pd.read_csv(StringIO(r.text), sep='\t')
    # Columns: stringId_A, stringId_B, preferredName_A, preferredName_B, ncbiTaxonId,
    # score, nscore (neighborhood), fscore (fusion), pscore (cooccurrence),
    # ascore (coexpression), escore (experiments), dscore (database), tscore (textmining)
    return df


genes = ['TP53', 'BRCA1', 'MDM2', 'ATM', 'CHEK2', 'CDK2']
df = get_string_network(genes, threshold=700)
print(f'{len(df)} interactions at score >= 700')
print('Channel composition for first 5 edges:')
print(df[['preferredName_A', 'preferredName_B', 'score',
          'escore', 'dscore', , ]].head())

BioGRID physical interactions, low-throughput only

Reference (BioGRID 4.4+):

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

# Use the PHYSICAL_LT_SYSTEMS set defined above (the full curated set).
# Importing or re-defining is equivalent; using the same constant keeps the filter consistent.


def biogrid_lt_physical(gene, api_key, taxon=9606):
    params = {
        'accesskey': api_key,
        'format': 'json',
        'searchNames': True,
        'geneList': gene,
        'taxId': taxon,
        'includeInteractors': True,
        'max': 10000,
    }
    r = requests.get(BIOGRID, params=params); r.raise_for_status()
    data = r.json()
    rows = []
    for v in data.values():
        if v['THROUGHPUT'] == 'Low Throughput' and v['EXPERIMENTAL_SYSTEM'] in PHYSICAL_LT_SYSTEMS:
            rows.append({
                'gene_a': v['OFFICIAL_SYMBOL_A'],
                'gene_b': v['OFFICIAL_SYMBOL_B'],
                'system': v['EXPERIMENTAL_SYSTEM'],
                'pmid': v['PUBMED_ID'],
            })
    return pd.DataFrame(rows)

SIGNOR signed signaling

SIGNOR = 'https://signor.uniroma2.it/getData.php'


def signor_for_gene(gene_symbol):
    '''Return signed, directed signaling interactions involving the gene.'''
    params = {'organism': 'human', 'entity': gene_symbol}
    r = requests.get(SIGNOR, params=params); r.raise_for_status()
    rows = []
    for line in r.text.strip().split('\n')[1:]:
        cols = line.split('\t')
        if len(cols) >= 8:
            rows.append({
                'source': cols[0], 'target': cols[1], 'effect': cols[2],
                'mechanism': cols[3], 'pmid': cols[7],
            })
    return pd.DataFrame(rows)

OmniPath interactions (meta-database)

OMNI = 'https://omnipathdb.org'


def omnipath_interactions(genes, types='post_translational'):
    '''types: post_translational, transcriptional, mirna_target, lncrna_target.'''
    params = {
        'genesymbols': 1,
        'fields': 'sources,references,curation_effort,n_resources',
        'partners': ','.join(genes),
        'types': types,
        'license': 'academic',  # or 'commercial' for permissive-only sources
    }
    r = requests.get(f'{OMNI}/interactions', params=params); r.raise_for_status()
    df = pd.read_csv(StringIO(r.text), sep='\t')
    return df


df = omnipath_interactions(['TP53', 'MDM2', 'BRCA1'])
# Rich metadata: directionality, sign, sources, references, curation effort
print(df[['source_genesymbol', 'target_genesymbol', 'is_directed', 'is_stimulation',
          'is_inhibition', 'n_resources', 'n_references']].head())

Multi-resource aggregation

Goal: Build a union network of interactions from multiple resources; track provenance per edge.

Approach: Query each resource; normalize gene symbols; merge into a networkx Graph with sources attribute per edge.

Reference (requests 2.31+, networkx 3.2+):

import networkx as nx


def aggregate_networks(genes, biogrid_key=None):
    g = nx.Graph()

    # STRING (high confidence)
    string_df = get_string_network(genes, threshold=700)
    for _, row in string_df.iterrows():
        a, b = sorted([row['preferredName_A'], row['preferredName_B']])
        edge = g.get_edge_data(a, b, default={'sources': set(), 'max_score': 0})
        edge['sources'].add('STRING')
        edge['max_score'] = max(edge['max_score'], row['score'] / 1000.0)
        g.add_edge(a, b, **edge)

    # OmniPath
    omni_df = omnipath_interactions(genes)
    for _, row in omni_df.iterrows():
        a, b = sorted([row['source_genesymbol'], row['target_genesymbol']])
        edge = g.get_edge_data(a, b, default={'sources': set(), 'max_score': 0})
        edge['sources'].add('OmniPath')
        g.add_edge(a, b, **edge)

    # BioGRID (if key available)
    if biogrid_key:
        for gene in genes:
            biogrid_df = biogrid_lt_physical(gene, biogrid_key)
            for _, row in biogrid_df.iterrows():
                a, b = sorted([row['gene_a'], row[]])
                edge = g.get_edge_data(a, b, default={: (), : })
                edge[].add()
                g.add_edge(a, b, **edge)

     g

Network statistics

def summary(g):
    return {
        'nodes': g.number_of_nodes(),
        'edges': g.number_of_edges(),
        'density': nx.density(g),
        'components': nx.number_connected_components(g),
        'mean_degree': sum(dict(g.degree()).values()) / max(g.number_of_nodes(), 1),
    }


# Edges supported by multiple resources are higher-confidence
high_conf = [(a, b, d) for a, b, d in g.edges(data=True) if len(d['sources']) >= 2]
print(f'Multi-source edges: {len(high_conf)}/{g.number_of_edges()}')

Failure modes

Confusing functional with physical interactions

• Trigger: Treating STRING's combined score as "physically interact".
• Mechanism: STRING aggregates seven channels; textmining and coexpression are functional, not physical.
• Symptom: Network includes co-mentioned but non-interacting proteins.
• Fix: Filter STRING to experiments channel only (escore > threshold); or use BioGRID/IntAct for strictly physical.

Wrong confidence tier for the use case

• Trigger: Default STRING threshold 400 for a publication network.
• Mechanism: Includes weak textmining hits.
• Symptom: Network has many low-quality edges; downstream stats inflated.
• Fix: Use threshold 700 (high) for publication; 900 for experimentally-validated-core.

High-throughput interactions trusted as low-throughput

• Trigger: Treating Y2H or AP-MS bulk screens like curated low-throughput evidence.
• Mechanism: HT screens have higher false-positive rates.
• Symptom: Spurious interactions; network overfit to specific screens.
• Fix: Filter THROUGHPUT = 'Low Throughput' in BioGRID; or use IntAct with curated MI scores.

Symbol drift

• Trigger: Using MARCH1 for a query; renamed to MARCHF1 in 2020.
• Mechanism: HGNC renamed Excel-autocorrect-affected genes; some resources updated, some didn't.
• Symptom: Empty results; or matches to wrong gene.
• Fix: Resolve symbols to HGNC IDs first via UniProt or Ensembl; query by ID when possible.

STRING version drift

• Trigger: Code using version-11-5.string-db.org.
• Mechanism: Deprecated 2023; v12 is the current release.
• Symptom: 404 or silent return of stale data.
• Fix: Use version-12-0 (pinned for reproducibility) or string-db.org (live).

License surprise

• Trigger: Building a commercial product using ConsensusPathDB.
• Mechanism: ConsensusPathDB is academic-only.
• Symptom: License violation downstream.
• Fix: Audit each resource's license before commercial use; OmniPath has a license=commercial parameter that filters to commercially-permissive sources.

Asymmetric / directional confusion

• Trigger: Treating SIGNOR or OmniPath directional edges as undirected.
• Mechanism: Signaling edges carry direction and sign; collapsing loses information.
• Symptom: Wrong network topology in pathway analysis.
• Fix: Use DiGraph (nx.DiGraph) for directed resources; preserve effect and mechanism attributes.

Rate limit on bulk STRING queries

• Trigger: Looping STRING /network for 1000 gene sets.
• Mechanism: STRING asks for one request at a time per caller_identity.
• Symptom: Connection errors; throttling.
• Fix: Sleep 1-2 seconds between calls; respect caller_identity rules; for very large batches, use the bulk download.

Common errors

Error / symptom	Cause	Solution
STRING 404	Deprecated version-11-5 URL	Use version-12-0 or unversioned
BioGRID empty result	Missing API key or wrong taxId	Get key; use NCBI taxon ID
Symbol mismatch	HGNC renaming	Resolve via UniProt/Ensembl ID
HT interactions inflate network	No throughput filter	Filter THROUGHPUT = 'Low Throughput'
Functional vs physical confusion	Mixed STRING channels	Filter to escore for physical
Directional edges collapsed	Used Graph for directed source	Use DiGraph for SIGNOR/OmniPath
License violation in commercial pipeline	ConsensusPathDB or PhosphoSitePlus	Switch to permissive sources
OmniPath returns nothing	license=commercial filter too strict	Drop the filter for academic use

References

• Szklarczyk D, Kirsch R, Koutrouli M, et al. (2023) The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51:D638-D646.
• Oughtred R, Rust J, Chang C, et al. (2021) The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci 30:187-200.
• Del Toro N, Shrivastava A, Ragueneau E, et al. (2022) The IntAct database: efficient access to fine-grained molecular interaction data. Nucleic Acids Res 50:D648-D653.
• Lo Surdo P, Iannuccelli M, Contino S, et al. (2023) SIGNOR 3.0, the SIGnaling network open resource 3.0: 2022 update. Nucleic Acids Res 51:D631-D637.
• Milacic M, Beavers D, Conley P, et al. (2024) The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res 52:D672-D678.
• Luck K, Kim DK, Lambourne L, et al. (2020) A reference map of the human binary protein interactome. Nature 580:402-408.
• Drew K, Wallingford JB, Marcotte EM. (2021) hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies. Mol Syst Biol 17:e10016.
• Türei D, Valdeolivas A, Gül L, et al. (2021) Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol Syst Biol 17:e9923.

Related Skills

• uniprot-access - Resolve symbols to UniProt accessions
• ensembl-rest - Cross-reference Ensembl IDs in network nodes
• gene-regulatory-networks/coexpression-networks - Co-expression as a complement to PPI
• pathway-analysis/go-enrichment - Functional enrichment of network genes
• pathway-analysis/reactome-pathways - Use Reactome pathways alongside Reactome interactions
• data-visualization/network-visualization - Visualize the resulting networks