HIDDENdb: Co-dependency database reveals a plethora of genetic and protein interactions

TL;DR

HIDDENdb is a comprehensive, interactive database that integrates genetic and protein co-dependencies from large-scale datasets, revealing functional modules and structural insights into gene and protein interactions.

Contribution

It introduces HIDDENdb, a novel integrated resource combining diverse datasets and advanced analysis to map gene and protein co-dependencies across biological contexts.

Findings

Enrichment of co-dependencies with AlphaFold-predicted interfaces

Identification of gene modules with shared dependency patterns

Accessible web interface for exploring co-dependency networks

Abstract

Genetic interactions and protein co-dependencies shape cellular fitness, buffering capacity, and disease vulnerability. However, systematic integration of co-dependency relationships across heterogeneous datasets remains limited. Here, we present HIDDENdb (Harnessing Intelligent Data Discovery to Explore Gene Networks), a comprehensive database that captures genetic and protein co-dependencies inferred from large-scale perturbation screens, multi-omics datasets, and curated interaction repositories. HIDDENdb integrates genome-wide loss-of-function screens (CRISPR and shRNA) with other unbiased resources (BioGRID-ORCS and GWAS) to construct a map of co-dependency relationships across diverse biological contexts. Using robust statistical modeling and network inference approaches, we identify modules of genes and proteins exhibiting shared dependency patterns across cell lines. Notably, top-ranked gene-gene co-dependency pairs are enriched for high-confidence AlphaFold-predicted protein-protein interfaces, suggesting that a subset of inferred functional relationships may reflect underlying structural interactions. Importantly, the database enables users to explore co-dependency networks interactively. HIDDENdb is freely accessible through a web-based interface at https://bofillderoslab.shinyapps.io/hiddendb/.