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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.06903 |
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| _version_ | 1866915842073034752 |
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| author | De Silva, Iresha Bandu, Shantha Pathma Kidmose, Rune T. Maseras, Genona T. Bataillon, Thomas Ros, Xavier Bofill-De |
| author_facet | De Silva, Iresha Bandu, Shantha Pathma Kidmose, Rune T. Maseras, Genona T. Bataillon, Thomas Ros, Xavier Bofill-De |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06903 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HIDDENdb: Co-dependency database reveals a plethora of genetic and protein interactions De Silva, Iresha Bandu, Shantha Pathma Kidmose, Rune T. Maseras, Genona T. Bataillon, Thomas Ros, Xavier Bofill-De Molecular Networks 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/. |
| title | HIDDENdb: Co-dependency database reveals a plethora of genetic and protein interactions |
| topic | Molecular Networks |
| url | https://arxiv.org/abs/2603.06903 |