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Detalle de: MORE interpretable multi-omic regulatory networks to characterise phenotypes

Title MORE interpretable multi-omic regulatory networks to characterise phenotypes
Abstract

Studying phenotype-specific regulatory mechanisms is crucial to understanding the molecular basis of diseases and other complex traits. However, existing approaches for constructing multi-omic regulatory networks MO-RN are scarce, and most cannot integrate diverse omics modalities, incorporate prior biological knowledge, or infer phenotype-specific networks. To address these challenges, we present MORE (Multi-Omics REgulation), a novel R package for inferring multi-modal regulatory networks. MORE is available at https://github.com/BiostatOmics/MORE and supports any number and type of omics layers while optionally incorporating prior regulatory knowledge. Leveraging advanced regression-based models and variable selection techniques, MORE identifies significant regulatory relationships. This tool also provides useful functionalities for the biological interpretation of MO-RN: network visualisations, differential regulatory networks, and functional enrichment analyses of key network features. We evaluated MORE on simulated multi-omic datasets and benchmarked it against state-of-the-art tools. Our tool consistently outperformed other methods regarding accuracy in identifying significant regulators, model goodness-of-fit, and computational efficiency. We further applied MORE to a multi-omic ovarian cancer dataset to uncover tumour subtype-specific regulatory mechanisms associated with distinct survival outcomes. This analysis revealed differential regulatory patterns to understand the molecular basis of each subtype. By addressing the limitations of methods for multi-omic network inference, MORE represents a valuable resource for studying regulatory systems. Its ability to construct phenotype-specific regulatory networks with high accuracy and interpretability positions it as a useful resource for researchers seeking to unravel the complexities of molecular interactions and regulatory mechanisms across diverse biological contexts.

Apa Reference Aguerralde-Martin, M., Clemente-Císcar, M., Conesa, A. & Tarazona, S. (2025). MORE interpretable multi-omic regulatory networks to characterise phenotypes. Briefings in Bioinformatics, 26(3). 10.1093/bib/bbaf270
Doi 10.1093/bib/bbaf270
Publication Date 1 May 2025
Publication Type Article
Researcher Ana Conesa
Ontology Omics, Bioinformatics Software and Tools, Analysis packages (R, Python, etc.), Benchmarking, Gene regulatory networks, Biological interpretation, Quantitative genetics
Research Groups
Tools

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