Authors
Punitha Chokalingam, W Joel, Swetha Varadarajan
Published in
Methods in molecular biology (Clifton, N.J.). Volume 3079. Pages 269-280.
Abstract
RNA-RNA interactions (RRIs) are central regulators of gene expression, governing processes such as microRNA-mediated silencing, lncRNA scaffolding, and viral RNA modulation. Traditional computational approaches, relying mainly on sequence or secondary structure, often fail to capture condition- and tissue-specific interactions. Multi-omics integration has emerged as a powerful strategy to overcome these limitations. By combining transcriptomics, RNA-protein interactions, and epitranscriptomic modifications, predictive models gain both accuracy and mechanistic insight. Transcriptomic data provide expression profiles across tissues and conditions, RNA-protein interaction maps (e.g., CLIP-seq from ENCODE) reveal protein-mediated modulation, and epitranscriptomic marks (m6A, m5C, pseudouridylation) influence RNA stability, localization, and interaction propensity. This chapter reviews key datasets, including TCGA for cancer transcriptomes, ENCODE for RNA-binding proteins, and epitranscriptome maps, and discusses strategies for multi-omics integration. Early fusion approaches combine features before modeling, while late fusion integrates predictions from modality-specific models. Applications in cancer and viral infection demonstrate the value of integrated RRI prediction. Finally, we address ongoing challenges such as data sparsity, context-dependency, and computational scalability, and highlight future directions for large-scale multi-omics RRI studies.
PMID:
42955686
Bibliographic data and abstract were imported from PubMed on 30 Oct 2026.
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