Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins

Bioinformatics
Martin StražarTomaž Curk

Abstract

RNA binding proteins (RBPs) play important roles in post-transcriptional control of gene expression, including splicing, transport, polyadenylation and RNA stability. To model protein-RNA interactions by considering all available sources of information, it is necessary to integrate the rapidly growing RBP experimental data with the latest genome annotation, gene function, RNA sequence and structure. Such integration is possible by matrix factorization, where current approaches have an undesired tendency to identify only a small number of the strongest patterns with overlapping features. Because protein-RNA interactions are orchestrated by multiple factors, methods that identify discriminative patterns of varying strengths are needed. We have developed an integrative orthogonality-regularized nonnegative matrix factorization (iONMF) to integrate multiple data sources and discover non-overlapping, class-specific RNA binding patterns of varying strengths. The orthogonality constraint halves the effective size of the factor model and outperforms other NMF models in predicting RBP interaction sites on RNA. We have integrated the largest data compendium to date, which includes 31 CLIP experiments on 19 RBPs involved in splicing (such...Continue Reading

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Mar 30, 2018·PLoS Computational Biology·Ivan DotuJeffrey H Chuang
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Jul 13, 2021·Bioinformatics·Salma Sohrabi-Jahromi, Johannes Söding
Dec 21, 2021·Briefings in Bioinformatics·Junkang WeiYu Li

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