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De novo prediction of RNA-protein interactions with graph neural networks.

ABSTRACT: RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein interactions for select proteins; however, the time- and resource-intensive nature of these technologies call for the development of computational methods to complement their predictions. Here, we leverage recent, large-scale CLIP-seq experiments to construct a de novo predictor of RNA-protein interactions based on graph neural networks (GNN). We show that the GNN method allows us not only to predict missing links in an RNA-protein network, but to predict the entire complement of targets of previously unassayed proteins, and even to reconstruct the entire network of RNA-protein interactions in different conditions based on minimal information. Our results demonstrate the potential of modern machine learning methods to extract useful information on post-transcriptional regulation from large data sets.

SUBMITTER: Arora V

PROVIDER: S-EPMC9745830 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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De novo prediction of RNA-protein interactions with graph neural networks.

Arora Viplove V Sanguinetti Guido G

RNA (New York, N.Y.) 20220825 11

RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein interactions for select proteins; however, the time- and resource-intensive nature of these technologies call for the development of computational methods to complement their predictions. Here, we leverage recent, large-scale CLIP-seq experiments to ...[more]

PMID: 36008134

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Dataset Information

De novo prediction of RNA-protein interactions with graph neural networks.

Publications

De novo prediction of RNA-protein interactions with graph neural networks.

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