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.

De novo prediction of RNA-protein interactions with graph neural networks / Arora, Viplove; Sanguinetti, Guido. - In: RNA. - ISSN 1355-8382. - 28:11(2022), pp. 1469-1480. [10.1261/rna.079365.122]

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

Arora, Viplove;Sanguinetti, Guido
2022-01-01

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.
2022
RNA
28
11
1469
1480
https://www.biorxiv.org/content/10.1101/2021.09.28.462100v3.abstract
Arora, Viplove; Sanguinetti, Guido
File in questo prodotto:
File Dimensione Formato  
2021.09.28.462100v2.full.pdf

accesso aperto

Descrizione: preprint
Tipologia: Documento in Pre-print
Licenza: Non specificato
Dimensione 321.04 kB
Formato Adobe PDF
321.04 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/132250
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 2
  • ???jsp.display-item.citation.isi??? ND
social impact