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Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions

View ORCID ProfileZichao Yan, William L. Hamilton, Mathieu Blanchette
doi: https://doi.org/10.1101/2020.02.11.931030
Zichao Yan
1School of Computer Science, McGill University
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  • ORCID record for Zichao Yan
William L. Hamilton
1School of Computer Science, McGill University
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  • For correspondence: wlh@cs.mcgill.ca blanchem@cs.mcgill.ca
Mathieu Blanchette
1School of Computer Science, McGill University
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  • For correspondence: wlh@cs.mcgill.ca blanchem@cs.mcgill.ca
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Abstract

Motivation RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor.

Results In this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches. We also introduce an approach to rectify particular type of sequence bias present in many CLIP-Seq data sets, and we show that correcting this bias is essential in order to learn meaningful predictors and properly evaluate their accuracy. Finally, we provide new approaches to interpret the trained models and extract simple, biologically-interpretable representations of the learned sequence and structural motifs.

Availability Source code can be accessed at https://www.github.com/HarveyYan/RNAonGraph.

Contact wlh{at}cs.mcgill.ca, blanchem{at}cs.mcgill.ca

Footnotes

  • zichao.yan{at}mail.mcgill.ca

  • https://www.github.com/HarveyYan/RNAonGraph

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 12, 2020.
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Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions
Zichao Yan, William L. Hamilton, Mathieu Blanchette
bioRxiv 2020.02.11.931030; doi: https://doi.org/10.1101/2020.02.11.931030
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Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions
Zichao Yan, William L. Hamilton, Mathieu Blanchette
bioRxiv 2020.02.11.931030; doi: https://doi.org/10.1101/2020.02.11.931030

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