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Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions
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 im...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355240/ https://www.ncbi.nlm.nih.gov/pubmed/32657407 http://dx.doi.org/10.1093/bioinformatics/btaa456 |
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author | Yan, Zichao Hamilton, William L Blanchette, Mathieu |
author_facet | Yan, Zichao Hamilton, William L Blanchette, Mathieu |
author_sort | Yan, Zichao |
collection | PubMed |
description | 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 an important type of sequence bias caused by the RNase T1 enzyme used in many CLIP-Seq experiments, 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 AND IMPLEMENTATION: Source code can be accessed at https://www.github.com/HarveyYan/RNAonGraph. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-7355240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-73552402020-07-16 Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions Yan, Zichao Hamilton, William L Blanchette, Mathieu Bioinformatics Macromolecular Sequence, Structure, and Function 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 an important type of sequence bias caused by the RNase T1 enzyme used in many CLIP-Seq experiments, 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 AND IMPLEMENTATION: Source code can be accessed at https://www.github.com/HarveyYan/RNAonGraph. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-07 2020-07-13 /pmc/articles/PMC7355240/ /pubmed/32657407 http://dx.doi.org/10.1093/bioinformatics/btaa456 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Macromolecular Sequence, Structure, and Function Yan, Zichao Hamilton, William L Blanchette, Mathieu Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions |
title | Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions |
title_full | Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions |
title_fullStr | Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions |
title_full_unstemmed | Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions |
title_short | Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions |
title_sort | graph neural representational learning of rna secondary structures for predicting rna-protein interactions |
topic | Macromolecular Sequence, Structure, and Function |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355240/ https://www.ncbi.nlm.nih.gov/pubmed/32657407 http://dx.doi.org/10.1093/bioinformatics/btaa456 |
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