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PrismNet: predicting protein–RNA interaction using in vivo RNA structural information

Fundamental to post-transcriptional regulation, the in vivo binding of RNA binding proteins (RBPs) on their RNA targets heavily depends on RNA structures. To date, most methods for RBP–RNA interaction prediction are based on RNA structures predicted from sequences, which do not consider the various...

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Detalles Bibliográficos
Autores principales: Xu, Yiran, Zhu, Jianghui, Huang, Wenze, Xu, Kui, Yang, Rui, Zhang, Qiangfeng Cliff, Sun, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320048/
https://www.ncbi.nlm.nih.gov/pubmed/37140045
http://dx.doi.org/10.1093/nar/gkad353
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author Xu, Yiran
Zhu, Jianghui
Huang, Wenze
Xu, Kui
Yang, Rui
Zhang, Qiangfeng Cliff
Sun, Lei
author_facet Xu, Yiran
Zhu, Jianghui
Huang, Wenze
Xu, Kui
Yang, Rui
Zhang, Qiangfeng Cliff
Sun, Lei
author_sort Xu, Yiran
collection PubMed
description Fundamental to post-transcriptional regulation, the in vivo binding of RNA binding proteins (RBPs) on their RNA targets heavily depends on RNA structures. To date, most methods for RBP–RNA interaction prediction are based on RNA structures predicted from sequences, which do not consider the various intracellular environments and thus cannot predict cell type-specific RBP–RNA interactions. Here, we present a web server PrismNet that uses a deep learning tool to integrate in vivo RNA secondary structures measured by icSHAPE experiments with RBP binding site information from UV cross-linking and immunoprecipitation in the same cell lines to predict cell type-specific RBP–RNA interactions. Taking an RBP and an RNA region with sequential and structural information as input (‘Sequence & Structure’ mode), PrismNet outputs the binding probability of the RBP and this RNA region, together with a saliency map and a sequence–structure integrative motif. The web server is freely available at http://prismnetweb.zhanglab.net.
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spelling pubmed-103200482023-07-06 PrismNet: predicting protein–RNA interaction using in vivo RNA structural information Xu, Yiran Zhu, Jianghui Huang, Wenze Xu, Kui Yang, Rui Zhang, Qiangfeng Cliff Sun, Lei Nucleic Acids Res Web Server Issue Fundamental to post-transcriptional regulation, the in vivo binding of RNA binding proteins (RBPs) on their RNA targets heavily depends on RNA structures. To date, most methods for RBP–RNA interaction prediction are based on RNA structures predicted from sequences, which do not consider the various intracellular environments and thus cannot predict cell type-specific RBP–RNA interactions. Here, we present a web server PrismNet that uses a deep learning tool to integrate in vivo RNA secondary structures measured by icSHAPE experiments with RBP binding site information from UV cross-linking and immunoprecipitation in the same cell lines to predict cell type-specific RBP–RNA interactions. Taking an RBP and an RNA region with sequential and structural information as input (‘Sequence & Structure’ mode), PrismNet outputs the binding probability of the RBP and this RNA region, together with a saliency map and a sequence–structure integrative motif. The web server is freely available at http://prismnetweb.zhanglab.net. Oxford University Press 2023-05-04 /pmc/articles/PMC10320048/ /pubmed/37140045 http://dx.doi.org/10.1093/nar/gkad353 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://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 Web Server Issue
Xu, Yiran
Zhu, Jianghui
Huang, Wenze
Xu, Kui
Yang, Rui
Zhang, Qiangfeng Cliff
Sun, Lei
PrismNet: predicting protein–RNA interaction using in vivo RNA structural information
title PrismNet: predicting protein–RNA interaction using in vivo RNA structural information
title_full PrismNet: predicting protein–RNA interaction using in vivo RNA structural information
title_fullStr PrismNet: predicting protein–RNA interaction using in vivo RNA structural information
title_full_unstemmed PrismNet: predicting protein–RNA interaction using in vivo RNA structural information
title_short PrismNet: predicting protein–RNA interaction using in vivo RNA structural information
title_sort prismnet: predicting protein–rna interaction using in vivo rna structural information
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320048/
https://www.ncbi.nlm.nih.gov/pubmed/37140045
http://dx.doi.org/10.1093/nar/gkad353
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