核糖核酸
生物
RNA结合蛋白
计算生物学
核糖开关
核酸结构
结合位点
RNA编辑
细胞生物学
非编码RNA
遗传学
基因
作者
Yiran Xu,Jianghui Zhu,Wenze Huang,Kui Xu,Rui Yang,Qiangfeng Cliff Zhang,Lei Sun
出处
期刊:PubMed
日期:2023-05-04
摘要
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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