Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel.
Department of Computer Sciences, Technion-Israel Institute of Technology, Haifa, Israel.
Methods Mol Biol. 2022;2404:53-65. doi: 10.1007/978-1-0716-1851-6_3.
RNA-binding proteins (RBPs) play a key role in post-transcriptional regulation via binding to coding and non-coding RNAs. Recent development in experimental technologies, aimed to identify the targets of RBPs, has significantly broadened our knowledge on protein-RNA interactions. However, for many RBPs in many organisms and cell types, experimental RNA-binding data is not available. In this chapter we describe a computational approach, named RBPmap, available as a web service via http://rbpmap.technion.ac.il/ and as a stand-alone version for download. RBPmap was designed for mapping and predicting the binding sites of any RBP within a nucleic acid sequence, given the availability of an experimentally defined binding motif of the RBP. The algorithm searches for a sub-sequence that significantly matches the RBP motif, considering the clustering propensity of other weak matches within the motif environment. Here, we present different applications of RBPmap for discovering the involvement of RBPs and their targets in a variety of cellular processes, in health and disease states. Finally, we demonstrate the performance of RBPmap in predicting the binding targets of RBPs in large-scale RNA-binding data, reinforcing the strength of the tool in distinguishing cognate binding sites from weak motifs.
RNA 结合蛋白 (RBPs) 通过与编码和非编码 RNA 结合,在转录后调控中发挥关键作用。旨在鉴定 RBP 靶标的实验技术的最新发展,极大地拓宽了我们对蛋白质-RNA 相互作用的认识。然而,对于许多生物体和细胞类型中的许多 RBP,其实验 RNA 结合数据不可用。在本章中,我们描述了一种计算方法,名为 RBPmap,可通过 http://rbpmap.technion.ac.il/ 作为网络服务获得,并可作为独立版本下载。RBPmap 旨在为任何 RBP 在核酸序列中的结合位点进行作图和预测,前提是该 RBP 的实验定义的结合基序可用。该算法会搜索与 RBP 基序显著匹配的子序列,同时考虑到基序环境中其他弱匹配的聚类倾向。在这里,我们展示了 RBPmap 在发现 RBPs 及其靶标在各种细胞过程中的参与,以及在健康和疾病状态下的不同应用。最后,我们证明了 RBPmap 在预测大规模 RNA 结合数据中 RBP 结合靶标的性能,从而增强了该工具在区分同源结合位点和弱基序方面的优势。