Cogan Joseph A, Benova Natalia, Kuklinkova Rene, Boyne James R, Anene Chinedu A
School of Biological Sciences, University of Huddersfield, Huddersfield, HD1 3DH, United Kingdom.
School of Molecular and Cellular Biology, University of Leeds, Leeds, LS2 9JT, United Kingdom.
Bioinform Adv. 2024 Aug 26;4(1):vbae127. doi: 10.1093/bioadv/vbae127. eCollection 2024.
Recent RNA-centric experimental methods have significantly expanded our knowledge of proteins with known RNA-binding functions. However, the complete regulatory network and pathways for many of these RNA-binding proteins (RBPs) in different cellular contexts remain unknown. Although critical to understanding the role of RBPs in health and disease, experimentally mapping the RBP-RNA interactomes in every single context is an impossible task due the cost and manpower required. Additionally, identifying relevant RNAs bound by RBPs is challenging due to their diverse binding modes and function.
To address these challenges, we developed RBP interaction mapper RBPInper an integrative framework that discovers global RBP interactome using statistical data fusion. Experiments on splicing factor proline and glutamine rich (SFPQ) datasets revealed cogent global SFPQ interactome. Several biological processes associated with this interactome were previously linked with SFPQ function. Furthermore, we conducted tests using independent dataset to assess the transferability of the SFPQ interactome to another context. The results demonstrated robust utility in generating interactomes that transfers to unseen cellular context. Overall, RBPInper is a fast and user-friendly method that enables a systems-level understanding of RBP functions by integrating multiple molecular datasets. The tool is designed with a focus on simplicity, minimal dependencies, and straightforward input requirements. This intentional design aims to empower everyday biologists, making it easy for them to incorporate the tool into their research.
The source code, documentation, and installation instructions as well as results for use case are freely available at https://github.com/AneneLab/RBPInper. A user can easily compile similar datasets for a target RBP.
最近以RNA为中心的实验方法显著扩展了我们对具有已知RNA结合功能的蛋白质的认识。然而，许多这些RNA结合蛋白（RBP）在不同细胞环境中的完整调控网络和途径仍然未知。尽管对于理解RBP在健康和疾病中的作用至关重要，但由于所需的成本和人力，在每个单独的环境中通过实验绘制RBP-RNA相互作用组是一项不可能完成的任务。此外，由于RBP的结合模式和功能多样，识别其结合的相关RNA具有挑战性。
为了应对这些挑战，我们开发了RBP相互作用映射器RBPInper，这是一个使用统计数据融合来发现全局RBP相互作用组的综合框架。对富含脯氨酸和谷氨酰胺的剪接因子（SFPQ）数据集的实验揭示了有说服力的全局SFPQ相互作用组。与该相互作用组相关的几个生物学过程以前与SFPQ功能有关。此外，我们使用独立数据集进行测试，以评估SFPQ相互作用组转移到另一种环境的可转移性。结果表明，在生成可转移到未见过的细胞环境的相互作用组方面具有强大的实用性。总体而言，RBPInper是一种快速且用户友好的方法，通过整合多个分子数据集能够从系统层面理解RBP功能。该工具的设计侧重于简单性、最小依赖性和直接的输入要求。这种有意的设计旨在赋予普通生物学家权力，使他们能够轻松地将该工具纳入自己的研究中。
源代码、文档、安装说明以及用例结果可在https://github.com/AneneLab/RBPInper上免费获取。用户可以轻松地为目标RBP编译类似的数据集。
