使用 RBPInper 工具对 RNA 结合蛋白的 RNA 相互作用图谱进行元分析。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-08-26 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae127
Joseph A Cogan, Natalia Benova, Rene Kuklinkova, James R Boyne, Chinedu A Anene
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引用次数: 0

摘要

动机最近以 RNA 为中心的实验方法大大扩展了我们对已知具有 RNA 结合功能的蛋白质的了解。然而,许多 RNA 结合蛋白(RBPs)在不同细胞环境中的完整调控网络和途径仍然未知。尽管 RBPs 对了解其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,在实验中绘制每种情况下的 RBP-RNA 相互作用组是不可能完成的任务。此外,由于 RBPs 的结合模式和功能多种多样,确定与 RBPs 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,它是一个整合框架,能利用统计数据融合发现全局RBP相互作用组。在富含脯氨酸和谷氨酰胺的剪接因子(SFPQ)数据集上进行的实验揭示了清晰的全球 SFPQ 相互作用组。与该相互作用组相关的几个生物过程以前都与 SFPQ 的功能有关。此外,我们还使用独立数据集进行了测试,以评估 SFPQ 相互作用组在其他环境中的可移植性。结果表明,在生成可转移到未知细胞环境的相互作用组方面,RBPInper 具有很强的实用性。总之,RBPInper 是一种快速且用户友好的方法,它通过整合多个分子数据集来实现对 RBP 功能的系统级理解。该工具的设计注重简单性、最小依赖性和直接输入要求。这种有意识的设计旨在增强日常生物学家的能力,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、安装说明以及使用案例的结果均可在 https://github.com/AneneLab/RBPInper 免费获取。用户可以轻松地为目标 RBP 汇编类似的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Meta-analysis of RNA interaction profiles of RNA-binding protein using the RBPInper tool.

Motivation: 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.

Results: 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.

Availability and implementation: 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.

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