Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
Methods Mol Biol. 2024;2726:209-234. doi: 10.1007/978-1-0716-3519-3_9.
Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.
计算 RNA-RNA 相互作用(RRI)的预测是专门研究分子间 RNA 相互作用和非编码 RNA(如真核 microRNA 或原核小 RNA)的调控作用的核心方法。可用的方法可以根据其潜在的预测策略进行分类,每种方法都暗示着特定的能力和限制,这些往往对非专业用户来说并不透明。在这项工作中,我们回顾了七种 RRI 预测策略,并讨论了各自工具的优点和局限性,因为首先了解这些知识对于选择正确的工具至关重要。在 RRI 预测策略中,基于可及性的方法已被证明可以提供最可靠的预测。在这里,我们描述了如何将 IntaRNA 作为最先进的基于可及性的工具之一,应用于各种用例,以进行计算 RRI 预测任务。提供了针对个别 RRI 预测以及大规模靶标预测场景的详细实践示例。我们通过示例展示了 IntaRNA 的灵活性和功能。每个示例都是使用来自文献的真实数据设计的,并附有关于解释 IntaRNA 输出的各个结果的说明。我们的用例驱动说明使非专业用户能够全面理解和利用 IntaRNA 的功能,以进行有效的 RRI 预测。