Abstract：
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.
摘要：
RNA-RNA相互作用（RRI）的计算预测是用于特异性研究非编码RNA（如真核微RNA或原核小RNA）的分子间RNA相互作用和调节作用的核心方法。可用的方法可以根据其基础预测策略进行分类，每个都暗示特定的能力和限制通常对非专家用户不透明。在这项工作中，我们回顾了七类RRI预测策略，并讨论了各自工具的优势和局限性，因为这些知识对于选择正确的工具至关重要。在RRI预测策略中，基于可达性的方法已被证明可以提供最可靠的预测。这里,我们描述了IntaRNA，作为最先进的基于可访问性的工具之一，可以应用于计算RRI预测任务的各种用例中。提供了各个RRI预测以及大规模目标预测场景的详细实践示例。我们通过实例说明了IntaRNA的灵活性和能力。每个实施例都是使用来自文献的真实数据设计的，并附有解释来自IntaRNA输出的相应结果的说明。我们的用例驱动指令使非专家用户能够全面理解和利用IntaRNA的功能进行有效的RRI预测。
