Interactive implementations of thermodynamics-based RNA structure and RNA-RNA interaction prediction approaches for example-driven teaching.
Interactive implementations of thermodynamics-based RNA structure and RNA-RNA interaction prediction approaches for example-driven teaching.
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DOI：
10.1371/journal.pcbi.1006341
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发表时间：
2018-08
影响因子：
4.3
通讯作者：
Backofen R
中科院分区：
文献类型：
--
作者：
Raden M;Mohamed MM;Ali SM;Backofen R
基于RNA的细胞过程调控的研究正在成为生物或医学研究中越来越重要的部分。为了分析这类数据，与RNA相关的预测工具被集成到许多管道和工作流程中。为了正确地应用和调整这些程序，用户必须准确地了解它们的限制和概念。在这份手稿中，我们提供了数学基础，并提取了最先进的RNA结构和RNA-RNA相互作用预测算法的核心算法思想。为了允许读者更改和调整算法，或者处理不同的输入，我们提供了一个开源的Web界面，用于实现每个算法的JavaScript实现和可视化。这一概念性的、以教学为中心的演示能够对这些方法进行高水平的调查，同时为理解重要的概念提供足够的细节。这是通过使用http://rna.informatik.uni-freiburg.de/Teaching/.上提供的Web界面简单地生成和研究示例而得到的结合起来，我们为教、学和理解所讨论的预测工具提供了宝贵的资源，从而能够对RNA相关的影响进行更有见地的分析。RNA分子在许多细胞过程中扮演着中心角色。因此，对基于RNA的调控的分析提供了有价值的见解，并且往往对生物学和医学研究至关重要。为了正确地选择合适的算法并应用现有的RNA结构和RNA-RNA相互作用预测软件，充分了解它们的局限性和概念是至关重要的。最终用户很难实现这样的概述，因为大多数最先进的工具都是在专家级别介绍的，在教科书中没有讨论。在这份手稿中，我们提供了数学手段，并提取了算法概念，这些概念是最先进的RNA结构和RNA-RNA相互作用预测算法的核心。概念性的、以教学为中心的演示使我们能够使用简化的模型详细地理解这些方法，以达到教学目的。我们通过使用我们的算法实现的Web界面提供明确的示例来支持这一过程。总之，我们已经汇编了材料和网络应用程序，用于教学和自学几种最先进的算法，这些算法通常用于调查RNA在调节过程中的作用。
The investigation of RNA-based regulation of cellular processes is becoming an increasingly important part of biological or medical research. For the analysis of this type of data, RNA-related prediction tools are integrated into many pipelines and workflows. In order to correctly apply and tune these programs, the user has to have a precise understanding of their limitations and concepts. Within this manuscript, we provide the mathematical foundations and extract the algorithmic ideas that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms. To allow the reader to change and adapt the algorithms or to play with different inputs, we provide an open-source web interface to JavaScript implementations and visualizations of each algorithm. The conceptual, teaching-focused presentation enables a high-level survey of the approaches, while providing sufficient details for understanding important concepts. This is boosted by the simple generation and study of examples using the web interface available at http://rna.informatik.uni-freiburg.de/Teaching/. In combination, we provide a valuable resource for teaching, learning, and understanding the discussed prediction tools and thus enable a more informed analysis of RNA-related effects. RNA molecules are central players in many cellular processes. Thus, the analysis of RNA-based regulation has provided valuable insights and is often pivotal to biological and medical research. In order to correctly select appropriate algorithms and apply available RNA structure and RNA–RNA interaction prediction software, it is crucial to have a good understanding of their limitations and concepts. Such an overview is hard to achieve by end users, since most state-of-the-art tools are introduced on expert level and are not discussed in text books. Within this manuscript, we provide the mathematical means and extract the algorithmic concepts that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms. The conceptual, teaching-focused presentation enables a detailed understanding of the approaches using a simplified model for didactic purposes. We support this process by providing clear examples using the web interface of our algorithm implementation. In summary, we have compiled material and web applications for teaching—and the self-study of—several state-of-the-art algorithms commonly used to investigate the role of RNA in regulatory processes.