Workflow Development for the Functional Characterization of ncRNAs.
Workflow Development for the Functional Characterization of ncRNAs.
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DOI：
10.1007/978-1-4939-8982-9_5
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发表时间：
2019
影响因子：
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通讯作者：
M. Wolfien;D. Brauer;Andrea Bagnacani;O. Wolkenhauer
中科院分区：
文献类型：
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作者：
M. Wolfien;D. Brauer;Andrea Bagnacani;O. Wolkenhauer
在过去的十年中，ncRNA已被深入研究，并揭示了其在各种生物过程中的调节作用。世界范围内的研究工作已经确定了许多ncRNA和多种RNA亚型，这归因于已知与不同功能层（从DNA和RNA到蛋白质）相互作用的不同功能。这使得新鉴定的ncRNA的功能预测具有挑战性。目前的生物信息学和系统生物学方法显示出有希望的结果，以促进这些不同的ncRNA功能的识别。在这里，我们回顾（a）目前的实验方案，即，用于下一代测序，用于成功鉴定ncRNA;（B）测序数据分析工作流程以及可用的计算环境;和（c）功能性表征ncRNA的最新方法，例如，通过转录组关联研究、分子网络分析或人工智能指导的预测。此外，我们提出了一种策略，通过使用连接工作流程来涵盖未知转录本的识别和功能表征。
During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.