Mathew Samatha, Sivadas Ambily, Sehgal Paras, Kaushik Kriti, Vellarikkal Shamsudheen K, Scaria Vinod, Sivasubbu Sridhar
Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
Academy of Scientific and Innovative Research (AcSIR), New Delhi, India.
Methods Mol Biol. 2019;1912:77-110. doi: 10.1007/978-1-4939-8982-9_4.
Long noncoding RNAs (lncRNAs) belong to a class of RNA transcripts that do not have the potential to code for proteins. LncRNAs were largely discovered in the transcriptomes of human and several model organisms, using next-generation sequencing (NGS) approaches, which have enabled a comprehensive genome scale annotation of transcripts. LncRNAs are known to have dynamic expression status and have the potential to orchestrate gene regulation at the epigenetic, transcriptional, and posttranscriptional levels. Here we describe the experimental methods involved in the discovery of lncRNAs from the transcriptome of a popular model organism zebrafish (Danio rerio). A structured and well-designed computational analysis pipeline subsequent to the RNA sequencing can be instrumental in revealing the diversity of the lncRNA transcripts. We describe one such computational pipeline used for the discovery of novel lncRNA transcripts in zebrafish. We also detail the validation of the putative novel lncRNA transcripts using qualitative and quantitative assays in zebrafish.
长链非编码RNA（lncRNAs）属于一类没有编码蛋白质潜力的RNA转录本。使用下一代测序（NGS）方法，lncRNAs主要在人类和几种模式生物的转录组中被发现，这些方法能够对转录本进行全面的全基因组规模注释。已知lncRNAs具有动态表达状态，并有可能在表观遗传、转录和转录后水平上协调基因调控。在这里，我们描述了从一种流行的模式生物斑马鱼（Danio rerio）的转录组中发现lncRNAs所涉及的实验方法。RNA测序之后进行的结构化且精心设计的计算分析流程有助于揭示lncRNA转录本的多样性。我们描述了一种用于在斑马鱼中发现新型lncRNA转录本的计算流程。我们还详细介绍了使用斑马鱼中的定性和定量分析对推定的新型lncRNA转录本进行验证的过程。
