Ortogero Nicole, Hennig Grant W, Luong Dickson, Yan Wei
Department of Physiology and Cell Biology, University of Nevada School of Medicine, 1664 North Virginia Street, MS575, Reno, NV, 89557, USA.
Methods Mol Biol. 2015;1218:353-64. doi: 10.1007/978-1-4939-1538-5_22.
Small noncoding RNAs (sncRNAs) are widely expressed in the cell of almost all known species. Most sncRNAs appear to have regulatory roles, ranging from facilitating RNA production and modifications (e.g., snoRNAs) to control of mRNA stability and translational efficiency (e.g., miRNAs and endo-siRNA) and to transposon silencing (e.g., piRNAs). The affordability and efficiency of next-generation RNA deep sequencing (RNA-Seq) technologies have made sncRNA deep sequencing (sncRNA-Seq) analyses a routine in biomedical research. SncRNA-Seq analyses generate millions of reads and gigabytes of data; annotation of sncRNA-Seq data remains challenging due to a lack of comprehensive sncRNA annotation pipelines. To solve this problem, we have developed a computer-assisted sncRNA annotation pipeline, which uses open-source software and allows for not only proper classification of known sncRNAs, but also discovery of novel sncRNA species. In this chapter, we describe our sncRNA annotation protocol in detail.
小非编码RNA（sncRNAs）在几乎所有已知物种的细胞中广泛表达。大多数sncRNAs似乎具有调控作用，范围从促进RNA产生和修饰（如snoRNAs）到控制mRNA稳定性和翻译效率（如miRNAs和内源性siRNA）以及转座子沉默（如piRNAs）。新一代RNA深度测序（RNA-Seq）技术的可承受性和效率使得sncRNA深度测序（sncRNA-Seq）分析成为生物医学研究中的常规操作。sncRNA-Seq分析会产生数百万条读数和数千兆字节的数据；由于缺乏全面的sncRNA注释流程，sncRNA-Seq数据的注释仍然具有挑战性。为了解决这个问题，我们开发了一种计算机辅助的sncRNA注释流程，该流程使用开源软件，不仅可以对已知的sncRNAs进行正确分类，还可以发现新的sncRNA种类。在本章中，我们将详细描述我们的sncRNA注释方案。
