Creighton Chad J, Reid Jeffrey G, Gunaratne Preethi H
Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA.
Brief Bioinform. 2009 Sep;10(5):490-7. doi: 10.1093/bib/bbp019. Epub 2009 Mar 30.
MicroRNAs are short non-coding RNAs that regulate the stability and translation of mRNAs. Profiling experiments, using microarray or deep sequencing technology, have identified microRNAs that are preferentially expressed in certain tissues, specific stages of development, or disease states such as cancer. Deep sequencing utilizes massively parallel sequencing, generating millions of small RNA sequence reads from a given sample. Profiling of microRNAs by deep sequencing measures absolute abundance and allows for the discovery of novel microRNAs that have eluded previous cloning and standard sequencing efforts. Public databases provide in silico predictions of microRNA gene targets by various algorithms. To better determine which of these predictions represent true positives, microRNA expression data can be integrated with gene expression data to identify putative microRNA:mRNA functional pairs. Here we discuss tools and methodologies for the analysis of microRNA expression data from deep sequencing.
微小RNA是一类短的非编码RNA,可调控信使核糖核酸(mRNA)的稳定性和翻译过程。使用微阵列或深度测序技术的分析实验已鉴定出在某些组织、特定发育阶段或疾病状态(如癌症)中优先表达的微小RNA。深度测序利用大规模平行测序技术,从给定样本中生成数百万个小RNA序列读数。通过深度测序对微小RNA进行分析可测量其绝对丰度,并能发现以往克隆和标准测序工作未能发现的新型微小RNA。公共数据库通过各种算法提供微小RNA基因靶标的计算机预测结果。为了更好地确定这些预测中哪些代表真正的阳性结果,可以将微小RNA表达数据与基因表达数据整合,以识别假定的微小RNA:mRNA功能对。在此,我们讨论用于分析来自深度测序的微小RNA表达数据的工具和方法。