Mastriani Emilio, Zhai Rihong, Zhu Songling
Systemomics Center, College of Pharmacy, Harbin Medical University, Harbin, China.
Genomics Research Center (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China), Harbin Medical University, Harbin, China.
Methods Mol Biol. 2018;1751:127-138. doi: 10.1007/978-1-4939-7710-9_9.
MicroRNAs (miRNAs) are small, noncoding RNAs that are able to regulate the expression of targeted mRNAs. Thousands of miRNAs have been identified; however, only a few of them have been functionally annotated. Microarray-based expression analysis represents a cost-effective way to identify candidate miRNAs that correlate with specific biological pathways, and to detect disease-associated molecular signatures. Generally, microarray-based miRNA data analysis contains four major steps: (1) quality control and normalization, (2) differential expression analysis, (3) target gene prediction, and (4) functional annotation. For each step, a large couple of software tools or packages have been developed. In this chapter, we present a standard analysis pipeline for miRNA microarray data, assembled by packages mainly developed with R and hosted in Bioconductor project.
微小RNA（miRNA）是一类能够调控靶标mRNA表达的小型非编码RNA。现已鉴定出数千种miRNA；然而，其中只有少数已进行功能注释。基于微阵列的表达分析是一种经济高效的方法，可用于识别与特定生物学途径相关的候选miRNA，并检测疾病相关的分子特征。一般来说，基于微阵列的miRNA数据分析包含四个主要步骤：（1）质量控制和标准化，（2）差异表达分析，（3）靶基因预测，以及（4）功能注释。针对每个步骤，已经开发了大量的软件工具或程序包。在本章中，我们介绍了一种用于miRNA微阵列数据的标准分析流程，该流程由主要用R开发并托管于生物导体项目中的程序包组装而成。
