Statistical analysis of non-coding RNA data

被引：18

作者：

He, Qianchuan ^{[1
]}

Liu, Yang ^{[1
]}

Sun, Wei ^{[1
]}

机构：

[1] Fred Hutchinson Canc Res Ctr, Div Publ Hlth Sci, Seattle, WA 98109 USA

来源：

CANCER LETTERS | 2018年 / 417卷

基金：

美国国家卫生研究院;

关键词：

Long noncoding RNA; MicroRNA; Noncoding RNA; Statistical analysis; Statistical modeling; Target prediction; EXPRESSION ANALYSIS; VARIABLE SELECTION; MICRORNA; GENE; PREDICTION; IDENTIFICATION; CANCER; NORMALIZATION; TOPHAT;

D O I：

10.1016/j.canlet.2017.12.029

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 [肿瘤学];

摘要：

With rapid progress in high-throughput genome technology, the study of noncoding RNA has arisen as a highly popular topic in biomedical research. Noncoding RNA plays fundamental roles in cell proliferation, cell differentiation and epigenetic regulation, and the study of noncoding RNA will yield novel insights into gene regulation and provide new clues for disease treatment. However, due to the large volume and diverse functions of noncoding RNAs, the analysis of these RNAs has proved to be a challenging task. In this review, we review the commonly used computational tools for the identification of noncoding RNAs, and discuss popular statistical tools for their analysis. Due to the large body of noncoding RNA classes, we focus on the analysis of microRNA and long noncoding RNA, two of the most widely studied classes of noncoding RNAs. Specific examples are provided to show the context of the analysis. This review aims to provide up-to-date information on existing tools and methods for identifying and analyzing noncoding RNA. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：161 / 167

页数：7

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