Genomic data mining for functional annotation of human long noncoding RNAs

Abstract

Life may have begun in an RNA world, which is supported by increasing evidence of the vital role that RNAs perform in biological systems. In the human genome, most genes actually do not encode proteins; they are noncoding RNA genes. The largest class of noncoding genes is known as long noncoding RNAs (lncRNAs), which are transcripts greater in length than 200 nucleotides, but with no protein-coding capacity. While some lncRNAs have been demonstrated to be key regulators of gene expression and 3D genome organization, most lncRNAs are still uncharacterized. We thus propose several data mining and machine learning approaches for the functional annotation of human lncRNAs by leveraging the vast amount of data from genetic and genomic studies. Recent results from our studies and those of other groups indicate that genomic data mining can give insights into lncRNA functions and provide valuable information for experimental studies of candidate lncRNAs associated with human disease.

概要

越来越多证据表明RNA 在生物系统中扮演着重 要的角色,而这些发现支持了生命起源于RNA 的假设。在人类基因组中,大部分的基因并不编 码蛋白质,被称为非编码RNA 基因。长非编码 RNA(lncRNA)是其中最大的一类,其转录本长 度大于200 个核苷酸。虽然一些lncRNA 已被证 明是调控基因表达和3D 基因组结构的重要元 件,但是大部分lncRNA 还未被研究和注释。本 课题组利用大量基因组数据,提出一些基于数据 挖掘和机器学习的方法,对人类lncRNA 进行功 能注释。我们与其他同领域课题组的近期研究结 果表明,基因组数据挖掘可帮助加深对lncRNA 功能的理解,并为与疾病相关lncRNA 的实验研 究提供重要信息。