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变异游戏:利用二代测序技术和组学数据破解复杂遗传疾病

The variation game: Cracking complex genetic disorders with NGS and omics data.

作者信息

Cui Hongzhu, Dhroso Andi, Johnson Nathan, Korkin Dmitry

机构信息

Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, United States.

Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, United States; Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, United States.

出版信息

Methods. 2015 Jun;79-80:18-31. doi: 10.1016/j.ymeth.2015.04.018. Epub 2015 May 2.

DOI:10.1016/j.ymeth.2015.04.018
PMID:25944472
Abstract

Tremendous advances in Next Generation Sequencing (NGS) and high-throughput omics methods have brought us one step closer towards mechanistic understanding of the complex disease at the molecular level. In this review, we discuss four basic regulatory mechanisms implicated in complex genetic diseases, such as cancer, neurological disorders, heart disease, diabetes, and many others. The mechanisms, including genetic variations, copy-number variations, posttranscriptional variations, and epigenetic variations, can be detected using a variety of NGS methods. We propose that malfunctions detected in these mechanisms are not necessarily independent, since these malfunctions are often found associated with the same disease and targeting the same gene, group of genes, or functional pathway. As an example, we discuss possible rewiring effects of the cancer-associated genetic, structural, and posttranscriptional variations on the protein-protein interaction (PPI) network centered around P53 protein. The review highlights multi-layered complexity of common genetic disorders and suggests that integration of NGS and omics data is a critical step in developing new computational methods capable of deciphering this complexity.

摘要

下一代测序(NGS)和高通量组学方法取得的巨大进展,使我们在分子水平上对复杂疾病的机制理解又迈进了一步。在本综述中,我们讨论了与复杂遗传疾病相关的四种基本调控机制,如癌症、神经疾病、心脏病、糖尿病等诸多疾病。这些机制包括基因变异、拷贝数变异、转录后变异和表观遗传变异,可使用多种NGS方法进行检测。我们提出,在这些机制中检测到的故障不一定是独立的,因为这些故障常常与同一种疾病相关,且针对相同的基因、基因群或功能通路。例如,我们讨论了癌症相关的基因、结构和转录后变异对以P53蛋白为中心的蛋白质 - 蛋白质相互作用(PPI)网络可能产生的重新布线效应。本综述强调了常见遗传疾病的多层次复杂性,并表明整合NGS和组学数据是开发能够解读这种复杂性的新计算方法的关键一步。

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