遗传 ›› 2011, Vol. 33 ›› Issue (8): 829-846.doi: 10.3724/SP.J.1005.2011.00829

• 综述 • 上一篇    下一篇

基于新一代高通量技术的人类疾病组学研究策略

杨旭, 焦睿, 杨琳, 吴莉萍, 李英睿, 王俊   

  1. 深圳华大基因研究院, 深圳 518083
  • 收稿日期:2011-04-28 修回日期:2011-06-24 出版日期:2011-08-20 发布日期:2011-07-29
  • 通讯作者: 王俊 E-mail:wangj@genomics.org.cn
  • 基金资助:

    全基因组高分辨率中国(东亚)人群遗传变异图谱的绘制(编号:2011CB809200)

New-generation high-throughput technologies based ‘omics’ research strategy in human disease

YANG Xu, JIAO Rui, YANG Lin, WU Li-Ping, LI Ying-Rui, WANG Jun   

  1. BGI-Shenzhen, Shenzhen 518083, China
  • Received:2011-04-28 Revised:2011-06-24 Online:2011-08-20 Published:2011-07-29

摘要: 近年来, 包括第二代测序技术和蛋白质谱技术等在内的新一代高通量技术越来越多的应用于解决生物学问题尤其是人类疾病的研究。这种以数据为导向, 大规模、工业化的研究模式, 使得从基因组水平、转录组水平、蛋白质组水平等角度对疾病展开全方位、多层次的研究成为可能。文章综述了新一代高通量技术在DNA、RNA、表观遗传、宏基因组和蛋白质组水平的人类疾病研究进展以及在转化医学领域的应用。在基因组水平上, 外显子组测序是近年来持续的研究热点, 随着测序成本的不断降低, 全基因组重测序也越来越凸显了其在全基因组范围内检测大型结构变异的优势, 并使得个人基因组引领的个体化医疗逐渐成为可能。在转录组水平, 如小RNA测序技术可用来检测已知小RNA和预测新的小RNA, 这些小RNA不仅可以作为疾病诊断和预后的分子标志物, 在疾病治疗方面也具有无限潜力。在蛋白质组水平, 如目标蛋白质组学可以有目标地测定可能与疾病相关的特定蛋白质或多肽, 能够很好地应用于疾病的临床分期分型。文章进一步阐述了跨组学研究在疾病研究领域中的应用和发展趋势, 借助生物信息学分析方法进行多组学整合研究, 能更加系统地阐释疾病的发生及发展机理, 为疾病的诊断治疗提供强有力的工具。

关键词: 高通量技术, 第二代测序技术, 组学, 疾病研究

Abstract: In recent years, new-generation high-throughput technologies, including next-generation sequencing technology and mass spectrometry method, have been widely applied in solving biological problems, especially in human diseases field. This data driven, large-scale and industrialized research model enables the omnidirectional and multi-level study of human diseases from the perspectives of genomics, transcriptomics and proteomics levels, etc. In this paper, the latest development of the high-throughput technologies that applied in DNA, RNA, epigenomics, metagenomics including proteomics and some applications in translational medicine are reviewed. At genomics level, exome sequencing has been the hot spot of the recent research. However, the predominance of whole genome resequencing in detecting large structural variants within the whole genome level is coming to stand out as the drop of sequencing cost, which also makes it possible for personalized genome based medicine application. At trancriptomics level, e.g., small RNA sequencing can be used to detect known and predict unknown miRNA. Those small RNA could not only be the biomarkers for disease diagnosis and prognosis, but also show the potential of disease treatment. At proteomics level, e.g., target proteomics can be used to detect the possible disease-related protein or peptides, which can be useful index for clinical staging and typing. Furthermore, the application and development of trans-omics study in disease research are briefly introduced. By applying bioinformatics technologies for integrating multi-omics data, the mechanism, diagnosis and therapy of the disease are likely to be systemically explained and realized, so as to provide powerful tools for disease diagnosis and therapies.

Key words: omics, disease research, high-throughput technology, second-generation sequencing