南方医科大学学报 ›› 2015, Vol. 35 ›› Issue (07): 931-.

• •    下一篇

微生物组学大数据分析方法、挑战与机遇

盛华芳,周宏伟   

  • 出版日期:2015-07-20 发布日期:2015-07-20

Methods, challenges and opportunities for big data analyses of microbiome

  • Online:2015-07-20 Published:2015-07-20

摘要: 微生物组学是新兴学科,与肠道、代谢、生殖、神经等大量慢性疾病相关。通过测序分析微生物组,主要包括16S rRNA和
宏基因组两大技术。16S rRNA数据分析主要包括序列处理、样品多样性分析及统计分析3个步骤。宏基因组数据分析主要包
括序列处理、分类、注释及统计分析4个环节。随着测序技术的升级,测序成本将逐步降低,而大数据分析将成为核心内容。数
据的标准化和可积累性、通过数据建模和预测疾病的发生发展是未来应用的基础,数据知识产权保护以及数据本身价值的开发
与保护价值将日益显现,培养和基于培养的功能验证将是未来的重点之一。人体微生物组学将阐述并调整人与微生物组之间
的关系,此领域相关研究有巨大的发展空间。

Abstract: Microbiome is a novel research field related with a variety of chronic inflamatory diseases. Technically, there are two
major approaches to analysis of microbiome: metataxonome by sequencing the 16S rRNA variable tags, and metagenome by
shot-gun sequencing of the total microbial (mainly bacterial) genome mixture. The 16S rRNA sequencing analyses pipeline
includes sequence quality control, diversity analyses, taxonomy and statistics; metagenome analyses further includes gene
annotation and functional analyses. With the development of the sequencing techniques, the cost of sequencing will decrease,
and big data analyses will become the central task. Data standardization, accumulation, modeling and disease prediction are
crucial for future exploit of these data. Meanwhile, the information property in these data, and the functional verification with
culture-dependent and culture-independent experiments remain the focus in future research. Studies of human microbiome
will bring a better understanding of the relations between the human body and the microbiome, especially in the context of
disease diagnosis and therapy, which promise rich research opportunities.