科微学术

微生物学报

微生物组大数据管理与分析
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国家重点研发计划(2016YFC0901702,2016YFB1000605);国家“863计划”(2015AA020108)


Big data management and analysis of microbiome
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    摘要:

    高通量技术的迅猛发展促使微生物生态学研究获得了重大突破,掀起了元基因组学(Metagenomics)研究的热潮。元基因组学通常被定义为对未培养的环境样本中微生物群体的DNA序列分析。随着微生物组学数据的日益剧增,微生物大数据的高效管理与分析越来越受到研究者的关注。如何从海量的微生物组数据中挖掘出具有科研价值的数据信息并应用于实际问题成为当前的研究热点。目前已有很多计算生物学程序工具及数据库用于元基因组数据的分析与管理。本文主要综述了随着高通量测序技术的进步,国际上主要的微生物组计划及微生物组数据平台,如人类微生物组项目(humanmicrobiome project,HMP)、地球微生物组项目(earth microbiomeproject,EMP)、欧盟的肠道微生物组计划(metagenomics of humanintestinal tract,MetaHIT)、MG-RAST、iMicrobe、整合微生物组(integration microbial genomes,IMG)以及EBI Metagenomics等;介绍了微生物数据分析的主要流程与工具;提出了建设多源异构的微生物生态数据管理与分析系统的必要性。

    Abstract:

    Advances in high-throughput sequencing have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field metagenomics, which is often defined as the analysis of DNA sequences from microbial communities in environmental samples without prior need for culturing. Microbiome data management and analysis have caused concern for microbial researchers because of the dramatic increase of metagenomics data. It has been a research hotspot that how to mine valuable information for further application form such big microbial data. Till now, many metagenomics computational tools and databases have been provided in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and international microbiome projects as well as the platforms for microbial data archiving and analysis, such as Human Microbiome Project (HMP), Earth Microbiome Project (EMP), Metagenomics of Human Intestinal Tract (MetaHIT), MG-RAST, iMicrobe, Integration Microbial Genomes (IMG) and EBI Metagenomics and so on. We also discussed about the basic pipelines and main tools for metagenomics data. Finally, we proposed the necessity of establishing a platform for multi-source microbial data management and bioinformatic analysis.

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亓合媛,孙清岚,马俊才. 微生物组大数据管理与分析. 微生物学报, 2017, 57(6): 932-941

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  • 收稿日期:2017-02-16
  • 最后修改日期:2017-04-04
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  • 在线发布日期: 2017-05-27
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