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大规模处理微生物组组学数据的生物信息学工作流程中的挑战

Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale.

作者信息

Hu Bin, Canon Shane, Eloe-Fadrosh Emiley A, Babinski Michal, Corilo Yuri, Davenport Karen, Duncan William D, Fagnan Kjiersten, Flynn Mark, Foster Brian, Hays David, Huntemann Marcel, Jackson Elais K Player, Kelliher Julia, Li Po-E, Lo Chien-Chi, Mans Douglas, McCue Lee Ann, Mouncey Nigel, Mungall Christopher J, Piehowski Paul D, Purvine Samuel O, Smith Montana, Varghese Neha Jacob, Winston Donald, Xu Yan, Chain Patrick S G

机构信息

Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States.

Lawrence Berkeley National Laboratory, Berkeley, CA, United States.

出版信息

Front Bioinform. 2022 Jan 17;1:826370. doi: 10.3389/fbinf.2021.826370. eCollection 2021.

Abstract

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

摘要

微生物组科学这一新兴领域正在从对环境中存在的分类群和功能进行编目的描述性方法,转向应用多组学方法来研究微生物组的动态变化和功能。大量新的工具和算法已被设计出来,并用于个别研究者或研究团队所采集样本的特定目的。虽然这些进展颇具启发性,但由于生物信息学方法缺乏标准化应用,不同研究团队生成的微生物组数据的比较能力受到了阻碍。此外,很少有能够大规模处理宏基因组、宏转录组、宏蛋白质组和代谢组学数据的广泛生物信息学工作流程实例,也没有一个中央枢纽允许进行数据处理,或提供可查找、可访问、可互操作和可重复使用(FAIR)的多样组学数据。在此,我们回顾了微生物组研究领域在分析组学数据时存在的一些挑战,并介绍了国家微生物组数据协作组织如何采用标准化和开放获取的方法来应对这些挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2be/9580927/afd845c6f8de/fbinf-01-826370-g001.jpg

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