Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration
Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration
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DOI：
10.1093/bib/bbw031
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发表时间：
2017-05-01
影响因子：
9.5
通讯作者：
Masseroli, Marco
中科院分区：
文献类型：
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作者：
Cambiaghi, Alice;Ferrario, Manuela;Masseroli, Marco
代谢组学是一个快速发展的领域，包括在系统规模上分析大量的代谢物。代谢组学的两个主要目标是鉴定表征每种生物体状态的代谢物，并测量它们在不同情况下（例如病理条件，环境因素）的动力学。关于代谢物的知识对于理解大多数细胞现象至关重要，但仅凭这些信息还不足以全面了解所涉及的所有生物过程。因此，需要将代谢组学与转录组学和蛋白质组学相结合的综合方法，以获得比单独使用这些技术更深入的见解。虽然这些信息是可用的，不同的“组学”数据的多层次整合仍然是一个挑战。这些数据的处理、加工、分析和整合需要专门的数学、统计和生物信息学工具，而且存在着一些阻碍该领域快速进展的技术问题。在这里，我们回顾了四个主要的工具，为用户的数量或提供的功能（MetaCore（TM），MetaboAnalyst，InCroMAP和3 Omics）的几个可用的代谢组学数据分析和与其他“组学”数据的整合，突出他们的强和弱的方面;一些相关的问题，影响数据分析和集成也确定和讨论。总的来说，我们提供了一个客观的描述，一些主要的目前可用的软件包的工作，这可能有助于实验从业者在选择一个强大的管道代谢组学数据分析和集成。
Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different 'omics' data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCore (TM), MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other 'omics' data, highlighting their strong and weak aspects; a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration.