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代谢组学数据的功能分析

Functional Analysis of Metabolomics Data.

作者信息

Chagoyen Mónica, López-Ibáñez Javier, Pazos Florencio

机构信息

Computational Systems Biology Group (CNB-CSIC), C/ Darwin, 3, Madrid, 28049, Spain.

出版信息

Methods Mol Biol. 2016;1415:399-406. doi: 10.1007/978-1-4939-3572-7_20.

Abstract

Metabolomics aims at characterizing the repertory of small chemical compounds in a biological sample. As it becomes more massive and larger sets of compounds are detected, a functional analysis is required to convert these raw lists of compounds into biological knowledge. The most common way of performing such analysis is "annotation enrichment analysis," also used in transcriptomics and proteomics. This approach extracts the annotations overrepresented in the set of chemical compounds arisen in a given experiment. Here, we describe the protocols for performing such analysis as well as for visualizing a set of compounds in different representations of the metabolic networks, in both cases using free accessible web tools.

摘要

代谢组学旨在对生物样品中的小分子化合物库进行表征。随着检测到的化合物数量越来越多且规模越来越大,需要进行功能分析,以便将这些原始的化合物列表转化为生物学知识。进行此类分析最常用的方法是“注释富集分析”,转录组学和蛋白质组学中也会用到。这种方法提取在给定实验中出现的化合物集中过度富集的注释。在这里,我们描述了进行此类分析以及在代谢网络的不同表示形式中可视化一组化合物的方案,在这两种情况下都使用免费的网络工具。

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