ABSTRACT

Introduction

Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system.

Areas covered

This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches.

Expert opinion

Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.

中文翻译：

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代谢组学数据分析和解释中基于网络的策略：从分子网络到生物学解释。

摘要

介绍

代谢组学已成为系统生物学的重要组成部分。但是，数据分析仍然经常以还原论的方式进行，重点放在单个代谢物的变化上。尽管这些方法确实提供了有关生物体代谢表型的相关见解，但在考虑整个系统时，可能会更好地探索代谢关系的复杂性质。

覆盖区域

这篇综述从不同的角度突出了可用于代谢组学数据分析的多种网络策略，包括：基于定量信息的关联网络，协助代谢物注释的质谱相似性网络以及用于系统数据解释的生化网络。我们还重点介绍了通过探索此类方法获得的有关代谢组织的一些相关见解。

专家意见

基于网络的分析是一种既定方法，可以识别质谱中的非直觉代谢关系以及未知化合物。另外，在代谢网络中，来自代谢组学的数据表示是直观的，并允许使用统计分析，从系统的角度可以更好地总结相关的代谢变化。