MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools
MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools
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DOI：
10.3390/metabo9070144
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发表时间：
2019-07-01
期刊：
影响因子：
4.1
通讯作者：
van der Hooft, Justin J. J.
中科院分区：
文献类型：
--
作者：
Ernst, Madeleine;Kang, Kyo Bin;van der Hooft, Justin J. J.
代谢组学已经开始采用计算方法对大型数据集进行化学解释。然而，代谢物注释仍然是一个关键挑战。最近，分子网络和MS2LDA作为分子挖掘工具出现，在质谱裂解数据中发现分子家族和子结构。此外，计算机注释工具可获得碎片化光谱的候选分子并对其进行排序。理想情况下，从这些计算工具中获得和推断的所有结构信息都可以结合起来，以增加人们可以从数据集中获得的化学洞察力。然而，由于每个工具都有自己的输出格式，并且缺乏跨这些工具的有效数据匹配，因此目前的集成受到阻碍。在这里，我们介绍MolNetEnhancer，一个结合分子网络，MS2LDA，计算机注释工具（如网络注释传播或DEREPLICATOR）的输出的工作流程，以及通过ClassyFire的自动化学分类，以提供代谢组学数据的更全面的化学概述，同时阐明每个碎片光谱的结构细节。我们提出了四个植物和细菌的案例研究的例子，并显示MolNetEnhancer如何使化学注释，可视化，并发现分子家族内的微妙的亚结构多样性。我们得出结论，MolNetEnhancer是一个有用的工具，极大地帮助代谢组学研究人员通过多个独立的计算机管道的组合来破译代谢组。
Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and e ffi cient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR), and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.