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MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools

机译：MolNetEnhancer：通过整合代谢组学挖掘和注释工具来增强分子网络

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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 efficient 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.
机译：代谢组学已经开始包含用于对大数据集进行化学解释的计算方法。然而，代谢物注释仍然是关键的挑战。最近，分子网络和MS2LDA成为分子挖掘工具，可在质谱碎裂数据中找到分子家族和亚结构。此外，计算机内注释工具可以获取碎片分子并对其进行排名，以获取碎片光谱。理想情况下，可以将从这些计算工具获得并推断出的所有结构信息进行组合，以增加从数据集中可以获得的化学洞察力。但是，由于每个工具都有其自己的输出格式，并且目前缺乏跨这些工具的有效数据匹配，因此集成受到了阻碍。在这里，我们介绍MolNetEnhancer，该工作流结合了分子网络，MS2LDA，计算机注释工具（例如网络注释传播或DEREPLICATOR）的输出以及通过ClassyFire进行的自动化学分类，以提供代谢组学数据的更全面的化学概览，同时同时阐明了每个碎片谱的结构细节。我们提供了来自四个植物和细菌案例研究的示例，并展示了MolNetEnhancer如何实现化学注释，可视化以及发现分子家族内微妙的亚结构多样性。我们得出的结论是，MolNetEnhancer是一个有用的工具，可以通过组合多个独立的计算机模拟管线，极大地帮助代谢组学研究人员破译代谢组。

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