|Title||Molnetenhancer: Enhanced molecular networks by integrating metabolome mining and annotation tools|
|Author(s)||Ernst, Madeleine; Kang, Kyo Bin; Caraballo-Rodríguez, Andrés Mauricio; Nothias, Louis Felix; Wandy, Joe; Chen, Christopher; Wang, Mingxun; Rogers, Simon; Medema, Marnix H.; Dorrestein, Pieter C.; Hooft, Justin J.J. van der|
|Source||Metabolites 9 (2019)7. - ISSN 2218-1989|
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Chemical classification - In silico workflows - Metabolite annotation - Metabolite identification - Metabolome mining - Molecular families - Networking - Substructures|
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.