Next Article in Journal
Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)
Next Article in Special Issue
rMSIKeyIon: An Ion Filtering R Package for Untargeted Analysis of Metabolomic LDI-MS Images
Previous Article in Journal
Statistical Workflow for Feature Selection in Human Metabolomics Data
Previous Article in Special Issue
Visualization and Interpretation of Multivariate Associations with Disease Risk Markers and Disease Risk—The Triplot
Open AccessArticle

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

1
Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
2
Department of Congenital Disorders, Center for Newborn Screening, Statens Serum Institut, 2300 Copenhagen, Denmark
3
Research Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women’s University, Seoul 04310, Korea
4
Glasgow Polyomics, University of Glasgow, Glasgow G12 8QQ, UK
5
School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
6
Bioinformatics Group, Department of Plant Sciences, Wageningen University, 6708 PB Wageningen, The Netherlands
7
Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
8
Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA
*
Authors to whom correspondence should be addressed.
Metabolites 2019, 9(7), 144; https://doi.org/10.3390/metabo9070144
Received: 29 May 2019 / Revised: 10 July 2019 / Accepted: 11 July 2019 / Published: 16 July 2019
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. View Full-Text
Keywords: chemical classification; in silico workflows; metabolite annotation; metabolite identification; metabolome mining; molecular families; networking; substructures chemical classification; in silico workflows; metabolite annotation; metabolite identification; metabolome mining; molecular families; networking; substructures
Show Figures

Graphical abstract

MDPI and ACS Style

Ernst, M.; Kang, K.B.; Caraballo-Rodríguez, A.M.; Nothias, L.-F.; Wandy, J.; Chen, C.; Wang, M.; Rogers, S.; Medema, M.H.; Dorrestein, P.C.; van der Hooft, J.J. MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools. Metabolites 2019, 9, 144.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop