- Kyo Bin Kang (1)
- Pieter C. Dorrestein (1)
- Christopher Chen (1)
- Madeleine Ernst (1)
- Louis Felix Nothias (1)
- Marnix H. Medema (1)
- Justin J.J. Hooft van der (1)
- Doris M. Jacobs (1)
- Andrés Mauricio Caraballo-Rodríguez (1)
- John P.M. Duynhoven Van (1)
- Simon Rogers (1)
- Joe Wandy (1)
- Mingxun Wang (1)
Molnetenhancer: Enhanced molecular networks by integrating metabolome mining and annotation tools
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 - \ 2019
Metabolites 9 (2019)7. - ISSN 2218-1989
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.
Assessment of dietary exposure and effect in humans : The role of NMR
Duynhoven, John P.M. Van; Jacobs, Doris M. - \ 2016
Progress in Nuclear Magnetic Resonance Spectroscopy 96 (2016). - ISSN 0079-6565 - p. 58 - 72.
Dietary effect - Dietary exposure - Metabolite identification - Metabolomics - NMR
In human nutritional science progress has always depended strongly on analytical measurements for establishing relationships between diet and health. This field has undergone significant changes as a result of the development of NMR and mass spectrometry methods for large scale detection, identification and quantification of metabolites in body fluids. This has allowed systematic studies of the metabolic fingerprints that biological processes leave behind, and has become the research field of metabolomics. As a metabolic profiling technique, NMR is at its best when its unbiased nature, linearity and reproducibility are exploited in well-controlled nutritional intervention and cross-sectional population screening studies. Although its sensitivity is less good than that of mass spectrometry, NMR has maintained a strong position in metabolomics through implementation of standardisation protocols, hyphenation with mass spectrometry and chromatographic techniques, accurate quantification and spectral deconvolution approaches, and high-throughput automation. Thus, NMR-based metabolomics has contributed uniquely to new insights into dietary exposure, in particular by unravelling the metabolic fates of phytochemicals and the discovery of dietary intake markers. NMR profiling has also contributed to the understanding of the subtle effects of diet on central metabolism and lipoprotein metabolism. In order to hold its ground in nutritional metabolomics, NMR will need to step up its performance in sensitivity and resolution; the most promising routes forward are the analytical use of dynamic nuclear polarisation and developments in microcoil construction and automated fractionation.