期刊
METABOLITES
卷 9, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/metabo9070144
关键词
chemical classification; in silico workflows; metabolite annotation; metabolite identification; metabolome mining; molecular families; networking; substructures
资金
- ASDI eScience grant from the Netherlands eScience Center-NLeSC [ASDI.2017.030]
- National Research Foundation of Korea (NRF) - Korea government (MIST) [2019R1F1A1058068]
- USA National Science Foundation [IOS-1656481]
- EPSRC [EP/R018634/1]
- BBSRC [BB/R022054/1]
- BBSRC [BB/R022054/1] Funding Source: UKRI
- EPSRC [EP/R018634/1] Funding Source: UKRI
- National Research Foundation of Korea [2019R1F1A1058068] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
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