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

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 spectrom...

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Veröffentlicht in:	Metabolites Jg. 9; H. 7; S. 144
Hauptverfasser:	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., van der Hooft, Justin J.J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 16.07.2019 MDPI
Schlagworte:	Annotations chemical classification Computer applications in silico workflows Libraries Mass spectrometry Mass spectroscopy metabolite annotation metabolite identification metabolome mining Metabolomics molecular families Natural products networking Propagation Scientific imaging Software packages substructures metabolite annotation substructures molecular families in silico workflows metabolome mining chemical classification networking metabolite identification
ISSN:	2218-1989, 2218-1989
Online-Zugang:	Volltext
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Abstract	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.
AbstractList	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. 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.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.
Author	Kang, Kyo Bin van der Hooft, Justin J.J. Medema, Marnix H. Caraballo-Rodríguez, Andrés Mauricio Chen, Christopher Ernst, Madeleine Dorrestein, Pieter C. Wandy, Joe Wang, Mingxun Rogers, Simon Nothias, Louis-Felix
AuthorAffiliation	4 Glasgow Polyomics, University of Glasgow, Glasgow G12 8QQ, UK 7 Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA 1 Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA 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 8 Center for Microbiome Innovation, 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
AuthorAffiliation_xml	– name: 3 Research Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women’s University, Seoul 04310, Korea – name: 2 Department of Congenital Disorders, Center for Newborn Screening, Statens Serum Institut, 2300 Copenhagen, Denmark – name: 4 Glasgow Polyomics, University of Glasgow, Glasgow G12 8QQ, UK – name: 8 Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA – name: 7 Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA – name: 6 Bioinformatics Group, Department of Plant Sciences, Wageningen University, 6708 PB Wageningen, The Netherlands – name: 5 School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK – name: 1 Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
Author_xml	– sequence: 1 givenname: Madeleine orcidid: 0000-0001-9530-3837 surname: Ernst fullname: Ernst, Madeleine – sequence: 2 givenname: Kyo Bin orcidid: 0000-0003-3290-1017 surname: Kang fullname: Kang, Kyo Bin – sequence: 3 givenname: Andrés Mauricio surname: Caraballo-Rodríguez fullname: Caraballo-Rodríguez, Andrés Mauricio – sequence: 4 givenname: Louis-Felix orcidid: 0000-0001-6711-6719 surname: Nothias fullname: Nothias, Louis-Felix – sequence: 5 givenname: Joe orcidid: 0000-0002-3068-4664 surname: Wandy fullname: Wandy, Joe – sequence: 6 givenname: Christopher surname: Chen fullname: Chen, Christopher – sequence: 7 givenname: Mingxun surname: Wang fullname: Wang, Mingxun – sequence: 8 givenname: Simon orcidid: 0000-0003-3578-4477 surname: Rogers fullname: Rogers, Simon – sequence: 9 givenname: Marnix H. orcidid: 0000-0002-2191-2821 surname: Medema fullname: Medema, Marnix H. – sequence: 10 givenname: Pieter C. surname: Dorrestein fullname: Dorrestein, Pieter C. – sequence: 11 givenname: Justin J.J. orcidid: 0000-0002-9340-5511 surname: van der Hooft fullname: van der Hooft, Justin J.J.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31315242$$D View this record in MEDLINE/PubMed
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Copyright	2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019 by the authors. 2019
Copyright_xml	– notice: 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2019 by the authors. 2019
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SubjectTerms	Annotations chemical classification Computer applications in silico workflows Libraries Mass spectrometry Mass spectroscopy metabolite annotation metabolite identification metabolome mining Metabolomics molecular families Natural products networking Propagation Scientific imaging Software packages substructures
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Title	MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools
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