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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Metabolites |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-1989/9/7/144 |
| _version_ | 1818081909563981824 |
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| author | Madeleine Ernst Kyo Bin Kang Andrés Mauricio Caraballo-Rodríguez Louis-Felix Nothias Joe Wandy Christopher Chen Mingxun Wang Simon Rogers Marnix H. Medema Pieter C. Dorrestein Justin J.J. van der Hooft |
| author_facet | Madeleine Ernst Kyo Bin Kang Andrés Mauricio Caraballo-Rodríguez Louis-Felix Nothias Joe Wandy Christopher Chen Mingxun Wang Simon Rogers Marnix H. Medema Pieter C. Dorrestein Justin J.J. van der Hooft |
| author_sort | Madeleine Ernst |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T19:13:42Z |
| format | Article |
| id | doaj.art-c586728bbde3415caa5ab488ee229c58 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-12-10T19:13:42Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-c586728bbde3415caa5ab488ee229c582022-12-22T01:36:39ZengMDPI AGMetabolites2218-19892019-07-019714410.3390/metabo9070144metabo9070144MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation ToolsMadeleine Ernst0Kyo Bin Kang1Andrés Mauricio Caraballo-Rodríguez2Louis-Felix Nothias3Joe Wandy4Christopher Chen5Mingxun Wang6Simon Rogers7Marnix H. Medema8Pieter C. Dorrestein9Justin J.J. van der Hooft10Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USACollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USACollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USACollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USAGlasgow Polyomics, University of Glasgow, Glasgow G12 8QQ, UKCollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USACollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USASchool of Computing Science, University of Glasgow, Glasgow G12 8QQ, UKBioinformatics Group, Department of Plant Sciences, Wageningen University, 6708 PB Wageningen, The NetherlandsCollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USACollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USAMetabolomics 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.https://www.mdpi.com/2218-1989/9/7/144chemical classificationin silico workflowsmetabolite annotationmetabolite identificationmetabolome miningmolecular familiesnetworkingsubstructures |
| spellingShingle | Madeleine Ernst Kyo Bin Kang Andrés Mauricio Caraballo-Rodríguez Louis-Felix Nothias Joe Wandy Christopher Chen Mingxun Wang Simon Rogers Marnix H. Medema Pieter C. Dorrestein Justin J.J. van der Hooft MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools Metabolites chemical classification in silico workflows metabolite annotation metabolite identification metabolome mining molecular families networking substructures |
| title | MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools |
| title_full | MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools |
| title_fullStr | MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools |
| title_full_unstemmed | MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools |
| title_short | MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools |
| title_sort | molnetenhancer enhanced molecular networks by integrating metabolome mining and annotation tools |
| topic | chemical classification in silico workflows metabolite annotation metabolite identification metabolome mining molecular families networking substructures |
| url | https://www.mdpi.com/2218-1989/9/7/144 |
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