- Kyo Bin Kang (1)
- Pieter C. Dorrestein (1)
- Christopher Chen (1)
- Gabriela Cofré-Bravo (1)
- Alejandra Engler (1)
- Madeleine Ernst (1)
- Louis Felix Nothias (1)
- Marnix H. Medema (1)
- Justin J.J. Hooft van der (1)
- Laurens Klerkx (1)
- Andrés Mauricio Caraballo-Rodríguez (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.
Combinations of bonding, bridging, and linking social capital for farm innovation: How farmers configure different support networks
Cofré-Bravo, Gabriela ; Klerkx, Laurens ; Engler, Alejandra - \ 2019
Journal of Rural Studies 69 (2019). - ISSN 0743-0167 - p. 53 - 64.
Advisory systems - Agricultural innovation systems - Chile - Farm innovation - Micro AKIS - Networking - Organizational ambidexterity - Social capital - Technology adoption
On-farm agricultural innovation through incorporation of new technologies and practices requires access to resources such as knowledge, financial resources, training, and even emotional support, all of which require the support of different actors such as peers, advisors, and researchers. The literature has explored the support networks that farmers use and the overall importance ranking of different support actors, but it has not looked in detail at how these networks may differ for different farmers. This study fills this gap by looking at farmer support network configurations through the lens of the social capital available to them in such configurations. Using a Chilean fruit-farmer case, we examine how different types of social capital (bonding, bridging, and linking) are used to achieve what has been called ‘ambidexterity’. Ambidexterity implies both that open networks (based on linking and bridging social capital) are used to explore and access new knowledge and resources, and that closed networks (based on bonding social capital) are used to successfully implement and exploit new technologies and practices. Our findings show that farmers use all types of social capital – bonding, bridging, and linking – in their support networks, but that they have different configurations, five in this study. These configurations are based on personal motivations, innovation objectives, and resource endowments. Despite the different network configurations and types of social capital – which may be more balanced or less balanced in light of ambidexterity – farmers may achieve the same ambitions and type of innovations. A main theoretical implication is that the configuration of support networks is thus not a one-size-fits-all where each farmer's ranking of support actors for on-farm innovation is the same. This nuances earlier work and calls for more attention to a better understanding of how each support network configuration responds to a certain logic, and hence cannot be identified as superior or inferior.