CEU Mass Mediator 3.0: A Metabolite Annotation Tool.
Alberto Gil-de-la-FuenteJoanna GodzienSergio SaugarRodrigo García-CarmonaHasan BadranDavid Scott WishartCoral BarbasAbraham OteroPublished in: Journal of proteome research (2018)
CEU Mass Mediator (CMM, http://ceumass.eps.uspceu.es ) is an online tool that has evolved from a simple interface to query different metabolomic databases (CMM 1.0) to a tool that unifies the compounds from these databases and, using an expert system with knowledge about the experimental setup and the compounds properties, filters and scores the query results (CMM 2.0). Since this last major revision, CMM has continued to grow, expanding the knowledge base of its expert system and including new services to support researchers in the metabolite annotation and identification process. The information from external databases has been refreshed, and an in-house library with oxidized lipids not present in other sources has been added. This has increased the number of experimental metabolites up 332,665 and the number of predicted metabolites to 681,198. Furthermore, new taxonomy and ontology metadata have been included. CMM has expanded its functionalities with a service for the annotation of oxidized glycerophosphocholines, a service for spectral comparison from MS2 data, and a spectral quality-assessment service to determine the reliability of a spectrum for compound identification purposes. To facilitate the collaboration and integration of CMM with external tools and metabolomic platforms, a RESTful API has been created, and it has already been integrated into the HMDB (Human Metabolome Database). This paper will present the novel functionalities incorporated into version 3.0 of CMM.
Keyphrases
- healthcare
- mental health
- ms ms
- big data
- optical coherence tomography
- multiple sclerosis
- endothelial cells
- rna seq
- total knee arthroplasty
- primary care
- mass spectrometry
- clinical practice
- emergency department
- machine learning
- magnetic resonance imaging
- artificial intelligence
- health information
- social media
- health insurance
- drug induced
- deep learning