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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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CEU Mass Mediator 3.0: A Metabolite Annotation Tool.

Alberto Gil-de-la-Fuente1,2, Joanna Godzien2, Sergio Saugar1

  • 1Department of Information Technology, Escuela Politécnica Superior , Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe , Boadilla del Monte, Madrid 28668 , Spain.

Journal of Proteome Research
|December 22, 2018
PubMed
Summary

CEU Mass Mediator (CMM) version 3.0 enhances metabolite identification with an expanded knowledge base and new expert system services. This advanced tool supports researchers in annotating and identifying metabolites, improving metabolomic data analysis.

Keywords:
RESTannotationdatabasesidentificationknowledge representationmass spectrometrymetabolomicssoftware toolweb services

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Computational Biology

Background:

  • CEU Mass Mediator (CMM) has evolved from a simple database query tool (CMM 1.0) to an integrated expert system (CMM 2.0).
  • Continuous development has focused on expanding its knowledge base and services for metabolite annotation and identification.

Purpose of the Study:

  • To present the novel functionalities incorporated into version 3.0 of the CEU Mass Mediator (CMM).
  • To highlight improvements in metabolite data integration, annotation, and identification support for researchers.

Main Methods:

  • Expansion of the expert system's knowledge base and integration of an in-house oxidized lipid library.
  • Implementation of new services: oxidized glycerophosphocholine annotation, MS2 spectral comparison, and spectral quality assessment.
  • Development of a RESTful API for enhanced collaboration and integration with external metabolomic platforms.

Main Results:

  • Increased metabolite entries to 332,665 experimental and 681,198 predicted.
  • Incorporation of new taxonomy and ontology metadata.
  • Successful integration of the RESTful API into the Human Metabolome Database (HMDB).

Conclusions:

  • CMM 3.0 offers significantly enhanced capabilities for metabolite annotation and identification.
  • The new features and API integration improve the tool's utility and interoperability within the metabolomics community.
  • CMM 3.0 represents a substantial advancement in supporting metabolomic research.