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Tools and databases of the KOMICS web portal for preprocessing, mining, and dissemination of metabolomics data.

Nozomu SakuraiTakeshi AraMitsuo EnomotoTakeshi MotegiYoshihiko MorishitaAtsushi KurabayashiYoko IijimaYoshiyuki OgataDaisuke NakajimaHideyuki SuzukiDaisuke Shibata
Published in: BioMed research international (2014)
A metabolome--the collection of comprehensive quantitative data on metabolites in an organism--has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal), where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study on Jatropha curcas is presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data.
Keyphrases
  • big data
  • mass spectrometry
  • machine learning
  • electronic health record
  • artificial intelligence
  • ms ms
  • emergency department
  • data analysis
  • high resolution
  • high throughput
  • quality improvement
  • medical students
  • rna seq