What can metabolomics learn from genomics and proteomics?
After nearly a decade, metabolomics has begun to acquire some credence in the scientific community although its acceptance cannot be compared with that of its forerunners, genomics and proteomics. The legitimization of metabolomics as a valid scientific entity depends on the size of the research community it influences. By far the most effective medium for inoculation is the web infrastructure: public servers that accommodate experimental data, simple formats and guidelines for their interpretation, and connectivity between data and tools for analysis. When these elements satisfy the condition to initiate a social epidemic, metabolomics will be accepted as a fundamental data-driven science that can unite hitherto independently conducted research disciplines.
Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Japan. arita@k.u-tokyo.ac.jp
Masanori
M
Arita
D022126:Access to Information; D000818:Animals; D001426:Bacterial Proteins / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D023281:Genomics / Q000379:methods*; D006801:Humans; D020407:Internet; D055432:Metabolomics / Q000379:methods* / Q000639:trends*; D010940:Plant Proteins / Q000378:metabolism; D011381:Programming Languages; D040901:Proteomics / Q000379:methods*
D001426:Bacterial Proteins; D010940:Plant Proteins