Current progress in computational metabolomics.
Being a relatively new addition to the 'omics' field, metabolomics is still evolving its own computational infrastructure and assessing its own computational needs. Due to its strong emphasis on chemical information and because of the importance of linking that chemical data to biological consequences, metabolomics must combine elements of traditional bioinformatics with traditional cheminformatics. This is a significant challenge as these two fields have evolved quite separately and require very different computational tools and skill sets. This review is intended to familiarize readers with the field of metabolomics and to outline the needs, the challenges and the recent progress being made in four areas of computational metabolomics: (i) metabolomics databases; (ii) metabolomics LIMS; (iii) spectral analysis tools for metabolomics and (iv) metabolic modeling.
Wishart
David S
DS
Department of Computing Science, Department of Biological Sciences and National Institute for Nanotechnology (NRC-NINT), University of Alberta, Edmonton Alberta, Canada T6G 2E8. david.wishart@ualberta.ca
D020650:Combinatorial Chemistry Techniques / Q000639:trends; D019295:Computational Biology / Q000639:trends*; D030562:Databases, Protein / Q000639:trends*; D020869:Gene Expression Profiling / Q000379:methods*; D008660:Metabolism; D025941:Protein Interaction Mapping / Q000639:trends*; D020543:Proteome / Q000378:metabolism*; D040901:Proteomics / Q000639:trends*
D020543:Proteome