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      Critical assessment of human metabolic pathway databases: a stepping stone for future integration

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          Abstract

          Background

          Multiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts.

          Results

          We compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison.

          Conclusions

          Our results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor.

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          Most cited references36

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          The ENZYME database in 2000.

          A Bairoch (2000)
          The ENZYME database is a repository of information related to the nomenclature of enzymes. In recent years it has became an indispensable resource for the development of metabolic databases. The current version contains information on 3705 enzymes. It is available through the ExPASy WWW server (http://www.expasy.ch/enzyme/ ).
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            The Systems Biology Graphical Notation.

            Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
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              WikiPathways: Pathway Editing for the People

              WikiPathways provides a collaborative platform for creating, updating, and sharing pathway diagrams and serves as an example of content curation by the biology community.
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                Author and article information

                Journal
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central
                1752-0509
                2011
                14 October 2011
                : 5
                : 165
                Affiliations
                [1 ]Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE, Amsterdam, the Netherlands
                [2 ]Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands
                [3 ]Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, the Netherlands
                [4 ]Netherlands Consortium for Systems Biology, University of Amsterdam, PO Box 94215, 1090 GE, Amsterdam, the Netherlands
                [5 ]Department of Clinical Chemistry, Laboratory Genetic Metabolic Diseases, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE, Amsterdam, the Netherlands
                [6 ]Department of Pediatrics, Emma Children's Hospital, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE, Amsterdam, the Netherlands
                Article
                1752-0509-5-165
                10.1186/1752-0509-5-165
                3271347
                21999653
                5a798b08-e00d-4729-840d-681357ea4879
                Copyright ©2011 Stobbe et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 29 July 2011
                : 14 October 2011
                Categories
                Research Article

                Quantitative & Systems biology
                Quantitative & Systems biology

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