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Open Access Highly Accessed Research article

Consensus and conflict cards for metabolic pathway databases

Miranda D Stobbe157, Morris A Swertz45, Ines Thiele3, Trebor Rengaw45, Antoine HC van Kampen1256 and Perry D Moerland15*

Author Affiliations

1 Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, P.O. Box 22700, Amsterdam 1100 DE, the Netherlands

2 Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, the Netherlands

3 Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, Luxembourg

4 Genomics Coordination Center, University Medical Center Groningen & University of Groningen, P.O. Box 30001, Groningen 9700 RB, the Netherlands

5 Netherlands Bioinformatics Centre, Geert Grooteplein 28, Nijmegen 6525 GA, the Netherlands

6 Netherlands Consortium for Systems Biology, University of Amsterdam, P.O. Box 94215, Amsterdam 1090 GE, the Netherlands

7 Current address: Institute for Research in Biomedicine (IRB Barcelona), c/Baldiri Reixac 10, Barcelona 08028, Spain

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BMC Systems Biology 2013, 7:50  doi:10.1186/1752-0509-7-50

Published: 26 June 2013

Abstract

Background

The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial.

Results

We introduce the concept of Consensus and Conflict Cards (C2Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C2CardsHuman, as a web application http://www.molgenis.org/c2cards webcite, covering five human pathway databases.

Conclusions

C2Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C2Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement.

Keywords:
Metabolic network; Consensus; Community support; Human; Pathway database