Consensus and conflict cards for metabolic pathway databases

BMC Systems Biology

Perry D Moerland¹  Antoine HC van Kampen²  Trebor Rengaw¹  Ines Thiele⁴  Morris A Swertz¹  Miranda D Stobbe³ 

[1] Netherlands Bioinformatics Centre, Geert Grooteplein 28, Nijmegen 6525 GA, the Netherlands;Netherlands Consortium for Systems Biology, University of Amsterdam, P.O. Box 94215, Amsterdam 1090 GE, the Netherlands;Current address: Institute for Research in Biomedicine (IRB Barcelona), c/Baldiri Reixac 10, Barcelona 08028, Spain;Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, Luxembourg

关键词: Pathway database;    Human;    Community support;    Consensus;    Metabolic network;   

Others  :  1142725
DOI  :  10.1186/1752-0509-7-50

 received in 2012-10-23, accepted in 2013-06-20,  发布年份 2013

【 摘 要 】

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 C₂Cards^Human, 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.

【 授权许可】

   
2013 Stobbe et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

【 参考文献 】

• [1]Karp PD, Caspi R: A survey of metabolic databases emphasizing the MetaCyc family. Arch Toxicol 2011, 85:1015-1033.
• [2]Oberhardt MA, Palsson BØ, Papin JA: Applications of genome-scale metabolic reconstructions. Mol Syst Biol 2009, 5:320.
• [3]Herrgård MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Blüthgen N, Borger S, Costenoble R, Heinemann M, et al.: A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnol 2008, 26:1155-1160.
• [4]Stobbe MD, Houten SM, Jansen GA, van Kampen AHC, Moerland PD: Critical assessment of human metabolic pathway databases: a stepping stone for future integration. BMC Syst Biol 2011, 5:165. BioMed Central Full Text
• [5]Mo ML, Palsson BØ: Understanding human metabolic physiology: a genome-to-systems approach. Trends Biotechnol 2009, 27:37-44.
• [6]Thiele I, Palsson BØ: Reconstruction annotation jamborees: a community approach to systems biology. Mol Syst Biol 2010, 6:361.
• [7]Thiele I, Hyduke DR, Steeb B, Fankam G, Allen DK, Bazzani S, Charusanti P, Chen FC, Fleming RM, Hsiung CA, et al.: A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2. BMC Syst Biol 2011, 5:8. BioMed Central Full Text
• [8]Wittig U, De Beuckelaer A: Analysis and comparison of metabolic pathway databases. Brief Bioinform 2001, 2:126-142.
• [9]Radrich K, Tsuruoka Y, Dobson P, Gevorgyan A, Swainston N, Baart G, Schwartz JM: Integration of metabolic databases for the reconstruction of genome-scale metabolic networks. BMC Syst Biol 2010, 4:114. BioMed Central Full Text
• [10]Chindelevitch L, Stanley S, Hung D, Regev A, Berger B: MetaMerge: scaling up genome-scale metabolic reconstructions, with application to Mycobacterium tuberculosis. Genome Biol 2012, 13:R6. BioMed Central Full Text
• [11]Schellenberger J, Park JO, Conrad TM, Palsson BØ: BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinforma 2010, 11:213. BioMed Central Full Text
• [12]Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BØ: Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA 2007, 104:1777-1782.
• [13]Hao T, Ma HW, Zhao XM, Goryanin I: Compartmentalization of the Edinburgh Human Metabolic Network. BMC Bioinforma 2010, 11:393. BioMed Central Full Text
• [14]Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD: Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 2004, 6:R2. BioMed Central Full Text
• [15]Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucl Acids Res 2012, 40:D109-D114.
• [16]Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, Caudy M, Garapati P, Gopinath G, Jassal B, et al.: Reactome: a database of reactions, pathways and biological processes. Nucl Acids Res 2011, 39:D691-D697.
• [17]Orth JD, Palsson BØ: Systematizing the generation of missing metabolic knowledge. Biotechnol Bioeng 2010, 107:403-412.
• [18]Elbers CC, van Eijk KR, Franke L, Mulder F, van der Schouw YT, Wijmenga C, Onland-Moret NC: Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol 2009, 33:419-431.
• [19]Willemoës M: Competition between ammonia derived from internal glutamine hydrolysis and hydroxylamine present in the solution for incorporation into UTP as catalysed by Lactococcus lactis CTP synthase. Arch Biochem Biophys 2004, 424:105-111.
• [20]Kassel KM, Au DR, Higgins MJ, Hines M, Graves LM: Regulation of Human Cytidine Triphosphate Synthetase 2 by Phosphorylation. J Biol Chem 2010, 285:33727-33736.
• [21]Bierau J, Lindhout M, Bakker JA: Pharmacogenetic significance of inosine triphosphatase. Pharmacogenomics 2007, 8:1221-1228.
• [22]Johansson M: Identification of a novel human uridine phosphorylase. Biochem Biophys Res Commun 2003, 307:41-46.
• [23]el Kouni MH, el Kouni MM, Naguib FNM: Differences in Activities and Substrate Specificity of Human and Murine Pyrimidine Nucleoside Phosphorylases: Implications for Chemotherapy with 5-Fluoropyrimidines. Cancer Res 1993, 53:3687-3693.
• [24]Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H, Herwig R: ConsensusPathDB: toward a more complete picture of cell biology. Nucl Acids Res 2011, 39:D712-D717.
• [25]Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C: Pathway commons, a web resource for biological pathway data. Nucl Acids Res 2011, 39:D685-D690.
• [26]Östlund G, Schmitt T, Forslund K, Köstler T, Messina DN, Roopra S, Frings O, Sonnhammer ELL: InParanoid 7: new algorithms and tools for eukaryotic orthology analysis. Nucl Acids Res 2010, 38:D196-D203.
• [27]Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD, et al.: A community-driven global reconstruction of human metabolism. Nat Biotech 2013, 31:419-425.
• [28]Groth P, Gibson A, Velterop J: The anatomy of a nanopublication. Information Services and Use 2010, 30:51-56.
• [29]Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C: WikiPathways: pathway editing for the people. PLoS Biol 2008, 6:e184.
• [30]Green ML, Karp PD: Genome annotation errors in pathway databases due to semantic ambiguity in partial EC numbers. Nucleic Acids Res 2005, 33:4035-4039.
• [31]Swertz MA, Dijkstra M, Adamusiak T, van der Velde JK, Kanterakis A, Roos TE, Lops J, Thorisson GA, Arends D, Byelas G, et al.: The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button. BMC Bioinforma 2010, 11:S12.