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Cellular and Molecular Biology Letters

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Volume 16, Issue 2 (Jun 2011)

GIDMP: Good protein-protein interaction data metamining practice

Dariusz Plewczynski / Tomas Klingström
Published Online: 2011-03-26 | DOI: https://doi.org/10.2478/s11658-011-0004-1

Abstract

Studying the interactome is one of the exciting frontiers of proteomics, as shown lately at the recent bioinformatics conferences (for example ISMB 2010, or ECCB 2010). Distribution of data is facilitated by a large number of databases. Metamining databases have been created in order to allow researchers access to several databases in one search, but there are serious difficulties for end users to evaluate the metamining effort. Therefore we suggest a new standard, “Good Interaction Data Metamining Practice” (GIDMP), which could be easily automated and requires only very minor inclusion of statistical data on each database homepage. Widespread adoption of the GIDMP standard would provide users with: a standardized way to evaluate the statistics provided by each metamining database, thus enhancing the end-user experiencea stable contact point for each database, allowing the smooth transition of statisticsa fully automated system, enhancing time- and cost-effectiveness.

The proposed information can be presented as a few hidden lines of text on the source database www page, and a constantly updated table for a metamining database included in the source/credits web page.

Keywords: Proteins; Interactome; Pathways; Signaling; Metamining; Literature curation; Protein-protein interaction; Bioinformatics; Systems biology

  • [1] Prieto, C. and De Las Rivas, J. APID: Agile Protein Interaction DataAnalyzer. Nucleic Acids Res. 34 (2006) W298–302. http://dx.doi.org/10.1093/nar/gkl128CrossrefGoogle Scholar

  • [2] Kamburov, A., Wierling, C., Lehrach, H. and Herwig, R. ConsensusPathDB-a database for integrating human functional interaction networks. Nucleic Acids Res. 37 (2009) D623–D628. http://dx.doi.org/10.1093/nar/gkn698Web of ScienceCrossrefGoogle Scholar

  • [3] Blankenburg, H., Finn, R.D., Prlić, A., Jenkinson, A.M., Ramírez, F., Emig, D., Schelhorn, S.E., Büch, J., Lengauer, T. and Albrecht, M. DASMI: exchanging, annotating and assessing molecular interaction data. Bioinformatics 25 (2009) 1321–1328. http://dx.doi.org/10.1093/bioinformatics/btp142CrossrefWeb of ScienceGoogle Scholar

  • [4] Jayapandian, M., Chapman, A., Tarcea, V.G., Yu, C., Elkiss, A., Ianni, A., Liu, B., Nandi, A., Santos, C., Andrews, P., Athey, B., States, D. and Jagadish, H.V. Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together. Nucleic Acids Res. 35 (2007) D566–D571. http://dx.doi.org/10.1093/nar/gkl859Web of ScienceCrossrefGoogle Scholar

  • [5] http://www.pathwaycommons.org/ PubMedGoogle Scholar

  • [6] Chaurasia, G., Iqbal, Y., Hanig, C., Herzel, H., Wanker, E.E. and Futschik, M.E. UniHI: an entry gateway to the human protein interactome. Nucleic Acids Res. 35 (2007) D590–D594. http://dx.doi.org/10.1093/nar/gkl817CrossrefWeb of ScienceGoogle Scholar

  • [7] Kerrien, S., Alam-Faruque, Y., Aranda, B., Bancarz, I., Bridge, A., Derow, C., Dimmer, E., Feuermann, M., Friedrichsen, A., Huntley, R., Kohler, C., Khadake, J., Leroy, C., Liban, A., Lieftink, C., Montecchi-Palazzi, L., Orchard, S., Risse, J., Robbe, K., Roechert, B., Thorneycroft, D., Zhang, Y., Apweiler, R. and Hermjakob, H. IntAct-open source resource for molecular interaction data. Nucleic Acids Res. 35 (2007) D561–D565. http://dx.doi.org/10.1093/nar/gkl958CrossrefWeb of ScienceGoogle Scholar

  • [8] Breitkreutz, B.J., Stark, C., Reguly, T., Boucher, L., Breitkreutz, A., Livstone, M., Oughtred, R., Lackner, D.H., Bahler, J., Wood, V., Dolinski, K. and Tyers, M. The BioGRID Interaction Database: 2008 update. Nucleic Acids Res. 36 (2008) D637–D640. http://dx.doi.org/10.1093/nar/gkm1001CrossrefGoogle Scholar

  • [9] Plewczynski, D. and Ginalski, K. The interactome: Predicting the proteinprotein interactions in cells. Cell. Mol. Biol. Lett. 14 (2009) 1–22. http://dx.doi.org/10.2478/s11658-008-0024-7Web of ScienceCrossrefGoogle Scholar

  • [10] Plewczynski, D. Brainstorming: weighted voting prediction of inhibitors for protein targets. J. Mol. Model. (2010) in press. Web of ScienceGoogle Scholar

  • [11] Klingström, T. and Plewczynski, D. Protein-protein interaction and pathway databases, a graphical review. Brief. Bioinform. (2010) in press, DOI: 10.1093/bib/bbq064. CrossrefWeb of ScienceGoogle Scholar

About the article

Published Online: 2011-03-26

Published in Print: 2011-06-01


Citation Information: Cellular and Molecular Biology Letters, ISSN (Online) 1689-1392, DOI: https://doi.org/10.2478/s11658-011-0004-1.

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© 2011 Versita Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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