Skip to content
BY-NC-ND 3.0 license Open Access Published by De Gruyter March 26, 2011

GIDMP: Good protein-protein interaction data metamining practice

  • Dariusz Plewczynski EMAIL logo and Tomas Klingström

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.

[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/gkl12810.1093/nar/gkl128Search in Google Scholar PubMed PubMed Central

[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/gkn69810.1093/nar/gkn698Search in Google Scholar PubMed PubMed Central

[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/btp14210.1093/bioinformatics/btp142Search in Google Scholar PubMed PubMed Central

[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/gkl85910.1093/nar/gkl859Search in Google Scholar PubMed PubMed Central

[5] http://www.pathwaycommons.org/ Search in Google 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/gkl81710.1093/nar/gkl817Search in Google Scholar PubMed PubMed Central

[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/gkl95810.1093/nar/gkl958Search in Google Scholar PubMed PubMed Central

[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/gkm100110.1093/nar/gkm1001Search in Google Scholar PubMed PubMed Central

[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/s11658-008-0024-710.2478/s11658-008-0024-7Search in Google Scholar PubMed PubMed Central

[10] Plewczynski, D. Brainstorming: weighted voting prediction of inhibitors for protein targets. J. Mol. Model. (2010) in press. 10.1007/s00894-010-0854-xSearch in Google Scholar PubMed PubMed Central

[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. 10.1093/bib/bbq064Search in Google Scholar PubMed

Published Online: 2011-3-26
Published in Print: 2011-6-1

© 2011 University of Wrocław, Poland

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Downloaded on 29.3.2024 from https://www.degruyter.com/document/doi/10.2478/s11658-011-0004-1/html
Scroll to top button