7th International Conference on Practical Applications of Computational Biology & Bioinformatics pp 145-153 | Cite as
A Workflow for the Application of Biclustering to Mass Spectrometry Data
Conference paper
Abstract
Biclustering techniques have been successfully applied to analyze microarray data and they begin to be applied to the analysis of mass spectrometry data, a high-throughput technology for proteomic data analysis which has been an active research area during the last years. In this work, we propose a novel workflow to the application of biclustering to MALDI-TOF mass spectrometry data, supported by a software desktop application which covering all of its stages. We evaluate the adequacy of applying biclustering to analyze mass spectrometry by comparing between biclustering and hierarchical clustering over two real datasets. Results are promising since they revealed the ability of these techniques to extract useful information, opening a door to further works.
Keywords
biclustering mass spectrometry BiMS BiBit BimaxPreview
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References
- 1.Roy, P., Truntzer, C., Maucort-Boulch, D., Jouve, T., Molinari, N.: Protein mass spectra data analysis for clinical biomarker discovery: A global review. Briefings Bioinf. 12(2), 176–186 (2011)CrossRefGoogle Scholar
- 2.Tibshirani, R., Hastie, T., Narasimhan, B., Soltys, S., Shi, G., Koong, A., Le, Q.T.: Sample classification from protein mass spectrometry, by ’peak probability contrasts”. Bioinformatics 20(17), 3034–3044 (2004)CrossRefGoogle Scholar
- 3.Diamandis, E.: Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: Opportunities and potential limitations. Expert Syst. Appl. 3(4), 367–378 (2004)Google Scholar
- 4.Yang, P., Zhang, Z., Zhou, B.B., Zomaya, A.Y.: A clustering based hybrid system for bio-marker selection and sample classification of mass spectrometry data. Neurocomputing 73(13-15), 2317–2331 (2010)CrossRefGoogle Scholar
- 5.McDonald, R., Skipp, P., Bennell, J., Potts, C., Thomas, L., O’Connor, C.D.: Mining whole-sample mass spectrometry proteomics data for biomarkers – An overview. Expert Syst. Appl. 36(3), 5333–5340 (2009)CrossRefGoogle Scholar
- 6.Choi, H., Kim, S., Gingras, A.C., Nesvizhskii, A.: Analysis of protein complexes through model-based biclustering of label-free quantitative AP-MS data. Mol. Syst. Biol. 6, 385 (2010)CrossRefGoogle Scholar
- 7.Coombes, K.R., Baggerlyand, K.A., Morris, J.S.: Pre-Processing Mass Spectrometry Data. In: Dubitzky, M., Granzow, M., Berrar, D. (eds.) Fundamentals of Data Mining in Genomics and Proteomics. Kluwer, Boston (2007)Google Scholar
- 8.Eidhammer, I., Flikka, K., Martens, L., Mikalsen, S.: Computational Methods for Mass Spectrometry Proteomics. Jon Wiley & Sons, Ltd., England (2008)Google Scholar
- 9.Armananzas, R., Saeys, Y., Inza, I., Garcia-Torres, M., Bielza, C., van de Peer, Y., Larranaga, P.: Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms. IEEE/ACM Trans. Comput. Biol. Bioinf. 8(3), 760–774 (2011)CrossRefGoogle Scholar
- 10.Barla, A., Jurman, G., Riccadonna, S., Merler, S., Chierici, M., Furlanello, C.: Machine learning methods for predictive proteomics. Briefings Bioinf. 9(2), 119–128 (2008)CrossRefGoogle Scholar
- 11.Yang, C., He, Z., Yu, W.: Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis. BMC Bioinf. 10, 4 (2009)CrossRefGoogle Scholar
- 12.Du, P., Kibbe, W.A., Lin, S.M.: Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics 22(17), 2059–2065 (2006)CrossRefGoogle Scholar
- 13.Gibb, S., Strimmer, K.: MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28(17), 2270–2271 (2012)CrossRefGoogle Scholar
- 14.Madeira, S.C., Oliveira, A.L.: Biclustering Algorithms for Biological Data Analysis: A Survey. IEEE/ACM Trans. Comput. Biol. Bioinf. I(I), 24–45 (2004)CrossRefGoogle Scholar
- 15.Verma, N.K., Meena, S., Bajpai, S., Singh, A., Nagrare, A., Cui, Y.: A Comparison of Biclus-tering Algorithms. In: Proceedings of the Int. Conf. Syst. Med. Biol. (ICSMB 2010), pp. 90–97 (2010)Google Scholar
- 16.Prelić, A., Bleuler, S., Zimmermann, P., Wille, A., Bühlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)CrossRefGoogle Scholar
- 17.Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., Zitzler, E.: BicAT: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)CrossRefGoogle Scholar
- 18.Rodriguez-Baena, D.S., Perez-Pulido, A.J., Aguilar-Ruiz, J.S.: A biclustering algorithm for extracting bit-patterns from binary datasets. Bioinformatics 27(19), 2738–2745 (2001)CrossRefGoogle Scholar
- 19.López-Cortés, R., Oliveira, E., Núñez, C., Lodeiro, C., Páez de la Cadena, M., Fdez-Riverola, F., López-Fernández, H., Reboiro-Jato, M., Glez-Peña, D., Capelo, J.L., Santos, H.M.: Fast human serum profiling through chemical depletion coupled to gold-nanoparticle-assisted protein separation. Talanta 100, 239–245 (2012)CrossRefGoogle Scholar
- 20.Nunes-Miranda, J.D., Santos, H.M., Reboiro-Jato, M., Fdez-Riverola, F., Igrejas, G., Lodeiro, C., Capelo, J.L.: Direct matrix assisted laser desorption ionization mass spectrometry-based analysis of wine as a powerful tool for classification purposes. Talanta 91, 72–76 (2012)CrossRefGoogle Scholar
- 21.Glez-Peña, D., Reboiro-Jato, M., Maia, P., Díaz, F., Fdez-Riverola, F.: AIBench: a rapid application development framework for translational research in biomedicine. Comput. Meth. Prog. Bio. 98, 191–203 (2010)CrossRefGoogle Scholar
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