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
- Bimax
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
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)
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)
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)
Eidhammer, I., Flikka, K., Martens, L., Mikalsen, S.: Computational Methods for Mass Spectrometry Proteomics. Jon Wiley & Sons, Ltd., England (2008)
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)
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)
Yang, C., He, Z., Yu, W.: Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis. BMC Bioinf. 10, 4 (2009)
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)
Gibb, S., Strimmer, K.: MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28(17), 2270–2271 (2012)
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)
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)
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)
Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., Zitzler, E.: BicAT: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
López-Fernández, H. et al. (2013). A Workflow for the Application of Biclustering to Mass Spectrometry Data. In: Mohamad, M., Nanni, L., Rocha, M., Fdez-Riverola, F. (eds) 7th International Conference on Practical Applications of Computational Biology & Bioinformatics. Advances in Intelligent Systems and Computing, vol 222. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00578-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-00578-2_19
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00577-5
Online ISBN: 978-3-319-00578-2
eBook Packages: EngineeringEngineering (R0)