The Community for Technology Leaders
Green Image
Issue No. 01 - January-March (2010 vol. 7)
ISSN: 1545-5963
pp: 153-165
Sara C. Madeira , Universidade da Beira Interior, Covilhã, KDBIO Group, INESC-ID, Lisbon, and Lisbon Technical University, Lisboa
Miguel C. Teixeira , Lisbon Technical University, Lisboa
Isabel Sá-Correia , Lisbon Technical University, Lisboa
Arlindo L. Oliveira , KDBIO Group, INESC-ID, Libson and Lisbon Technical University, Lisboa
Although most biclustering formulations are NP-hard, in time series expression data analysis, it is reasonable to restrict the problem to the identification of maximal biclusters with contiguous columns, which correspond to coherent expression patterns shared by a group of genes in consecutive time points. This restriction leads to a tractable problem. We propose an algorithm that finds and reports all maximal contiguous column coherent biclusters in time linear in the size of the expression matrix. The linear time complexity of CCC-Biclustering relies on the use of a discretized matrix and efficient string processing techniques based on suffix trees. We also propose a method for ranking biclusters based on their statistical significance and a methodology for filtering highly overlapping and, therefore, redundant biclusters. We report results in synthetic and real data showing the effectiveness of the approach and its relevance in the discovery of regulatory modules. Results obtained using the transcriptomic expression patterns occurring in Saccharomyces cerevisiae in response to heat stress show not only the ability of the proposed methodology to extract relevant information compatible with documented biological knowledge but also the utility of using this algorithm in the study of other environmental stresses and of regulatory modules in general.
Biclustering, time series gene expression data, expression patterns, regulatory modules.

M. C. Teixeira, S. C. Madeira, I. Sá-Correia and A. L. Oliveira, "Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. , pp. 153-165, 2008.
199 ms
(Ver 3.3 (11022016))