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Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm
January-March 2010 (vol. 7 no. 1)
pp. 153-165
ASCII Text
x 
 
Sara C. Madeira, Miguel C. Teixeira, Isabel Sá-Correia, Arlindo L. Oliveira, "Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 1, pp. 153-165, January-March, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2008.34,
author = {Sara C. Madeira and Miguel C. Teixeira and Isabel Sá-Correia and Arlindo L. Oliveira},
title = {Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {7},
number = {1},
issn = {1545-5963},
year = {2010},
pages = {153-165},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.34},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm
IS - 1
SN - 1545-5963
SP153
EP165
EPD - 153-165
A1 - Sara C. Madeira,
A1 - Miguel C. Teixeira,
A1 - Isabel Sá-Correia,
A1 - Arlindo L. Oliveira,
PY - 2010
KW - Biclustering
KW - time series gene expression data
KW - expression patterns
KW - regulatory modules.
VL - 7
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
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.

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Index Terms:
Biclustering, time series gene expression data, expression patterns, regulatory modules.
Citation:
Sara C. Madeira, Miguel C. Teixeira, Isabel Sá-Correia, Arlindo L. Oliveira, "Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 1, pp. 153-165, Jan.-March 2010, doi:10.1109/TCBB.2008.34
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