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OJBTM
Online
Journal of Bioinformatics ©
Volume 16
(2): 226-246, 2015.
Biclustering of tuberculosis
microarray data.
Surabhi Pradhan and C.K. Verma
Department of Mathematics, Bioinformatics
and Computer Applications, Maulana Azad National
Institute of Technology, Bhopal, India
ABSTRACT
Pradhan S, Verma CK., Biclustering of
tuberculosis microarray data, Onl J Bioinform., 16 (2): 226-246,
2015. With complete genome sequence of bacteria it is now possible to use
microarray data for analysis of expressed genes. Biclustering
has not been applied to tuberculosis for discovering similar patterns of gene
expression across different samples. A biclustering method
for discovery of co-expressed and correlated genes in tuberculosis is described.
Cheng and Church (CC), Order Preserving SubMatrices
(OPSM), BiMax algorithm and XMOTIF algorithms were applied
to discover gene biclusters. The CC algorithm
generates large biclusters compared to other
algorithms, but often yields gene groups which have unchanged expression and
thus may not reveal interesting co-regulation patterns. The OPSM algorithm yields less biclusters but reveals
functionally enriched genes and provide more information required for
study of biological pathways. The BiMax yields very useful
patterns compared to the other algorithms as it represents the gene groups
which are either upregulated or down-regulated in specific conditions. Results generated correlated, order preserving,
up-regulated, down-regulated and conserved genes of Mycobacterium
Tuberculosis.
Keywords: Tuberculosis, microarray data, biclustering methods, co-expressed genes.