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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.
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