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 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




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.