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Online Journal of Bioinformatics©
Established 1995

ISSN  1443-2250


Volume 19(3):271-280, 2018.

Gene expression analysis of microarrays for tuberculosis drugs.

 

Anu Rastogi1,2, Pallavi Gangwar2, Ankur Mohan2, Santosh Kumar2, Abhishek Malakar2,3

 

1Amity University, Noida, 2BCS-InSilico Biology, Lucknow, U.P, 3NBFGR::National Bureau of Fish Genetic Resources, Lucknow, U.P, 3India

 

ABSTRACT

 

Rastogi A, Gangwar P, Mohan A, Kumar S, Malakar A., Gene expression analysis of microarrays  for tuberculosis drugs., 19(3):271-280, 2018.  Type 1 IFN α/β was searched on GEO profiles of NCBI. Microarray data was extracted from GEO profiles of 22,284 genes and 52 experimental samples filtered to 2008 genes and clustered by HCL and KMC in Genesis software. We found genes IFNA2 and IFNB1 whose slight differences in expression enabled us to define a range 40 to 60 with 6 clusters of disease relevance. Twenty genes of interest were identified and amino acid FASTA sequences retrieved for phylogenetic analysis. Hierarchical and k means clustering methods were performed for analysis of microarray gene expression data of Mycobacterium Tuberculosis with Genesis tool. On the basis of co-expression, some possible gene targets in Mycobacterium Tuberculosis INS- IGF2 and TNP2 were found. As both proteins arise from the same node in the phylogenetic tree we modeled and validated for docking with improved binding energy of -15.3 K/cal/mol compared to ligands provided by Drugbank.

 

Keywords: Mycobacterium tuberculosis, Microarray data Analysis, Multidrug resistance, Hierarchial and K means, Antituberculosis.

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Keywords: Mycobacterium tuberculosis, Microarray data Analysis, Multidrug resistance, Hierarchial and K means, Antituberculosis.