Telomerase, an enzyme which plays a key role in the maintenance of the ends of chromosome in eukaryotes have been found activated in many cancer cells. Telomerase is an enzyme that adds specific DNA sequence repeats i.e., “TTAGGG” in all vertebrates to the telomere region and thereby maintains the telomere length along with other telomere binding proteins. A group of 6 proteins make up the Shelterin complex which is responsible for telomere capping. The objective of this work is to detect the homologues with the query sequences, identify evolutionarily conserved regions in the query, find patterns of concerted evolution by detecting conserved domain architectures in the query and subject proteins, phylogenetic relationships amongst the query and subject proteins were analyzed to match them with the concerted evolution pattern and Telomerase inhibitors were screened to detect the best fit molecule which could possibly act to prevent carcinogenesis.

Microarrays have become the effective, broadly used tools in biological and medical research to address a wide range of problems. Many statistical methods are available for analyzing and systematizing the complex data into meaningful information, and one of the main goals in analyzing gene expression data is the detection of samples or genes with similar expression pattern. In microarray cluster analysis, there is a challenging problem; the dimension of gene expression is much larger than the sample size. The sample based clustering is to find the phenotype structure or substructure of the samples. Currently most of research work focuses on the supervised analysis, relatively less attention has been paid to unsupervised approaches in sample based analysis which is important when domain knowledge is incomplete or hard to obtain. The standard k-means clustering algorithm is used for much practical application. But its output is quite sensitive to initial position of cluster centers and the dimension of data. In this paper, we present a new framework for unsupervised sample based clustering using informative genes for microarray data. We propose the method to find initial Centroid for k-means and we have used similarity measure to find the informative genes. The goal of our clustering approach is to perform better cluster discovery on sample with informative gene. We have applied our proposed method to cancer data sets. By comparing the results of original and new approach, it was found that the results obtained are more accurate.

RNA interference (RNAi) is a naturally occurring phenomenon of post transcriptional gene silencing and has been found to be highly conserved among multicellular organisms. 21mer effector RNAs, named small or short interfering RNAs (siRNAs) or microRNAs are produced from a precursor hairpin structure by cleavage using a ribonuclease class III enzyme – DICER and are incorporated into a multimeric protein complex, known as the RNA-induced silencing complex (RISC). One of the two small RNA strands serves as the guide and tows the RISC to a complementary RNA. After hybridization the endonucleolytic “slicer” activity of RISC cleaves the target RNA brought about this time by the cytoplasmic variant of DICER, thus preventing its translation. miRNAs, however, are capable of inhibiting translation of the targeted mRNA without bringing about its degradation (at least in mammalian cells). Another important component of the RISC complex are the Argonaute proteins which play dual roles of stabilizing the complex and also regulate the formation of the RNA – RNA duplex. The need for in silico analysis of the components of the RNA interference pathway arises from the fact that very little is known about the structural and interacting properties of these components.