A REVIEW ON COMPUTATIONAL APPROACHES FOR IDENTIFYING NON-SYNONYMOUS MUTATION AND DNA METHYLATION IN CANCER

Abstract

Cancer is a complex disease where, genetic and epigenetic changes plays a major role. Non- synonymous mutations and DNA methylation are important factors in cancer starts and development. Non- synonymous mutations are genetic changes that cause amino acid substitutions within proteins, which frequently result in defective or carcinogenic products. DNA methylation, on the other hand, is the addition of a methyl group to cytosine residues, which affects gene expression and contributes to cancer. Understanding the underlying mechanisms and designing targeted therapeutics require identifying these molecular events in cancer genomes. The traditional methods of identifying the mutations and DNA methylation has made a great challenge in the research fields. Computational approaches have shown to be important in this attempt. They cover a wide range of approaches, from mutation variant calling procedures to methylation pattern analysis tools. These methods will make use of high-throughput sequencing data to detect and characterize non- synonymous mutations and DNA methylation changes on a genome-wide scale. In this review, we will discuss about the multi omics dataset retrievals, tools used for the identification of mutated non- synonymous mutation and the algorithms and techniques used for DNA Methylation for identifying mutated regions. The critical values for the specific results also been noted in this study. Keywords: DNA Methylation, Non-synonymous genomic mutation, cancer, bioinformatics tools, algorithms