Publication

Abstract : Genome sequencing is one of the key areas of research that helps analyze the genome and contributes to the diagnosis of various diseases like cancer, Ebola virus, covid, etc. There are various genome sequencing techniques available for detecting the mutations and for sequencing a gene including whole-genome sequencing methods. There are also various deep learning techniques like mutations identification and disease classification. Mutation detection in the genome can also be achieved with sequence alignment techniques such as the Needleman Wunsch algorithm and smith-waterman algorithm. In this paper, a new approach of mutation identification technique has been proposed using color encoding, hamming distance, and Levenshtein distance in identifying the cancerous mutation in the gene. Research says that most of the mutations (>50%) that lead to cancer are due to the mutations in the TP53 gene which is present in chromosome 17. So, the TP53, a coding gene that is responsible for the production of P53 protein has been taken as a reference gene for the identification of cancer. A visual mutation identification technique using color encodings is also added which makes the user easily identify the mutation visually. The implementation of the sequencing techniques has been done using python programming.KeywordsMutationsTumor suppressor geneString matchingHamming distanceLevenshtein distanceColor encodings