CPMFD: An algorithm for Classification of Point Mutations together with Frameshift Determination in related mRNA sequences

Abstract

Mutations are responsible for the genetic origin of various diseases. Existing techniques for mutation identification often fails to detect the full spectrum of mutations in complex genomes hindering progress in diagnosis, treatment and prevention of diseases. Here we propose an algorithm to identify the location and type of mutation occurring in a mutated string with respect to a reference mRNA sequence. In addition to identifying insertion and deletion, by constructing suitable rational combinations of the prime numbers, our algorithm is able to classify point mutations in a novel way by distinguishing missense mutation from silent mutation. Amino acid transformation at each missense mutation site is identified. Moreover, the method allows to locate regions in the sequence undergoing frameshift. It turns out to be efficient when applied on sample dataset. Application of this framework to two haplotypes of the Plasmodium falciparum datasets exhibits different mutation profile to develop similar chloroquine resistance. Despite the overwhelming similarity between the $\beta$-globin genes of pygmy and common chimpanzees, our algorithm is able to pinpoint the minute details of the mutations occurring in them differentiating the two species. Additionally, in Alzheimer datasets, the method meticulously identifies true variations in related genes.