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

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. Moreover, the method allows to locate regions in the sequence undergoing frameshift. This algorithm turns out to be efficient when applied on simulated dataset. Application of this framework to two haplotypes of the Plasmodium falciparum datasets exhibit different mutation profile to develop similar chloroquine resistance. We investigate the β-globin genes found in pygmy and common chimpanzee to identify mutations of all sort distinguishing the two species. Additionally, in Alzheimer datasets, our method meticulously identifies true variations in the related genes.