A novel approach for the identification of single nucleotide polymorphisms.

Abstract

Human Genome Project (HGP), Genome Wide Association Studies (GWAS) and The Cancer Genome Atlas (TCGA) are some of the remarkable research endeavors that generated massive amounts of information about Single Nucleotide Polymorphisms (SNPs) and other genetic variations, providing valuable insights for understanding the association of SNPs with diseases. It enables early diagnosis, prevention, and treatment planning for diseases. In this study, a novel approach is proposed for the identification of SNPs. This approach consists of two techniques: technique I introduces a modified matching strategy for chosen matching algorithms and technique II combines the Divide-and-Conquer technique with technique 1. Performance evaluation of the proposed techniques is performed using performance metrics such as Precision, Recall, F-measure, Execution Time, and Resource Utilization (including CPU Utilization and RAM Usage). The proposed techniques overcome most of the research gaps and shortcomings of the existing techniques.