A fast and accurate SNP detection method on the cloud platform

Single nucleotide polymorphisms (SNPs) provide abundant information about genetic variation, and it is crucial for further genetic analysis. The detection and annotation of SNPs from next-generation sequencing (NGS) data play an important role on the manifestation of phenotypic events. Various methods have been developed for single-nucleotide polymorphisms from next-generation sequencing data, however, most of these methods for identifying single-nucleotide polymorphisms are slow to detect SNPs and need highly resource share. A fast and accurate single-nucleotide polymorphism detection program based on the logistic regression model and Bayesian framework is proposed. In order to evaluate the performance of this program, the time for identifying SNPs has compared with other programs on the cloud platform. The result shows that the proposed method can save nearly half of the time in the same operating conditions and data.