A Hybrid Computational Grid Architecture for Comparative Genomics

Comparative genomics provides a powerful tool for studying evolutionary changes among organisms, helping to identify genes that are conserved among species, as well as genes that give each organism its unique characteristics. However, the huge datasets involved makes this approach impractical on traditional computer architectures leading to prohibitively long runtimes. In this paper, we present a new computational grid architecture based on a hybrid computing model to significantly accelerate comparative genomics applications. The hybrid computing model consists of two types of parallelism: coarse grained and fine grained. The coarse-grained parallelism uses a volunteer computing infrastructure for job distribution, while the fine-grained parallelism uses commodity computer graphics hardware for fast sequence alignment. We present the deployment and evaluation of this approach on our grid test bed for the all-against-all comparison of microbial genomes. The results of this comparison are then used by phenotype--genotype explorer (PheGee). PheGee is a new tool that nominates candidate genes responsible for a given phenotype.