[Regular Paper] A High-Performance Sequence Analysis Engine for Shotgun Metagenomics through GPU Acceleration

Abstract

With the continual growth of low-cost and high-throughput DNA sequence technology, the scale and amount of next-generation sequencing (NGS) datasets are continually increasing in many genomics research areas. Shotgun metagenomics sequencing provides comprehensive information on microorganisms, based on complex samples of the ecosystem. Due to challenges of its scale and computational complexity, efficient sequence processing and analyzing tools are needed. In this paper, we propose a novel high-performance shotgun metagenomics sequence analysis engine for the task of sequence comparison. It includes two major components. First, a customized shifting database, which is optimized from any existing DNA sequence dataset. Second, a high-performance sequence computation algorithm that utilizes the customized shifting reference database and accelerates GPU parallel computing. We elaborated upon the efficiency and computational complexity of our proposed approach in an HPC server, which has eight Nvidia Tesla P100 GPUs. We also conducted a case study to detect viral sequences from patients' blood samples. Our experimental result shows that we obtain similar accuracy to the conventional BLAST method, but with a computational speed that is about twenty times faster.