Prefiltering Model for Homology Detection Algorithms on GPU

Abstract

Homology detection has evolved over the time from heavy algorithms based on dynamic programming approaches to lightweight alternatives based on different heuristic models. However, the main problem with these algorithms is that they use complex statistical models, which makes it difficult to achieve a relevant speedup and find exact matches with the original results. Thus, their acceleration is essential. The aim of this article was to prefilter a sequence database. To make this work, we have implemented a groundbreaking heuristic model based on NVIDIA's graphics processing units (GPUs) and multicore processors. Depending on the sensitivity settings, this makes it possible to quickly reduce the sequence database by factors between 50% and 95%, while rejecting no significant sequences. Furthermore, this prefiltering application can be used together with multiple homology detection algorithms as a part of a next-generation sequencing system. Extensive performance and accuracy tests have been carried out in the Spanish National Centre for Biotechnology (NCB). The results show that GPU hardware can accelerate the execution times of former homology detection applications, such as National Centre for Biotechnology Information (NCBI), Basic Local Alignment Search Tool for Proteins (BLASTP), up to a factor of 4. KEY POINTS • Owing to the increasing size of the current sequence datasets, filtering approach and high-performance computing (HPC) techniques are the best solution to process all these information in acceptable processing times. • Graphics processing unit cards and their corresponding programming models are good options to carry out these processing methods. • Combination of filtration models with HPC techniques is able to offer new levels of performance and accuracy in homology detection algorithms such as National Centre for Biotechnology Information Basic Local Alignment Search Tool.