A GPU-Based Smith-Waterman Approach for Genome Editing

Abstract

CRISPR is a mechanism used by archaea and bacteria to prevent virus attacks. The CRISPR-Cas9 technique was inspired by this natural genome editing system in bacteria where the latter captures snippets of DNA from invading viruses to create DNA segments known as CRISPR arrays. CRISPR-Cas9 aims to detect RNA virus pattern locations in DNA sequences and then use them to deactivate attacking viruses. Many CRISPR-Cas9 tools and algorithms were implemented for genome editing to locate viruses. However, some of these algorithms have poor performance which hinders their applicability on large DNA sequences. In this paper, we use a well-known sequence alignment algorithm, Smith Waterman, to calculate the similarity between a pattern query and long DNA sequences. Our approach has four versions which are sequential, parallel CPU, parallel GPU and hybrid CPU-GPU implementations. Our experiments show that the hybrid implementation gets the best results with performance gain reaching 25 times faster than the sequential implementation and 6 times faster than the parallel CPU implementation.