Searching for Potential gRNA Off-Target Sites for CRISPR/Cas9 Using Automata Processing Across Different Platforms

The CRISPR/Cas system is a bacteria immune system protecting cells from foreign genetic elements. One version that attracted special interest is CRISPR/Cas9, because it can be modified to edit genomes at targeted locations. However, the risk of binding and damaging off-target locations limits its power. Identifying all these potential off-target sites is thus important for users to effectively use the system to edit genomes. This process is computationally expensive, especially when one allows more differences in gRNA targeting sequences. In this paper, we propose using automata to search for off-target sites while allowing differences between the reference genome and gRNA targeting sequences. We evaluate the automata-based approach on four different platforms, including conventional architectures such as the CPU and the GPU, and spatial architectures such as the FPGA and Micron's Automata Processor. We compare the proposed approach with two off-target search tools (CasOFFinder (GPU) and CasOT (CPU)), and achieve over 83x speedups on the FPGA compared with CasOFFinder and over 600x speedups compared with CasOT. More customized hardware such as the AP can provide additional speedups (1.5x for the kernel execution) compared with the FPGA. We also evaluate the automata-based solution using single-thread HyperScan (a high-performance automata processing library) on the CPU. HyperScan outperforms CasOT by over 29.7x. The automata-based approach on iNFAnt2 (a DFA/NFA engine on the GPU) does not consistently work better than CasOFFinder, and only show a slightly better speedup compared with single-thread HyperScan on the CPU (4.4x for the best case). These results show that the automata-based approach provides significant algorithmic benefits, and that accelerators such as the FPGA and the AP can provide substantial additional speedups. However, iNFAnt2 does not confer a clear advantage because the proposed method does not map well to the GPU architecture. Furthermore, we propose several methods to further improve the performance on spatial architectures, and some potential architectural modifications for future automata processing hardware.