Abstract-While the sequencing capability of modern instruments continues to increase exponentially, the computational problem of mapping short sequenced reads to a reference genome still constitutes a bottleneck in the analysis pipeline. A variety of mapping tools (e.g., BOWTIE, BWA) is available for general-purpose computer architectures. These tools can take many hours or even days to deliver mapping results, depending on the number of input reads, the size of the reference genome and the number of allowed mismatches or insertion/deletions, making the mapping problem an ideal candidate for hardware acceleration. In this paper, we present FHAST (FPGA hardware accelerated sequence-matching tool), a drop-in replacement for BOWTIE that uses a hardware design based on field programmable gate arrays (FPGA). Our architecture masks memory latency by executing multiple concurrent hardware threads accessing memory simultaneously. FHAST is composed by multiple parallel engines to exploit the parallelism available to us on an FPGA. We have implemented and tested FHAST on the Convey HC-1 and later ported on the Convey HC-2ex, taking advantage of the large memory bandwidth available to these systems and the shared memory image between hardware and software. A preliminary version of FHASTrunning on the Convey HC-1 achieved up to 70x speedup compared to BOWTIE (single-threaded). An improved version of FHAST running on the Convey HC-2ex FPGAs achieved up to 12x fold speed gain compared to BOWTIE running eight threads on an eight-core conventional architecture, while maintaining almost identical mapping accuracy. FHAST is a drop-in replacement for BOWTIE, so it can be incorporated in any analysis pipeline that uses BOWTIE (e.g., TOPHAT).
Fernandez et al. presented an FPGAbased accelerator for the Bowtie tool using the Convey HC-1/HC-2ex systems REF .
FHAST: FPGA-Based Acceleration of Bowtie in Hardware
IEEE/ACM Transactions on Computational Biology and Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Computer Science Medicine