A General Reconfigurable Architecture for the BLAST Algorithm
Journal of VLSI Signal Processing Systems
A high performance fpga-based implementation of position specific iterated blast
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
Overview of the Blue Gene/L system architecture
IBM Journal of Research and Development
Network-on-Chip Hardware Accelerators for Biological Sequence Alignment
IEEE Transactions on Computers
Search and clustering orders of magnitude faster than BLAST
Bioinformatics
FPGA-based hardware acceleration for local complexity analysis of massive genomic data
Integration, the VLSI Journal
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Modern biological research has greatly benefited from genomics. Such research however requires extensive computational power, traditionally employed on large-scale cluster machines as well as multi-core systems. Recent research in reconfigurable architectures however suggests that FPGA-based acceleration of genomic algorithms greatly improves the performance and energy efficiency when compared to multi-core systems and clusters. In this work, we present an initial attempt for massive systolic acceleration of the popular CAST algorithm employed by biologists for complexity analysis of genomic data. CAST is used for detecting (and subsequently masking) low-complexity regions (LCRs) in protein sequences. We designed and implemented a high-performance hardware-accelerated version of CAST for which we built an FPGA prototype, and benchmarked its performance against serial and multithreaded versions of the CAST algorithm in software. The proposed architecture achieves remarkable speedup compared to both serial and multithreaded CAST implementations ranging from approx. 100x-9500x, depending on the dataset features, such as low-complexity content and sequence length distribution. Such performance may enable complex analyses of voluminous sequence datasets, and has the potential to interoperate with other hardware architectures for protein sequence analysis.