ClawHMMER: A Streaming HMMer-Search Implementatio
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Accelerator design for protein sequence HMM search
Proceedings of the 20th annual international conference on Supercomputing
MPI-HMMER-Boost: Distributed FPGA Acceleration
Journal of VLSI Signal Processing Systems
A parallel strategy for biological sequence alignment in restricted memory space
Journal of Parallel and Distributed Computing
A High Performance Reconfigurable Core for Motif Searching Using Profile HMM
AHS '08 Proceedings of the 2008 NASA/ESA Conference on Adaptive Hardware and Systems
HMMer acceleration using systolic array based reconfigurable architecture
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
High speed biological sequence analysis with hiddenMarkov models on reconfigurable platforms
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
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As new protein sequences are discovered on an everyday basis and protein databases continue to grow exponentially with time, computational tools take more and more time to search protein databases to discover the common ancestors of them. HMMER is among the most used tools in protein search and comparison and multiple efforts have been made to accelerate its execution by using dedicated hardware prototyped on FPGAs. In this paper we introduce a novel algorithm called the Divergence Algorithm, which not only enables the FPGA accelerator to reduce execution time, but also enables further acceleration of the alignment generation algorithm of the HMMER programs by reducing the number of cells of the Dynamic Programming matrices it has to calculate. We also propose a more accurate performance measurement strategy that considers all the execution times while doing protein searches and alignments, while other works only consider hardware execution times and do not include alignment generation times. Using our proposed hardware accelerator and the Divergence Algorithm, we were able to achieve gains up to 182x when compared to the unaccelerated HMMER software running on a general purpose CPU.