High Speed Biological Sequence Analysis with Hidden Markov Models on Reconfigurable Platforms

Oliver, Tim; Schmidt, Bernhard; Jakop, Y.; Maskell, Douglas L.

doi:10.1109/titb.2008.917898

Cited by 2 publications

(2 citation statements)

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“…As the Viterbi algorithm has been used in many topics, such as pairwise alignments, multiple sequence alignment and gene prediction, there is much research focusing on the acceleration of the Viterbi algorithm [10][11] [12]. The acceleration of the Viterbi algorithm commonly utilizes a log-transformation of the original equations, which transform floating-point multiplication operations into floating-point addition operations.…”

Section: Related Workmentioning

confidence: 99%

FPGA acceleration of the pair-HMMs forward algorithm for DNA sequence analysis

Ren

Sima

Al-Ars

2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Many DNA sequence analysis tools have been developed to turn the massive raw DNA sequencing data generated by NGS (Next Generation Sequencing) platforms into biologically meaningful information. The pair-HMMs forward algorithm is widely used to calculate the overall alignment probability needed by a number of DNA analysis tools. In this paper, we propose a novel systolic array design to accelerate the pair-HMMs forward algorithm on FPGAs. A number of architectural features have been implemented to improve the performance of the design, such as early exit points to increase the utilization of the array for small sequence sizes, as well as on-chip buffering to enable the processing of long sequences effectively. We present an implementation of the design on the Convey supercomputing platform. Experimental results show that the FPGA implementation of the pair-HMMs forward algorithm is up to 67x faster, compared to software-only execution.

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Section: Related Workmentioning

confidence: 99%

FPGA acceleration of the pair-HMMs forward algorithm for DNA sequence analysis

Ren

Sima

Al-Ars

2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…Walters et al [8] implemented the HMMER algorithm on a hybrid system consisting of reconfigurable devices and an MPI cluster and they achieved a speedup of up to 30x in a system with a large number of CPUs and 2 FPGA devices. Oliver et al in [9,10] mapped the Plan7 Viterbi algorithm, which is the basic stage of the HMMER algorithm, on a Spartan 3 FPGA achieving acceleration of up to 30x against the execution of this same stage on a high-end AMD CPU. Takagi et al in [11] propose a fully parallel implementation of the Viterbi algorithm which, when it is executed on a high end FPGA, it achieves an up to 360x speedup for certain sequences vs. the execution of the official software on a Core2Duo CPU.…”

Section: Related Workmentioning

confidence: 99%

Parallel accelerators for GlimmerHMM bioinformatics algorithm

Chrysanthou

Chrysos

Sotiriades

et al. 2011

2011 Design, Automation &Amp; Test in Europe

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In the last decades there is an exponential growth in the amount of genomic data that need to be analyzed. A very important problem in biology is the extraction of the biologically functional genomic DNA from the actual genome of the organisms. There have been proposed many computational biology algorithms that solve the gene finding problem which utilize various approaches; GlimmerHMM is considered one of the most efficient such algorithms. This paper presents two different accelerators for the GlimmerHMM algorithm. One of them is implemented on a modern FPGA platform exploiting the parallelism that reconfigurable logic offers and the other one utilizes a GPU (Graphic Processing Unit) taking advantage of a highly multithreaded operational environment. The performance of the implemented systems is compared against the one achieved when the official distribution of the algorithm is executed on a high-end multi-core server; the speedup initiated, for the most compute intensive part, is up to 200x for the FPGA-based system and up to 34x for the GPU-based system.

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High Speed Biological Sequence Analysis with Hidden Markov Models on Reconfigurable Platforms

Cited by 2 publications

References 0 publications

FPGA acceleration of the pair-HMMs forward algorithm for DNA sequence analysis

FPGA acceleration of the pair-HMMs forward algorithm for DNA sequence analysis

Parallel accelerators for GlimmerHMM bioinformatics algorithm

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