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SW# – GPU enabled exact alignments on genome scale

Matija Korpar, Mile Sikic
University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, HR 10000 Zagreb, Croatia
arXiv:1304.5966 [cs.DC], 22 Apr 2013

@article{2013arXiv1304.5966K,

   author={Korpar}, M. and {Sikic}, M.},

   title={"{SW# – GPU enabled exact alignments on genome scale}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1304.5966},

   primaryClass={"cs.DC"},

   keywords={Computer Science – Distributed, Parallel, and Cluster Computing, Quantitative Biology – Genomics},

   year={2013},

   month={apr},

   adsurl={http://adsabs.harvard.edu/abs/2013arXiv1304.5966K},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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Sequence alignment is one of the oldest and the most famous problems in bioinformatics. Even after 45 years, for one reason or another, this problem is still actual; current solutions are trade-offs between execution time, memory consumption and accuracy. We purpose SW#, a new CUDA GPU enabled and memory efficient implementation of dynamic programming algorithms for local alignment. In this implementation indels are treated using the affine gap model. Although there are other GPU implementations of the Smith-Waterman algorithm, SW# is the only publicly available implementation that can produce sequence alignments on genome-wide scale. For long sequences, our implementation is at least a few hundred times faster than a CPU version of the same algorithm.
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