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Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space

Richard Wilton, Tamas Budavari, Ben Langmead, Sarah J. Wheelan, Steven L. Salzberg, Alexander S. Szalay
Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA
PeerJ, DOI 10.7717/peerj.808, 2015

@article{wilton2015arioc,

   title={Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space},

   author={Wilton, Richard and Budavari, Tamas and Langmead, Ben and Wheelan, Sarah J and Salzberg, Steven L and Szalay, Alexander S},

   journal={PeerJ},

   volume={3},

   pages={e808},

   year={2015},

   publisher={PeerJ Inc.}

}

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When computing alignments of DNA sequences to a large genome, a key element in achieving high processing throughput is to prioritize locations in the genome where high-scoring mappings might be expected. We formulated this task as a series of list-processing operations that can be efficiently performed on graphics processing unit (GPU) hardware.We followed this approach in implementing a read aligner called Arioc that uses GPU-based parallel sort and reduction techniques to identify high-priority locations where potential alignments may be found. We then carried out a read-by-read comparison of Arioc’s reported alignments with the alignments found by several leading read aligners. With simulated reads, Arioc has comparable or better accuracy than the other read aligners we tested. With human sequencing reads, Arioc demonstrates significantly greater throughput than the other aligners we evaluated across a wide range of sensitivity settings. The Arioc software is available at https://github.com/RWilton/Arioc. It is released under a BSD open-source license.
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