Faster sequence alignment through GPU-accelerated restriction of the seed-and-extend search space

Richard Wilton, Tamas Budavari, Ben Langmead, Sarah Wheelan, Steven L. Salzberg, Alex Szalay
Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, USA
bioRxiv 007641, (1 August 2014)


   title={Faster sequence alignment through GPU-accelerated restriction of the seed-and-extend search space},

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




   publisher={Cold Spring Harbor Labs Journals}


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MOTIVATION: In computing pairwise alignments of biological sequences, software implementations employ a variety of heuristics that decrease the computational effort involved in computing potential alignments. A key element in achieving high processing throughput is to identify and prioritize potential alignments where high-scoring mappings can be expected. These tasks involve list-processing operations that can be efficiently performed on GPU hardware. RESULTS: We implemented a read aligner called A21 that exploits GPU-based parallel sort and reduction techniques to restrict the number of locations where potential alignments may be found. When compared with other high-throughput aligners, this approach finds more high-scoring mappings without sacrificing speed or accuracy. A21 running on a single GPU is about 10 times faster than comparable CPU-based tools; it is also faster and more sensitive in comparison with other recent GPU-based aligners.
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