11568

Massively parallel read mapping on GPUs with PEANUT

Johannes Koster, Sven Rahmann
Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen
arXiv:1403.1706 [cs.DS], (7 Mar 2014)
@article{2014arXiv1403.1706K,

   author={K{"o}ster}, J. and {Rahmann}, S.},

   title={"{Massively parallel read mapping on GPUs with PEANUT}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1403.1706},

   primaryClass={"cs.DS"},

   keywords={Computer Science – Data Structures and Algorithms, Computer Science – Distributed, Parallel, and Cluster Computing, Quantitative Biology – Quantitative Methods},

   year={2014},

   month={mar},

   adsurl={http://adsabs.harvard.edu/abs/2014arXiv1403.1706K},

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

}

We present PEANUT (ParallEl AligNment UTility), a highly parallel GPU-based read mapper with several distinguishing features, including a novel q-gram index (called the q-group index) with small memory footprint built on-the-fly over the reads and the possibility to output both the best hits or all hits of a read. Designing the algorithm particularly for the GPU architecture, we were able to reach maximum core occupancy for several key steps. Our benchmarks show that PEANUT outperforms other state-of- the-art mappers in terms of speed and sensitivity. The software is available at http://peanut.readthedocs.org/.
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