Massively parallel read mapping on GPUs with PEANUT
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/.
March 10, 2014 by hgpu
