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Accelerating error correction in high-throughput short-read DNA sequencing data with CUDA

Haixiang Shi, B. Schmidt, Weiguo Liu, W. Muller-Wittig
School of Computer Engineering, Nanyang Technological University, Singapore 639798
In IPDPS ’09: Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing (May 2009), pp. 1-8

@conference{shi2009accelerating,

   title={Accelerating error correction in high-throughput short-read DNA sequencing data with CUDA},

   author={Shi, H. and Schmidt, B. and Liu, W. and Muller-Wittig, W.},

   booktitle={Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on},

   pages={1–8},

   issn={1530-2075},

   year={2009},

   organization={IEEE}

}

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Emerging DNA sequencing technologies open up exciting new opportunities for genome sequencing by generating read data with a massive throughput. However, produced reads are significantly shorter and more error-prone compared to the traditional Sanger shotgun sequencing method. This poses challenges for de-novo DNA fragment assembly algorithms in terms of both accuracy (to deal with short, error-prone reads) and scalability (to deal with very large input data sets). In this paper we present a scalable parallel algorithm for correcting sequencing errors in high-throughput short-read data. It is based on spectral alignment and uses the CUDA programming model. Our computational experiments on a GTX 280 GPU show runtime savings between 10 and 19 times (for different error-rates using simulated datasets as well as real Solexa/Illumina datasets).
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