Accelerating error correction in high-throughput short-read DNA sequencing data with CUDA
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}
}
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).
November 27, 2010 by hgpu
