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

: Shi, Haixiang; Schmidt, Bertil; Liu, Weiguo; Müller-Wittig, Wolfgang K.


IEEE Computer Society, Technical Committee on Parallel Processing; Association for Computing Machinery -ACM-, Special Interest Group on Computer Architecture -SIGARCH-:
IEEE International Symposium on Parallel & Distributed Processing, IPDPS 2009. Proceedings : Rome, Italy, 23 - 29 May 2009
Piscataway/NJ: IEEE, 2009
ISBN: 978-1-4244-3751-1
ISBN: 978-1-4244-3750-4
ISSN: 1530-2075
8 pp.
International Parallel and Distributed Processing Symposium (IPDPS) <23, 2009, Rome>
Conference Paper
Fraunhofer IGD ()
Bioinformatics; sequence alignment; parallel algorithms

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).