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2009
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Accelerating error correction in high-throughput short-read DNA sequencing data with CUDA

8 years 11 months ago
Accelerating error correction in high-throughput short-read DNA sequencing data with CUDA
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 highthroughput 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).
Haixiang Shi, Bertil Schmidt, Weiguo Liu, Wolfgang
Added 24 May 2010
Updated 24 May 2010
Type Conference
Year 2009
Where IPPS
Authors Haixiang Shi, Bertil Schmidt, Weiguo Liu, Wolfgang Müller-Wittig
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