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).

• Type: Conference
• Year: 2009
• Where: IPPS
• Authors: Haixiang Shi, Bertil Schmidt, Weiguo Liu, Wolfgang Müller-Wittig
• Keywords: Distributed And Parallel Computing, DNA Sequencing Technologies, IPPS 2009, Sequencing, Shotgun Sequencing Method