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...

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Bibliographic Details
Main Authors: Haixiang Shi, Schmidt, B., Weiguo Liu, Muller-Wittig, W.
Format: Conference Proceeding
Language:English
Subjects:
Acceleration
Assembly
Bioinformatics
DNA
Error correction
Genomics
Parallel algorithms
Runtime
Scalability
Throughput
Online Access:Request full text
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Summary: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).
ISSN:1530-2075
DOI:10.1109/IPDPS.2009.5160924