A Parallel Algorithm for Spectrum-based Short Read Error Correction

Correcting sequence errors in high-throughput DNA sequencing by taking advantage of redundant sampling and low error rates is often an important first step in applications of this technology. Consequently, a number of error correction methods have been developed in the recent years. Due to an order of magnitude throughput gain per year, some of these technologies are now generating upwards of a billion reads per run. In this paper, we present an algorithm for parallel zing error correction methods that are based on frequency spectrum of kmers observed in input reads. Based on this, we present a parallelization of Reptile, a recently introduced error correction method that employs frequency spectrum of two different lengths, one for identifying correction possibilities and another for providing contextual information. Our method is well suited for distributed memory parallel computers and clusters. Experimental results indicate the method achieves near linear speedup and provides the ability to scale to larger data sets than previously demonstrated.