Error correction is often an important first step prior to analyzing reads from next-generation DNA sequencers. This talk will be focused on a flexible read decomposition method developed to improve the accuracy of error correction and make it more computationally efficient. The method relies on decomposing a read by overlapping tiles, each containing two or more kmers that serve as the basis for error correction. While the value of k is chosen so that the kmer occurs with sufficient frequency, the surrounding kmers within the tile provide the context for improving specificity and resolve ambiguity. The method adopts a flexible tile decomposition strategy to make swift progress in regions with sparse occurrence of errors and thorough exploration in regions of clustered errors. Space usage is reduced by avoiding explicit construction and storage of relationships among kmers, instead relying on the creation of space-efficient data structures that can compute such information on the fly. Experimental verification on benchmark data sets from Illumina Genome Analyzer confirms that the method achieves high error correction accuracy, while the improvements in run-time and memory usage enable scaling to large data sets.
展开▼
