Abstract
Second-generation DNA sequencing techniques generate short reads that can result in fragmented genome assemblies. Third-generation sequencing platforms mitigate this limitation by producing longer reads that span across complex and repetitive regions. However, the usefulness of such long reads is limited because of high sequencing error rates. To exploit the full potential of these longer reads, it is imperative to correct the underlying errors. We propose HECIL—Hybrid Error Correction with Iterative Learning—a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments. We demonstrate that HECIL outperforms state-of-the-art error correction algorithms for an overwhelming majority of evaluation metrics on diverse, real-world data sets including E. coli, S. cerevisiae, and the malaria vector mosquito A. funestus. Additionally, we provide an optional avenue of improving the performance of HECIL’s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.
Highlights
Various correction algorithms have been proposed for reducing the currently high error rates prevalent in long reads
We filter long reads of E. coli to exclude reads shorter than 100 bp, creating a final set of 33,360
Due to the high computational effort required by proovread and CoLoRMap to correct the reads of all flowcells, we present a comparative analysis based on a representative selection of three flowcells: 1, 4, and 16
Summary
Various correction algorithms have been proposed for reducing the currently high error rates prevalent in long reads. HGAP13 is a self-correcting algorithm (that is, it does not rely on additional sequencing data) that performs correction by computing multiple alignments of high coverage long reads. Another class of correction algorithms rely on short reads generated from the same (or related) samples, and is referred as hybrid correction algorithms. The iterative procedure further improves the quality of error correction both in terms of alignment and assembly-based metrics by incorporating knowledge derived from high-confidence corrections made in prior iterations. We speculate that the proposed iterative learning formalism can be incorporated into other contemporary hybrid error correction algorithms to improve performance, at the expense of total execution time
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
