DUDE-Seq considers the correction of errors from nucleotide sequences produced by next-generation sequencing. Our methodology, named DUDE-Seq, is derived from a general setting of reconstructing finite-valued source data corrupted by a discrete memoryless channel and provides an effective means for correcting substitution and homopolymer indel errors. Our experimental studies with real and simulated data sets suggest that the proposed DUDE-Seq outperforms existing alternatives in terms of error-correction capabilities, time efficiency, as boosting the reliability of downstream analyses. Further, DUDE-Seq is universally applicable across different sequencing platforms and analysis pipelines by a simple update of the noise model.
DUDE-Seq adopts an universal algorithm called Discrete Universal DEnoiser (DUDE) to the DNA sequence error correction problem. The semi-stochastic modeling approach from the DUDE framework naturally fits the setting of DNA sequence denoising problems.
Byunghan Lee, Taesup Moon*, Sungroh Yoon*, and Tsachy Weissman, "DUDE-Seq: Fast, Flexible, and Robust Denoising for Targeted Amplicon Sequencing," PLOS ONE, 12(7): e0181463, July 2017.