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PLoS One. 2017 Jul 27;12(7):e0181463. doi: 10.1371/journal.pone.0181463. eCollection 2017.

DUDE-Seq: Fast, flexible, and robust denoising for targeted amplicon sequencing.

Author information

Electrical and Computer Engineering, Seoul National University, Seoul, Korea.
College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea.
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
Neurology and Neurological Sciences, Stanford University, Stanford, California, United States of America.
Electrical Engineering, Stanford University, Stanford, California, United States of America.


We consider the correction of errors from nucleotide sequences produced by next-generation targeted amplicon sequencing. The next-generation sequencing (NGS) platforms can provide a great deal of sequencing data thanks to their high throughput, but the associated error rates often tend to be high. Denoising in high-throughput sequencing has thus become a crucial process for boosting the reliability of downstream analyses. Our methodology, named DUDE-Seq, is derived from a general setting of reconstructing finite-valued source data corrupted by a discrete memoryless channel and effectively corrects substitution and homopolymer indel errors, the two major types of sequencing errors in most high-throughput targeted amplicon sequencing platforms. Our experimental studies with real and simulated datasets suggest that the proposed DUDE-Seq not only outperforms existing alternatives in terms of error-correction capability and time efficiency, but also boosts the reliability of downstream analyses. Further, the flexibility of DUDE-Seq enables its robust application to different sequencing platforms and analysis pipelines by simple updates of the noise model. DUDE-Seq is available at

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