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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

1
Electrical and Computer Engineering, Seoul National University, Seoul, Korea.
2
College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea.
3
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
4
Neurology and Neurological Sciences, Stanford University, Stanford, California, United States of America.
5
Electrical Engineering, Stanford University, Stanford, California, United States of America.

Abstract

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 http://data.snu.ac.kr/pub/dude-seq.

PMID:
28749987
PMCID:
PMC5531809
DOI:
10.1371/journal.pone.0181463
[Indexed for MEDLINE]
Free PMC Article

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