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
this http URL

本文提出了一种基于 DUDE-Seq 的新方法，用于纠正下一代定向扩增测序的基因序列中出现的替换和同源多聚物插入 / 缺失错误，结果表明该方法不仅在纠错能力和时间效率方面优于现有的替代方法，而且还提高了下游分析的可靠性。