DUDE-Seq: Fast, Flexible, and Robust Denoising for Targeted Amplicon Sequencing

TL;DR

DUDE-Seq is a novel denoising method for targeted amplicon sequencing that effectively corrects errors, improves analysis reliability, and is adaptable across platforms, outperforming existing methods in accuracy and efficiency.

Contribution

It introduces DUDE-Seq, a flexible, robust denoising algorithm based on a discrete memoryless channel model, enhancing error correction in high-throughput sequencing.

Findings

Outperforms existing methods in error correction accuracy

Reduces processing time compared to alternatives

Enhances downstream analysis reliability

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.