[Paper Review] DUDE-Seq: Fast, flexible, and robust denoising of nucleotide sequences

Byunghan Lee, Taesup Moon+2 more|arXiv (Cornell University)|Nov 16, 2015

Genomics and Phylogenetic Studies60 references3 citations

TL;DR

DUDE-Seq is a fast, flexible, and robust denoising method for nucleotide sequences that corrects substitution and homopolymer indel errors using a discrete memoryless channel model. It outperforms existing methods in error correction accuracy and computational efficiency while being adaptable to diverse sequencing platforms via noise model updates.

ABSTRACT

We consider the correction of errors from nucleotide sequences produced by next-generation sequencing. The error rate in reads has been increasing with the shift of focus of mainstream sequencers from accuracy to throughput. Denoising in high-throughput sequencing is thus becoming a crucial component 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 provides an effective means for correcting substitution and homopolymer indel errors, the two major types of sequencing errors in most high-throughput sequencing platforms. Our experimental studies with real and simulated data sets suggest that the proposed DUDE-Seq not only outperforms existing alternatives in terms of error-correction capabilities, and time efficiency, but also boosts the reliability of downstream analyses. Further, the flexibility of DUDE-Seq enables us to robustly apply it to different sequencing platforms and analysis pipelines by a simple update of the noise model. [availability: this http URL]

Motivation & Objective

• To address the growing challenge of increasing error rates in next-generation sequencing due to the shift from accuracy to throughput.
• To develop a denoising method effective for the two major error types in high-throughput sequencing: substitution and homopolymer indel errors.
• To create a method that maintains high accuracy while achieving superior time efficiency compared to existing approaches.
• To ensure robustness across diverse sequencing platforms by enabling simple noise model updates.

Proposed method

• DUDE-Seq is derived from a general framework for reconstructing finite-valued source data corrupted by a discrete memoryless channel.
• It models sequencing errors as transitions in a discrete memoryless channel, enabling principled correction of substitution and homopolymer indel errors.
• The method uses a likelihood-based reconstruction approach that estimates the most probable original sequence given the observed noisy read and the noise model.
• It supports flexible adaptation to different sequencing platforms by updating the noise model without altering the core algorithm.
• The algorithm operates efficiently by leveraging local context and minimizing computational overhead.
• It is designed to be integrated into various analysis pipelines with minimal configuration changes.

Experimental results

Research questions

• RQ1 How can nucleotide sequences be effectively denoised to improve downstream analysis reliability in high-throughput sequencing?
• RQ2 What is the performance of DUDE-Seq in correcting substitution and homopolymer indel errors compared to existing methods?
• RQ3 To what extent does DUDE-Seq maintain high speed and low computational cost while achieving superior error correction?
• RQ4 Can DUDE-Seq be flexibly applied across diverse sequencing platforms with minimal reconfiguration?
• RQ5 How does DUDE-Seq enhance the accuracy of downstream analyses such as variant calling or assembly?

Key findings

• DUDE-Seq outperforms existing denoising methods in error-correction accuracy for both substitution and homopolymer indel errors on real and simulated datasets.
• The method achieves significant improvements in time efficiency, enabling faster processing of high-throughput sequencing data.
• DUDE-Seq enhances the reliability of downstream analyses by reducing false positives and improving variant detection accuracy.
• The flexibility of DUDE-Seq allows seamless adaptation to different sequencing platforms through simple noise model updates.
• Experimental results demonstrate robust performance across diverse sequencing technologies and analysis pipelines.