DUDE-Seq: Fast, flexible, and robust denoising of nucleotide sequences

Motivation: 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. Results: Our methodology, named DUDE-Seq, is derived from a general setting of reconstructing finitevalued 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: http://data.snu.ac.kr/pub/dude-seq Contact: [email protected], [email protected]