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• W2267036574 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]" @default.
• W2267036574 created "2016-06-24" @default.
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• W2267036574 date "2015-11-16" @default.
• W2267036574 modified "2023-09-27" @default.
• W2267036574 title "DUDE-Seq: Fast, flexible, and robust denoising of nucleotide sequences" @default.
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