Journal
BIOINFORMATICS
Volume 37, Issue 5, Pages 589-595Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btaa835
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Funding
- MEXT KAKENHI [JP24680031, JP16H05879, JP20H00624, JP16H06279, JP25240044]
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Researchers introduced a new generative model for quality scores to capture characteristics of errors in reads for long-read sequencers, and evaluated that their simulator successfully simulates reads consistent with real reads.
Motivation: Recent advances in high-throughput long-read sequencers, such as PacBio and Oxford Nanopore sequencers, produce longer reads with more errors than short-read sequencers. In addition to the high error rates of reads, non-uniformity of errors leads to difficulties in various downstream analyses using long reads. Many useful simulators, which characterize long-read error patterns and simulate them, have been developed. However, there is still room for improvement in the simulation of the non-uniformity of errors. Results: To capture characteristics of errors in reads for long-read sequencers, here, we introduce a generative model for quality scores, in which a hidden Markov Model with a latest model selection method, called factorized information criteria, is utilized. We evaluated our developed simulator from various points, indicating that our simulator successfully simulates reads that are consistent with real reads.
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