PBSIM2: A simulator for long-read sequencers with a novel generative model of quality scores

Yukiteru Ono, Kiyoshi Asai, Michiaki Hamada

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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. Availability and implementation: The source codes of PBSIM2 are freely available from https://github.com/yukiter uono/pbsim2.

Original languageEnglish
Pages (from-to)589-595
Number of pages7
JournalBioinformatics
Volume37
Issue number5
DOIs
Publication statusPublished - 2021 Mar 1

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Fingerprint

Dive into the research topics of 'PBSIM2: A simulator for long-read sequencers with a novel generative model of quality scores'. Together they form a unique fingerprint.

Cite this