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Simulation of nanopore sequencing signal data with tunable parameters.
Gamaarachchi, Hasindu; Ferguson, James M; Samarakoon, Hiruna; Liyanage, Kisaru; Deveson, Ira W.
Afiliação
  • Gamaarachchi H; School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia; hasindu@garvan.org.au i.deveson@garvan.org.au.
  • Ferguson JM; Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia.
  • Samarakoon H; Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, New South Wales 2010, Australia Australia.
  • Liyanage K; Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia.
  • Deveson IW; Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, New South Wales 2010, Australia Australia.
Genome Res ; 34(5): 778-783, 2024 06 25.
Article em En | MEDLINE | ID: mdl-38692839
ABSTRACT
In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, a transcriptome, or read sequences, and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation, and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols or noise-free "ideal" data, or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Sequenciamento por Nanoporos Limite: Humans Idioma: En Revista: Genome Res Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Sequenciamento por Nanoporos Limite: Humans Idioma: En Revista: Genome Res Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2024 Tipo de documento: Article