Abstract

An obstacle to validating and benchmarking methods for genome analysis is that there are few reference datasets available for which the "ground truth" about the mutational landscape of the sample genome is known and fully validated. Additionally, the free and public availability of real human genome datasets is incompatible with the preservation of donor privacy. In order to better analyze and understand genomic data, we need test datasets that model all variants, reflecting known biology as well as sequencing artifacts. Read simulators can fulfill this requirement, but are often criticized for limited resemblance to true data and overall inflexibility. We present NEAT (NExt-generation sequencing Analysis Toolkit), a set of tools that not only includes an easy-to-use read simulator, but also scripts to facilitate variant comparison and tool evaluation. NEAT has a wide variety of tunable parameters which can be set manually on the default model or parameterized using real datasets. The software is freely available at github.com/zstephens/neat-genreads.

Original languageEnglish (US)
Article numbere0167047
JournalPloS one
Volume11
Issue number11
DOIs
StatePublished - Nov 1 2016

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Genes
mutation
Mutation
genome
Simulators
Genome
Benchmarking
Privacy
Human Genome
Availability
Artifacts
genomics
Biological Sciences
Software
Datasets
testing
sampling
methodology

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Simulating next-generation sequencing datasets from empirical mutation and sequencing models. / Stephens, Zachary D.; Hudson, Matthew; Mainzer, Liudmila Sergeevna; Taschuk, Morgan; Weber, Matthew R.; Iyer, Ravishankar K.

In: PloS one, Vol. 11, No. 11, e0167047, 01.11.2016.

Research output: Contribution to journalArticle

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