Open AccessSimulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models
Author(s) -
Zachary Stephens,
Matthew E. Hudson,
Liudmila Sergeevna Mainzer,
Morgan Taschuk,
Matthew Weber,
Ravishankar K. Iyer
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0167047
Subject(s) - computer science , benchmarking , software , set (abstract data type) , data mining , genome , dna sequencing , scripting language , computational biology , biology , genetics , gene , programming language , operating system , marketing , business
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 .