Format

Send to

Choose Destination
PLoS One. 2016 Nov 28;11(11):e0167047. doi: 10.1371/journal.pone.0167047. eCollection 2016.

Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models.

Author information

1
Department of Electrical and Computer Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America.
2
Department of Crop Sciences, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America.
3
Institute for Genomic Biology, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America.
4
National Center for Supercomputing Applications, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America.
5
Ontario Institute for Cancer Research, Toronto, ON, Canada.

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.

PMID:
27893777
PMCID:
PMC5125660
DOI:
10.1371/journal.pone.0167047
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center