SimPrily: A Python framework to simplify high-throughput genomic simulations
AuthorGladstein, Ariella L.
Quinto-Cortés, Consuelo D.
Pistorius, Julian L.
Joyce, Blake L.
AffiliationUniv Arizona, Dept Ecol & Evolutionary Biol
Univ Arizona, BI05 Inst
Univ Arizona, Dept Comp Sci
Univ Arizona, Grad Interdisciplinary Program Appl Math
MetadataShow full item record
PublisherELSEVIER SCIENCE BV
CitationGladstein, A. L., Quinto-Cortés, C. D., Pistorius, J. L., Christy, D., Gantner, L., & Joyce, B. L. (2018). SimPrily: A Python framework to simplify high-throughput genomic simulations. SoftwareX, 7, 335-340.
Rights© 2018 The Authors. Published by Elsevier B.V.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractGenomic simulations are an important technique used in population genetics to infer demographic history, test for regions under selection, and create datasets to validate software. However, running thousands of simulations and manipulating large loci can present computational challenges. We present SimPrily, a Python tool optimized for high throughput computing (HTC), which facilitates simulation of whole chromosomes. SimPrily can use prior distributions of parameters to run simulations, incorporate single nucleotide polymorphism array ascertainment bias into the simulated model, and calculate a variety of genomic summary statistics. We include with SimPrily high-throughput workflows that leverage free computing resources through the Open Science Grid and CyVerse Discovery Environment, allowing researchers to run thousands or millions of large-locus simulations with minimal or no prior command line knowledge. (c) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
NoteOpen access journal
VersionFinal published version
SponsorsCyVerse [NSF DBI-0735191, DBI-1265383]; US NSF under OAC SI2-SSI program ; NSF ; U.S. Department of Energy's Office of Science