Literature DB >> 16020469

simuPOP: a forward-time population genetics simulation environment.

Bo Peng1, Marek Kimmel.   

Abstract

SUMMARY: simuPOP is a forward-time population genetics simulation environment. The core of simuPOP is a scripting language (Python) that provides a large number of objects and functions to manipulate populations, and a mechanism to evolve populations forward in time. Using this R/Splus-like environment, users can create, manipulate and evolve populations interactively, or write a script and run it as a batch file. Owing to its flexible and extensible design, simuPOP can simulate large and complex evolutionary processes with ease. At a more user-friendly level, simuPOP provides an increasing number of built-in scripts that perform simulations ranging from implementation of basic population genetics models to generating datasets under complex evolutionary scenarios. AVAILABILITY: simuPOP is freely available at http://simupop.sourceforge.net, distributed under GPL license.

Entities:  

Mesh:

Year:  2005        PMID: 16020469     DOI: 10.1093/bioinformatics/bti584

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  132 in total

Review 1.  Computer simulations: tools for population and evolutionary genetics.

Authors:  Sean Hoban; Giorgio Bertorelle; Oscar E Gaggiotti
Journal:  Nat Rev Genet       Date:  2012-01-10       Impact factor: 53.242

Review 2.  An overview of population genetic data simulation.

Authors:  Xiguo Yuan; David J Miller; Junying Zhang; David Herrington; Yue Wang
Journal:  J Comput Biol       Date:  2011-12-09       Impact factor: 1.479

3.  Using Ascertainment for Targeted Resequencing to Increase Power to Identify Causal Variants.

Authors:  M D Swartz; B Peng; C Reyes-Gibby; S Shete
Journal:  Stat Interface       Date:  2011       Impact factor: 0.582

4.  Simulating sequences of the human genome with rare variants.

Authors:  Bo Peng; Xiaoming Liu
Journal:  Hum Hered       Date:  2011-01-06       Impact factor: 0.444

5.  Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates.

Authors:  Mathieu Gautier
Journal:  Genetics       Date:  2015-10-19       Impact factor: 4.562

6.  Simulations provide support for the common disease-common variant hypothesis.

Authors:  Bo Peng; Marek Kimmel
Journal:  Genetics       Date:  2006-12-06       Impact factor: 4.562

7.  Sequence-level population simulations over large genomic regions.

Authors:  Clive J Hoggart; Marc Chadeau-Hyam; Taane G Clark; Riccardo Lampariello; John C Whittaker; Maria De Iorio; David J Balding
Journal:  Genetics       Date:  2007-10-18       Impact factor: 4.562

8.  Simulation of DNA sequence evolution under models of recent directional selection.

Authors:  Yuseob Kim; Thomas Wiehe
Journal:  Brief Bioinform       Date:  2008-12-24       Impact factor: 11.622

9.  SLiM: simulating evolution with selection and linkage.

Authors:  Philipp W Messer
Journal:  Genetics       Date:  2013-05-24       Impact factor: 4.562

10.  Factors influencing ascertainment bias of microsatellite allele sizes: impact on estimates of mutation rates.

Authors:  Biao Li; Marek Kimmel
Journal:  Genetics       Date:  2013-08-14       Impact factor: 4.562

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.