Literature DB >> 15271777

SelSim: a program to simulate population genetic data with natural selection and recombination.

Chris C A Spencer1, Graham Coop.   

Abstract

UNLABELLED: SelSim is a program for Monte Carlo simulation of DNA polymorphism data for a recombining region within which a single bi-allelic site has experienced natural selection. SelSim allows simulation from either a fully stochastic model of, or deterministic approximations to, natural selection within a coalescent framework. A number of different mutation models are available for simulating surrounding neutral variation. The package enables a detailed exploration of the effects of different models and strengths of selection on patterns of diversity. This provides a tool for the statistical analysis of both empirical data and methods designed to detect natural selection. AVAILABILITY: http://www.stats.ox.ac.uk/mathgen/software.html. SUPPLEMENTARY INFORMATION: http://www.stats.ox.ac.uk/mathgen/software.html.

Mesh:

Year:  2004        PMID: 15271777     DOI: 10.1093/bioinformatics/bth417

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


  68 in total

1.  The coalescent with selection on copy number variants.

Authors:  Kosuke M Teshima; Hideki Innan
Journal:  Genetics       Date:  2011-12-14       Impact factor: 4.562

2.  Exact coalescent simulation of new haplotype data from existing reference haplotypes.

Authors:  Chul Joo Kang; Paul Marjoram
Journal:  Bioinformatics       Date:  2012-01-17       Impact factor: 6.937

Review 3.  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

4.  TreesimJ: a flexible, forward time population genetic simulator.

Authors:  Brendan O'Fallon
Journal:  Bioinformatics       Date:  2010-07-29       Impact factor: 6.937

5.  Searching for footprints of positive selection in whole-genome SNP data from nonequilibrium populations.

Authors:  Pavlos Pavlidis; Jeffrey D Jensen; Wolfgang Stephan
Journal:  Genetics       Date:  2010-04-20       Impact factor: 4.562

6.  A powerful score test to detect positive selection in genome-wide scans.

Authors:  Ming Zhong; Kenneth Lange; Jeanette C Papp; Ruzong Fan
Journal:  Eur J Hum Genet       Date:  2010-05-12       Impact factor: 4.246

7.  Likelihood-free inference of population structure and local adaptation in a Bayesian hierarchical model.

Authors:  Eric Bazin; Kevin J Dawson; Mark A Beaumont
Journal:  Genetics       Date:  2010-04-09       Impact factor: 4.562

8.  Distinguishing positive selection from neutral evolution: boosting the performance of summary statistics.

Authors:  Kao Lin; Haipeng Li; Christian Schlötterer; Andreas Futschik
Journal:  Genetics       Date:  2010-11-01       Impact factor: 4.562

9.  Detecting directional selection in the presence of recent admixture in African-Americans.

Authors:  Kirk E Lohmueller; Carlos D Bustamante; Andrew G Clark
Journal:  Genetics       Date:  2010-12-31       Impact factor: 4.562

10.  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

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