articleBioinformaticsJan 12, 2006BRONZE OA

LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters

Human Genome Sciences (United States) · University of Washington

PubMed
Indexed incrossrefdoajpubmed

Abstract

UNLABELLED: We present a Markov chain Monte Carlo coalescent genealogy sampler, LAMARC 2.0, which estimates population genetic parameters from genetic data. LAMARC can co-estimate subpopulation Theta = 4N(e)mu, immigration rates, subpopulation exponential growth rates and overall recombination rate, or a user-specified subset of these parameters. It can perform either maximum-likelihood or Bayesian analysis, and accomodates nucleotide sequence, SNP, microsatellite or elecrophoretic data, with resolved or unresolved haplotypes. It is available as portable source code and executables for all three major platforms. AVAILABILITY: LAMARC 2.0 is freely available at http://evolution.gs.washington.edu/lamarc

Citation impact

650
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FWCI
22.02
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100%
References
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Authors

1

Topics & keywords

Keywords
  • Coalescent theory
  • Markov chain Monte Carlo
  • Bayesian probability
  • Executable
  • Population
  • Markov chain
  • Maximum likelihood
  • Haplotype
UN Sustainable Development Goals
  • Reduced inequalities
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