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Bioinformatics Advance Access published online on January 12, 2006
Bioinformatics, doi:10.1093/bioinformatics/btk051
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received November 15, 2005
Revised January 9, 2006
Accepted January 5, 2006

Applications note

LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters

Mary K. Kuhner 1 *

1 Department of Genome Sciences, Box 357730, University of Washington, Seattle, WA 98195-7730, USA

* To whom correspondence should be addressed.
Mary K. Kuhner, E-mail: lamarc{at}gs.washington.edu


  Abstract

Summary: 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} = 4Neµ, 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.


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