Migrate estimates effective population sizes and past migration rates between n population assuming a migration matrix model with asymmetric migration rates and different subpopulation sizes. Migrate uses maximum likelihood or Bayesian inference to jointly estimate all parameters. It can use the following data types:
- Sequence data using Felsenstein's 84 model with or without site rate variation,
- Single nucleotide polymorphism data (sequence-like data input, HAPMAP-like data input)
- Microsatellite data using a stepwise mutation model or a brownian motion mutation model (using the repeatlength input format or the fragment-length input format),
- Electrophoretic data using an 'infinite' allele model.
$ module spider migraten to find out what environment modules are available for this application.
For the serial binary:
$ module load migraten/4.4.4
Parallel Module (MPI)
For the MPI binary:
$ module load intel/2020 openmpi/4.1.1 migraten/5.0.4
- HPC_MIGRATEN_DIR - installation directory
- HPC_MIGRATEN_BIN - executable directory
- HPC_MIGRATEN_MAN - manual directory
A manual page is available. Run man migrate to view it after loading the appropriate module.
Full manual is available from the Migrate-n website.
Serial command example:
$ migrate-n parmfile -nomenu
Parallel command example:
$ mpiexec migrate-n-mpi parmfile -nomenu
If you publish research that uses migrate-n you have to cite it as follows:
Maximum likelihood: Beerli 1998; Beerli and Felsenstein 1999, 2001.
Bayesian inference: Beerli 2006; Beerli and Felsenstein 2001.
Missing population issues: Beerli 2004.
General use of migrate: Beerli 2009.
Bayes factor and marginal likelihood: Beerli and Palczewski 2010.
Beerli, P. (1998) Estimation of migration rates and population sizes in geographically structured populations. In: Advances in molecular ecology (Ed. G. Carvalho). NATO-ASI workshop series. IOS Press, Amsterdam. Pp. 39-53.
Beerli, P. and J. Felsenstein (1999) Maximum-likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach. Genetics, 152(2):763–73, 1999
Beerli, P. and J. Felsenstein (2001) Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proceedings of the National Academy of Sciences of the USA, 98(8):4563–4568.
Beerli, P. (2004) Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations. Molecular Ecology, 13:827–836.
Beerli, P. (2006) Comparison of Bayesian and maximum likelihood inference of population genetic parameters. Bioinformatics, 22(3):341–345.
Beerli, P. (2009) How to use migrate or why are markov chain monte carlo programs difficult to use? In G. Bertorelle, M. W. Bruford, H. C. Hauffe, A. Rizzoli, and C. Vernesi, editors, Population Genetics for Animal Conservation, volume 17 of Conservation Biology, pages 42–79. Cambridge University Press, Cambridge UK, 2009.
Beerli, P. and M. Palczewski (2010) Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics, 185:313–326