SPLATCHE is a spatially explicit simulation framework in population genetics. It may be used to generate the expected genetic diversity under a wide range of evolutionary scenarios including population dynamics in space and time. The program may thus serve for exploring and testing null hypotheses and doing model choice and parameter estimation if integrated into an Approximate Bayesian Computation (ABC) framework or equivalent approach. Several versions have been published so far, with different features.

May 2019: New Version 3.0

Access here current version !

  • The web page of version 3 can be found here.
  • Currat M, Arenas M, Quilodran CS, Excoffier L, Ray N (2019) SPLATCHE3: simulation of serial genetic data under spatially explicit evolutionary scenarios including long-distance dispersal. Bioinformatics 2019. PDF article.

Previous versions

January 2012: Version 2.01

Not updated anymore!

  • The web page of version 2 can be found here.
  • Ray N, Currat M, Foll M, Excoffier L (2010) SPLATCHE2: a spatially-explicit simulation framework for complex demography, genetic admixture and recombination. Bioinformatics 2010, Vol 26(3): 2993-2994. PDF article.
  • See a list of the publications citing SPLATCHE version 2 (listed in Google Scholar)

May 2005: Version 1.1

Not updated anymore!

  • The web page of version 1 can be found here.
  • Currat M, Ray N, Excoffier L (2004) SPLATCHE: a program to simulate genetic diversity taking into account environmental heterogeneity. Molecular Ecology Notes 4(1): 139-142. PDF article.
  • See a list of the publications citing SPLATCHE version 1 (listed in Google Scholar)

Main features

  • Spatially explicit simulations in population genetics
  • Simulation of unlinked, partially linked or fully linked genetic loci, using a recombination model.
  • Different kinds of molecular markers (DNA sequences, SNP, RFLP and STR).
  • Effect of environmental heterogeneity and fluctuations on population demography and migration.
  • Competition and admixture between two coexisting populations.
  • Serial sampling of genetic data.
  • Long-distance dispersal (LDD)
  • Various mutation models
  • Runs on various OS.