Aug 20, 2015

SFS_CODE: More Efficient and Flexible Forward Simulations

BioRxiv : the Preprint Server for Biology
Ryan D Hernandez, Lawrence H. Uricchio

Abstract

SUMMARY: Modern implementations of forward population genetic simulations are efficient and flexible, enabling the exploration of complex models that may otherwise be intractable. Here we describe an updated version of SFS\_CODE, which has increased efficiency and includes many novel features. Among these features is an arbitrary model of dominance, the ability to simulate partial and soft selective sweeps, as well as track the trajectories of mutations and/or ancestries across multiple populations under complex models that are not possible under a coalescent framework. We also release sfs\_coder, a Python wrapper to SFS\_CODE allowing the user to easily generate command lines for common models of demography, selection, and human genome structure, as well as parse and simulate phenotypes from SFS\_CODE output. Availability and Implementation: Our open source software is written in C and Python, and are available under the GNU General Public License at http://sfscode.sourceforge.net. Contact: ryan.hernandez@ucsf.edu Supplementary information: Detailed usage information is available from the project website at http://sfscode.sourceforge.net.

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