MKPRF @ BioHPC

This program samples from the posterior distribution of parameters in the MKPRF models of Bustamante et. al (2002) and Barrier et. al (2003) using a Markov Chain Monte Carlo algorithm. The results are summarized via summary statistics of the posterior distribution. There is also an option for receiving the complete MCMC samples for plotting posterior distributions. For more information about the program click here.

Please note that MKPRF currently only uses polymorphism data from Species 1 in its calculations of selection. This is equivalent to having MK Tables with fixed differences called from the alignment using all individuals from species 1 and species 2, but polymorphism data only from Species 1. We expect to have a major release of MKPRF very soon that will allow joint modeling of polymorphism and divergence, multiple hits correction, and non-stationary demography. If you have any questions please email cdb28@cornell.edu (Carlos Bustamante) or arb83@cornell.edu (Adam Boyko).

Calculations will be carried out on the BioHPC compute cluster at CBSU. You will receive e-mail notifications when the job is submitted, when it starts, and when it is finished. Output will be available via links embedded in the notification e-mails. For more information about this program and BioHPC interface in general, please visit our Frequently Asked Questions page.

E-mail: (only guests need to use this field, CBSU registered users should log in)

Job name: (please, no spaces, special characters etc., uderscore is OK) Please notify me about MKPRF updates

Input file:
For more information on input file format click here, you can see a simple example file here.

Cluster: Max CPUs: ( Show timeout info )

Options: (see documentationfile for more information):

alphas: betas: alphar:

betar: mu0: sigmasqr0:

kappa0: nu0: sigma0:

chains: howmany: burnin:

step: delta_t: rho:

rhosd: CUTOFF_R: CUTOFF_S:

CHECK_POLY: FIXED_VARIANCE: OUTPUT_MCMC:

OUTPUT_THETA: USE_FAST_T: USE_GENOMIC_DS:

USE_GENOMIC_POLYDIV: Force parallel run - the program will run in parallel even if sample sizes are different when number of lines in the input file is greater than 100