| Long Description || MENDEL is a genetic accounting program that allows realistic numerical simulation of the mutation/selection process over time. MENDEL is applicable to either haploid or diploid organisms, having either sexual or clonal reproduction. Each mutation that enters the simulated population is tracked from generation to generation to the end of the experiment - or until that mutation is lost either as a result of selection or random drift. Using a standard personal computer, the MENDEL program can be used to generate and track millions of mutations within a single population.
MENDEL's input variables include such things as mutation rate, distribution specifications for mutation effects, extent of dominance, mating characteristics, selection method, average fertility, heritability, non-scaling noise, linkage block properties, chromosome number, genome size, population size, population sub-structure, and number of generations.
The MENDEL program outputs, both in tabular and graphic form, provide several types of data including: deleterious and beneficial mutation counts per individual, mean individual fitness as a function of generation count, distribution of mutation effects, and allele frequencies.
MENDEL provides biologists with a new tool for research and teaching, and allows for the modeling of complex biological scenarios that would have previously been impossible.
Mendel operates in Linux, Windows, and MacIntosh environments. Mendel is described in more detail in the following publication: John C. Sanford and Chase W. Nelson (2012). The Next Step in Understanding Population Dynamics: Comprehensive Numerical Simulation. In: Studies in Population Genetics, M. Carmen Fusté (Ed.), ISBN: 978-953-51-0588-6, InTech Available from: http://www.intechopen.com/books/studies-in-population-genetics/the-next-step-in-understanding-population-dynamics-comprehensive-numerical-simulation
Sanford J, Baumgardner J, Brewer W, Gibson P, ReMine W, Mendel's Accountant: a biologically realistic forward-time population genetics program, Scalable Computing: Practice and Experience, Jan. 1, 2007