BayeScanAnalyze genetic data with this tool. | |
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BayeScan Ranking & Summary
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- License:
- Freeware
- Publisher Name:
- Matthieu Foll
- Operating Systems:
- Windows All
- File Size:
- 3.3 MB
BayeScan Tags
- analyzer analysis Analyze Genetic algorithm genetic image analyze data create genetic algorithm genetic data analyzer analyze genetic data genetic data analysis genetic data genetic change genetic Genetic System make genetic algorithm genetic sequence genetic information analyze fragmentation data analyze scientifi data analyze sampled data data analyze analyze genetic algorithm analyze alignment data analyze qPCR data analyze EEG data analyze kinship data Genetic code simulation Genetic code analyze chemotactical data genetic map creator create genetic map analyze ARISA data genetic algorithm component genetic operator genetic programming analyze qualitative data Genetic transduction pathway analyze data series genetic map genetic data sets analysis analyze genetic data sets recode genetic marker data analyze genetic variation two-allele genetic simulation simulate genetic map genetic map simulation genetic model genetic drift identify genetic barriers molecular genetic analyze comparative data identify candidate loci analyze data set analyze CSI data analyze genotyping data analyze genetic association genetic differentiation analyze GenePix data analyze MRI data display genetic code genetic optimizing
BayeScan Description
BayeScan is a simple, easy to use application specially designed to help you identify candidate loci under natural selection from genetic data, using differences in allele frequencies between populations. BayeScan is based on the multinomial-Dirichlet model. One of the simplest possible scenarios covered consists of an island model in which subpopulation allele frequencies are correlated through a common migrant gene pool from which they differ in varying degrees. The difference in allele frequency between this common gene pool and each subpopulation is measured by a subpopulation specific FST coefficient. Therefore, this formulation can consider realistic ecological scenarios where the effective size and the immigration rate may differ among subpopulations.
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