Appropriate models for heterogeneous multi-gene data sites
Seminar Room 1, Newton Institute
Multigene data sets are becoming the norm for phylogenetic studies; so called phylogenomic datasets may involve hundreds of genes for many species. These data sets create challenges for current phylogenetic methods, as different genes have different functions and hence evolve under different processes. The question is how best to model this heterogeneity to give reliable phylogenetic estimates of the species tree.
Here we discuss two approaches. The first approach involves stochastic partitioning of genes into different classes, the second approach is a form of penalised maximum likelihood where the penalty term 'encourages' parameter estimates to be similar between different gene trees.
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