Penalized likelihood phylogenetic inference: bridging the parsimony-likelihood gap.
The increasing diversity and heterogeneity of molecular data for phylogeny estimation has led to development of complex models and model-based estimators. Here, we propose a penalized likelihood (PL) framework in which the levels of complexity in the underlying model can be smoothly controlled. We demonstrate the PL framework for a four-taxon tree case and investigate its properties. The PL framework yields an estimator in which the majority of currently employed estimators such as the maximum-parsimony estimator, homogeneous likelihood estimator, gamma mixture likelihood estimator, etc., become special cases of a single family of PL estimators. Furthermore, using the appropriate penalty function, the complexity of the underlying models can be partitioned into separately controlled classes allowing flexible control of model complexity.
Department of Biology and Penn Genome Frontiers Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA. junhyong@sas.upenn.edu
Junhyong
J
Kim
Michael J
MJ
Sanderson
D000818:Animals; D001499:Bayes Theorem; D004330:Drosophila / Q000235:genetics; D019143:Evolution, Molecular; D014644:Genetic Variation; D016013:Likelihood Functions; D008957:Models, Genetic; D010802:Phylogeny