FUBAR: a fast, unconstrained bayesian approximation for inferring selection.

Model-based analyses of natural selection often categorize sites into a relatively small number of site classes. Forcing each site to belong to one of these classes places unrealistic constraints on the distribution of selection parameters, which can result in misleading inference due to model misspecification. We present an approximate hierarchical Bayesian method using a Markov chain Monte Carlo (MCMC) routine that ensures robustness against model misspecification by averaging over a large number of predefined site classes.

Detecting individual sites subject to episodic diversifying selection.

The imprint of natural selection on protein coding genes is often difficult to identify because selection is frequently transient or episodic, i.e. it affects only a subset of lineages.

Non-negative matrix factorization for learning alignment-specific models of protein evolution.

Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g.