Here, we introduce a series of empirically determined profile mixture models, with number of components ranging from 20 to 60. To draw a parallel with standard models, we only implemented the equivalent of the GTR approach, which means that the model could be applied only on large datasets. In addition, thus far, no empirical information was stored a priori in the model concerning the shapes of the profiles. However, such profile mixture models were introduced only in a Bayesian context, and were not available in a Maximum Likelihood framework. They perform particularly well on saturated data, and for that reason, are more robust to phylogenetic artefacts due to the presence of fast evolving species in the dataset ( Lartillot et al, 2007). In several instances, we showed that such mixture models provide a better fit than standard models. And to each class is associated a probability profile over the 20 amino-acids. Through the underlying mixture, the model implicitely clusters sites according to their class of biochemical constraint (hydrophobic, polar, positively charged, etc.). Such mixture models explicitely account for the fact that distinct sites are under distinct evolutionary pressures. Over the last few years, we proposed a simple alternative to empirical rate matrices, by using mixtures of stationary probability profiles ( Lartillot and Philippe, 2004). Such pre-learnt empirical matrices are available from several sources (WAG, JTT, LG).Īn alternative approach : profile mixture models
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |