Developing and applying heterogeneous phylogenetic models with XRate.

Oscar Westesson,Ian Holmes

doi:10.1371/journal.pone.0036898

Abstract

Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the “grammar” of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate 's new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate 's flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART.

Highlights

Phylogenetics, the modeling of evolution on trees, is an extremely powerful tool in computational biology
Felsenstein’s ‘‘pruning’’ algorithm allows combining these models with phylogenetic trees to compute the likelihood of multiple sequences [4]
As powerful as phylogenetic models are for explaining the evolutionary depth of a sequence alignment, they are even more powerful when combined with a model for the feature structure: the partition of the alignment into regions, each evolving under a particular model

Summary

Introduction

Phylogenetics, the modeling of evolution on trees, is an extremely powerful tool in computational biology. XRate is a phylogenetic modeling program that implements the key parameterization and inference algorithms given two ingredients: a user-specified phylo-grammar, and a multiple sequence alignment. XRate ’s builtin macro language allows large, repetitive grammars to be compactly represented, and enables the model structure to depend on aspects of the data, such as the tree or alignment.

Results

Conclusion