Novel Heterogeneous Maximum Likelihood Methods for The Detection of Adaptive Evolution.
Jennifer Commins1, Dr. James O. McInerney 2
1jennifer.commins@may.ie, NUI, Maynooth; 2james.o.mcinerney@may.ie, NUI, Maynooth
Maximum Likelihood (ML) methods are
useful and widely used in the analysis of molecular evolution. In particular ML
methods have become very popular in order to analyse sequences for signatures
of adaptive evolution. Of particular importance is the choice of the
evolutionary model in order to describe the data. With this in mind we have
designed new methods for robustly inferring the evolutionary history of extant
sequences and for precisely identifying signatures of adaptive evolution. In
our approach we require the minimum of user intervention in order to find
adaptive evolution events. A phylogenetic tree will be constructed from a
sequence alignment and assumed to be correct, for each internal node of the
tree; we evaluate silent (Ds) and replacement (Dn) substitutions between it and
adjoining nodes, both ancestral and descendent (ML assumes that it is possible
that every character-state can be found at every ancestral node). Also to be
evaluated is whether or not these replacements remained invariable or whether
they changed elsewhere in the phylogeny, the same kind of analysis is carried
out on the silent substitutions. In this way, a path through the tree is
maximised so that it represents the greatest difference in the behaviour of the
silent and replacement substitutions. This can then be tested either for
deviation from Dn:Ds ratio, or any other parameter that might indicate
selection. This approach directly addresses claims that current ML methods are
sensitive to violation of the assumptions regarding which model of evolution to
use and how closely it approximates to reality and that in some cases the
methods produce false-positive results when subjected to differing conditions.
The software (on completion) will be tested on both real and fake data sets,
where the likelihood can be verified using the existing Adaptive Evolution
database. The end product of this project will be a software product that is
capable of performing analyses of multiple sequence alignments in ways that are
closer to biological reality than the existing methods. The software will be
made freely available once completed at http://bioinf.may.ie/likelihood.