We present an algorithm that automatically selects a partitioning scheme for a given alignment without the need for pre-defined subsets.
Then one run of the method involves only summing corresponding pre-calculated values for all 20 amino acids, and for a given alignment of ~100 sequences of length ~400 aa it takes approximately 50 ms (AMD Athlon™ 64 Processor 3800+).
Susko and Roger [ 57] developed an algorithm for constructing bins of amino acids in order to minimize compositional heterogeneity for a given alignment by minimizing the maximum chi-squared statistic for a taxon of the data set.
Moreover, Golubchik et al. ([3]) showed that the absence of amino acid residues often leads to an incorrect placement of gaps in the alignments, even when the sequences were otherwise identical, and, for a given alignment, not all amino acid positions will be aligned with equal confidence [4].
Furthermore, it implements a sequential, PThreads-parallelized and MPI-parallelized algorithm for computing all quartets or a subset of quartets for a given alignment.
When all models generated for a given alignment have been evaluated, the predictor score distribution of the formers is assigned to the latter.
For a given alignment identity x, let Ix be the set of target strains with which the first segment has identity at least x.
Once the maximum likelihood for all available models is estimated and merged into a file by PalmDaemon, statistical information of the parameters can be accessed to evaluate the fitness of a model for a given alignment.
For a given alignment, the entropy of an amino acid position H x) is defined as H x) = −∑ P x) log P x) where x is one of 20 amino acid residue types.
In phylogenetic model comparison, we wish to choose between two competing models as the better explanation for a given alignment.
