Phylogeny and molecular evolution of green algae - Phycology ...

16 CHAPTER 1
Treefinder). In the second step log likelihoods of the guide tree under different partitioning strategies
and models are calculated. Subsequently, the corresponding AIC or BIC scores are calculated and
compared. The condition with the lowest AIC and/or BIC score is chosen for phylogenetic analysis.
Alternatively, Bayes factors (Nylander et al. 2004) can be used to compare different partitioning
strategies and models. For each tested condition a separate Bayesian analyses has to be run which
implies high computational times. This makes it unrealistic to compare many partitioning strategies
and models in a Bayesian framework.
Complex models of sequence evolution
The secondary structure of ribosomal RNA consists of loops and stems. The nucleotides in the stem
regions form base pairs and are interdependent because a change on one side of the stem has to be
compensated in the other side of stem to avoid malfunction of the molecule. Since models of
sequence evolution have to approach real evolution as close by as possible, it is recommended to
incorporate this site interdependence in the model. This can be done by partitioning the ribosomal
RNA into loops and stems and using a doublet model for the stem regions (Schöniger and Von
Haeseler 1994). However, the use of a doublet model is computational demanding.
Instead of partitioning protein coding genes into codon positions, a codon substitution model can be
applied. In this model, nucleotide triplets are considered as a single character and changes from one
triplet to another one are considered taking into account that some changes are more likely than
others (e.g. synonymous versus non-synonymous substitution). Although codon substitution models
are a more realistic approximation of protein sequence evolution than codon position models, they
come with a very high computational cost, hindering their use for large datasets (Shapiro et al. 2006).
Mixture models
Mixture models offer an attractive alternative to data partitioning and applying different models to
the partitions. Whereas a partitioned analysis assumes that all sites within a partition arose from a
single evolutionary process, mixture models relax this assumption by not expecting any prior
partitioning and applying a set of different models to each site in the alignment. The log likelihood of
each site is calculated as a weighted sum of the log likelihoods of each model for that site. The model
weights correspond to the probability that the site has evolved under the model in question. Mixture
models can thus apply different rate matrices to different parts of the dataset without explicitly
partitioning it (Pagel and Meade 2004, Venditti et al. 2008). This is an elegant way to incorporate
across site heterogeneity in the evolutionary process because it does not require prior knowledge
about differences of evolutionary processes between different parts of the dataset and it avoids
problems associated with differences of the evolutionary process within partitions that are defined a
priori. Although analyses using mixture models outperform analyses based on partitioned datasets,
they are restrictively time-consuming for large datasets.