\
Given an alignment, a phylogenetic tree can be generated based on the pair-wise distance matrix computed from the alignment.
>>> from cogent import LoadSeqs, DNA
>>> from cogent.phylo import distance
>>> from cogent.evolve.models import F81
>>> aln = LoadSeqs('data/primate_brca1.fasta')
>>> d = distance.EstimateDistances(aln, submodel=F81())
>>> d.run()
The example above will use the F81 nucleotide substitution model and run the distance.EstimateDistances() method with the default options for the optimiser. To configure the optimiser a dictionary of optimisation options can be passed onto the run command. The example below configures the Powell optimiser to run a maximum of 10000 evaluations, with a maximum of 5 restarts (a total of 5 x 10000 = 50000 evaluations).
>>> dist_opt_args = dict(max_restarts=5, max_evaluations=10000)
>>> d.run(dist_opt_args=dist_opt_args)
>>> print d
============================================================================================
Seq1 \ Seq2 Galago HowlerMon Rhesus Orangutan Gorilla Human Chimpanzee
--------------------------------------------------------------------------------------------
Galago * 0.2112 0.1930 0.1915 0.1891 0.1934 0.1892
HowlerMon 0.2112 * 0.0729 0.0713 0.0693 0.0729 0.0697
Rhesus 0.1930 0.0729 * 0.0410 0.0391 0.0421 0.0395
Orangutan 0.1915 0.0713 0.0410 * 0.0136 0.0173 0.0140
Gorilla 0.1891 0.0693 0.0391 0.0136 * 0.0086 0.0054
Human 0.1934 0.0729 0.0421 0.0173 0.0086 * 0.0089
Chimpanzee 0.1892 0.0697 0.0395 0.0140 0.0054 0.0089 *
--------------------------------------------------------------------------------------------
Phylogenetic Trees can be built by using the neighbour joining algorithm by providing a dictionary of pairwise distances. This dictionary can be obtained either from the output of distance.EstimateDistances()
>>> from cogent.phylo import nj
>>> njtree = nj.nj(d.getPairwiseDistances())
>>> njtree = njtree.balanced()
>>> print njtree.asciiArt()
/-Rhesus
/edge.1--|
| | /-HowlerMon
| \edge.0--|
| \-Galago
-root----|
|--Orangutan
|
| /-Human
\edge.2--|
| /-Gorilla
\edge.3--|
\-Chimpanzee
Or created manually as shown below.
>>> dists = {('a', 'b'): 2.7, ('c', 'b'): 2.33, ('c', 'a'): 0.73}
>>> njtree2 = nj.nj(dists)
>>> print njtree2.asciiArt()
/-a
|
-root----|--b
|
\-c
We illustrate the phylogeny reconstruction by least-squares using the F81 substitution model. We use the advanced-stepwise addition algorithm to search tree space. Here a is the number of taxa to exhaustively evaluate all possible phylogenies for. Successive taxa will are added to the top k trees (measured by the least-squares metric) and k trees are kept at each iteration.
>>> import cPickle
>>> from cogent.phylo.least_squares import WLS
>>> dists = cPickle.load(open('data/dists_for_phylo.pickle'))
>>> ls = WLS(dists)
>>> stat, tree = ls.trex(a = 5, k = 5, show_progress = False)
Other optional arguments that can be passed to the trex method are: return_all, whether the k best trees at the final step are returned as a ScoredTreeCollection object; order, a series of tip names whose order defines the sequence in which tips will be added during tree building (this allows the user to randomise the input order).
We illustrate the phylogeny reconstruction using maximum-likelihood using the F81 substitution model. We use the advanced-stepwise addition algorithm to search tree space, setting
>>> from cogent import LoadSeqs, DNA
>>> from cogent.phylo.maximum_likelihood import ML
>>> from cogent.evolve.models import F81
>>> aln = LoadSeqs('data/primate_brca1.fasta')
>>> ml = ML(F81(), aln)
The ML object also has the trex method and this can be used in the same way as for above, i.e. ml.trex(). We don’t do that here because this is a very slow method for phylogenetic reconstruction.
A thorough description is Using phylogeny application controllers to construct phylogenetic trees from alignments.