Seqsim Alignment Simulation Example with Non-standard alphabetΒΆ

Section author: Julia Goodrich

This is an example of how to use PyCogent’s seqsim module to simulate an alignment where the alphabet is defined by the user for a simple tree starting with a random sequence and a random substitution rate matrix.

First we will perform the necessary imports.

>>> from cogent.seqsim.usage import Rates
>>> from cogent.core.alignment import DenseAlignment
>>> from cogent.seqsim.tree import RangeNode
>>> from cogent.parse.tree import DndParser
>>> from cogent.core.alphabet import CharAlphabet
>>> from cogent.seqsim.usage import Usage
>>> from cogent.core.sequence import ModelSequence

Now, lets specify a 4 taxon tree:

>>> t = DndParser('(a:0.4,b:0.3,(c:0.15,d:0.2)edge.0:0.1);',
... constructor = RangeNode)

Create the alphabet by passing in the characters to CharAlphabet then create tuples of all the possible pairs using ** operator

>>> Bases = CharAlphabet('ABCD')
>>> Pairs = Bases**2

Generate a random sequence with the new alphabet and a random rate matrix, Usage is being used to define character frequencies for the random sequence. Then we create a random sequence of length five.

>>> u = Usage({'A':0.5,'B':0.2,'C':0.15,'D':0.25}, Alphabet = Bases)
>>> s = ModelSequence(u.randomIndices(5))
>>> q = Rates.random(Pairs)

Set q at the base of the tree and propagate it to all nodes in the tree,

>>> t.Q = q
>>> t.propagateAttr('Q')

Set a P matrix from every Q matrix on each node,

>>> t.assignP()

Use evolve to evolve sequences for each tip, Note: must evolve sequence data, not sequence object itself (for speed)

>>> t.evolve(s._data)

Build alignment,

>>> seqs = {}
>>> for n in
...     seqs[n.Name] = ModelSequence(n.Sequence,Bases)
>>> aln = DenseAlignment(seqs,Alphabet=Bases)

The result is a Cogent Alignment object, which can be used the same way as any other alignment object.