def main():
"""
main function
"""
tree = EvolTree(WRKDIR + 'tree.nw')
tree.workdir = 'data/protamine/PRM1/paml/'
random_swap(tree)
tree.link_to_evol_model(WRKDIR + 'paml/fb/fb.out', 'fb')
check_annotation(tree)
tree.link_to_evol_model(WRKDIR + 'paml/M1/M1.out', 'M1')
tree.link_to_evol_model(WRKDIR + 'paml/M2/M2.out', 'M2')
tree.link_to_evol_model(WRKDIR + 'paml/M7/M7.out', 'M7')
tree.link_to_evol_model(WRKDIR + 'paml/M8/M8.out', 'M8')
tree.link_to_alignment(WRKDIR + 'alignments.fasta_ali')
print 'pv of LRT M2 vs M1: ',
print tree.get_most_likely('M2', 'M1')
print 'pv of LRT M8 vs M7: ',
print tree.get_most_likely('M8', 'M7')
tree.show(histfaces=['M2'])
print 'The End.'
def main():
"""
main function
"""
tree = EvolTree (WRKDIR + 'tree.nw')
tree.workdir = 'data/protamine/PRM1/paml/'
random_swap(tree)
tree.link_to_evol_model (WRKDIR + 'paml/fb/fb.out', 'fb')
check_annotation (tree)
tree.link_to_evol_model (WRKDIR + 'paml/M1/M1.out', 'M1')
tree.link_to_evol_model (WRKDIR + 'paml/M2/M2.out', 'M2')
tree.link_to_evol_model (WRKDIR + 'paml/M7/M7.out', 'M7')
tree.link_to_evol_model (WRKDIR + 'paml/M8/M8.out', 'M8')
tree.link_to_alignment (WRKDIR + 'alignments.fasta_ali')
print 'pv of LRT M2 vs M1: ',
print tree.get_most_likely ('M2','M1')
print 'pv of LRT M8 vs M7: ',
print tree.get_most_likely ('M8','M7')
tree.show (histfaces=['M2'])
print 'The End.'
pass
input ('\n tree and alignment loaded\n Hit some key, to start computation of site models M1 and M2.\n')
print ('running model M1')
tree.run_model ('M1')
print ('running model M2')
tree.run_model ('M2')
print ('\n\n comparison of models M1 and M2, p-value: ' + str(tree.get_most_likely ('M2','M1')))
#tree.show()
print ('by default the hist represented is this one:')
tree.show (histfaces=['M2'])
print ('but we can choose between many others...')
model2 = tree.get_evol_model ('M2')
col2 = {'NS' : 'black', 'RX' : 'black',
'RX+': 'black', 'CN' : 'black',
'CN+': 'black', 'PS' : 'black', 'PS+': 'black'}
model2.set_histface (up=False, kind='curve', colors=col2, ylim=[0,4], hlines = [2.5, 1.0, 4.0, 0.5], header = 'Many lines, error boxes, background black',
hlines_col=['orange', 'yellow', 'red', 'cyan'], errors=True)
tree.show(histfaces=['M2'])
marks = ['1', '3', '7']
tree.mark_tree (marks, ['#1'] * 3)
print (tree.write ())
print ('we can easily colorize marked branches')
# display marked branches in orange
for node in tree.traverse ():
if not hasattr (node, 'mark'):
continue
if node.mark == '':
continue
node.img_style = NodeStyle ()
node.img_style ['bgcolor'] = '#ffaa00'
tree.show()
print ('''now running branch models
free branch models, 2 groups of branches, one with Gorilla and
chimp, the other with the rest of the phylogeny
''')
print ('running branch free...')
tree.run_model ('b_free.137')
print ('running branch neut...')
tree.run_model ('b_neut.137')
print ('running M0 (all branches have the save value of omega)...')
tree.run_model ('M0')
input ('''Now we can do comparisons...
Compare first if we have one or 2 rates of evolution among phylogeny.
LRT between b_free and M0 (that is one or two rates of omega value)
from ete3 import EvolTree
tree = EvolTree ("data/S_example/measuring_S_tree.nw")
print tree
raw_input ('\n tree loaded, hit some key.\n')
print 'Now, it is necessary to link this tree to an alignment:'
tree.link_to_alignment ('data/S_example/alignment_S_measuring_evol.fasta')
raw_input ('\n alignment loaded, hit some key to see.\n')
tree.show()
print '''
we will run free-ratio model that is one of models available through
function run_model:
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
'''
print tree.run_model.__doc__ +'\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
tree.run_model ('fb.example')
raw_input ('free-ratio model runned, all results are store in a Model object.')
fb = tree.get_evol_model('fb.example')
print 'Have a look to the parameters used to run this model on codeml: '
from ete3 import TreeStyle
from ete3 import EvolTree
from ete3 import faces
tree = EvolTree("data/S_example/measuring_S_tree.nw")
tree.link_to_alignment('data/S_example/alignment_S_measuring_evol.fasta')
print(tree)
print('\n Running free-ratio model with calculation of ancestral sequences...')
tree.run_model('fb_anc')
#tree.link_to_evol_model('/tmp/ete3-codeml/fb_anc/out', 'fb_anc')
I = TreeStyle()
I.force_topology = False
I.draw_aligned_faces_as_table = True
I.draw_guiding_lines = True
I.guiding_lines_type = 2
I.guiding_lines_color = "#CCCCCC"
for n in sorted(tree.get_descendants() + [tree], key=lambda x: x.node_id):
if n.is_leaf(): continue
anc_face = faces.SequenceFace(n.sequence, 'aa', fsize=10, bg_colors={})
I.aligned_foot.add_face(anc_face, 1)
I.aligned_foot.add_face(
faces.TextFace('node_id: #%d ' % (n.node_id), fsize=8), 0)
print('display result of bs_anc model, with ancestral amino acid sequences.')
tree.show(tree_style=I)
print('\nThe End.')
)
marks = ['1', 3, '7']
tree.mark_tree(marks, ['#1'] * 3)
print(tree.write())
# display marked branches in orange
for node in tree.traverse():
if not hasattr(node, 'mark'):
continue
if node.mark == '':
continue
node.img_style = NodeStyle()
node.img_style['bgcolor'] = '#ffaa00'
tree.show()
print('''now running branch-site models C and D that represents
the addition of one class of sites in on specific branch.
These models must be compared to null models M1 and M3.
if branch-site models are detected to be significantly better,
than, one class of site is evolving at different rate in the marked
clade.
''')
# TODO: re-enable model M3
print('running branch-site C...')
tree.run_model('bsC.137')
#print ('running branch-site D...')
#tree.run_model ('bsD.137')
tree.mark_tree([leaf.node_id], marks=['#1'])
print (tree.write())
# to organize a bit, we name model with the name of the marked node
# any character after the dot, in model name, is not taken into account
# for computation. (have a look in /tmp/ete3.../bsA.. directory)
print ('running model bsA and bsA1')
tree.run_model('bsA.'+ leaf.name)
tree.run_model('bsA1.' + leaf.name)
print ('p-value of positive selection for sites on this branch is: ')
ps = tree.get_most_likely('bsA.' + leaf.name, 'bsA1.'+ leaf.name)
rx = tree.get_most_likely('bsA1.'+ leaf.name, 'M0')
print (str(ps))
print ('p-value of relaxation for sites on this branch is: ')
print (str(rx))
model = tree.get_evol_model("bsA." + leaf.name)
if ps < 0.05 and float(model.classes['foreground w'][2]) > 1:
print ('we have positive selection on sites on this branch')
tree.show(histfaces=['bsA.' + leaf.name])
elif rx<0.05 and ps>=0.05:
print ('we have relaxation on sites on this branch')
else:
print ('no signal detected on this branch, best fit for M0')
print ('\nclean tree, remove marks')
tree.mark_tree(map(lambda x: x.node_id, tree.get_descendants()),
marks=[''] * len(tree.get_descendants()), verbose=True)
# nothing working yet to get which sites are under positive selection/relaxation,
# have to look at the main outfile or rst outfile
print ('The End.')
from ete3 import faces
tree = EvolTree ("data/S_example/measuring_S_tree.nw")
tree.link_to_alignment ('data/S_example/alignment_S_measuring_evol.fasta')
print tree
print '\n Running free-ratio model with calculation of ancestral sequences...'
tree.run_model ('fb_anc')
#tree.link_to_evol_model('/tmp/ete3-codeml/fb_anc/out', 'fb_anc')
I = TreeStyle()
I.force_topology = False
I.draw_aligned_faces_as_table = True
I.draw_guiding_lines = True
I.guiding_lines_type = 2
I.guiding_lines_color = "#CCCCCC"
for n in sorted (tree.get_descendants()+[tree],
key=lambda x: x.node_id):
if n.is_leaf(): continue
anc_face = faces.SequenceFace (n.sequence, 'aa', fsize=10, bg_colors={})
I.aligned_foot.add_face(anc_face, 1)
I.aligned_foot.add_face(faces.TextFace('node_id: #%d '%(n.node_id),
fsize=8), 0)
print 'display result of bs_anc model, with ancestral amino acid sequences.'
tree.show(tree_style=I)
print '\nThe End.'
06 Feb 2011
use slr to compute evolutionary rates
"""
__author__ = "Francois-Jose Serra"
__email__ = "*****@*****.**"
__licence__ = "GPLv3"
__version__ = "0.0"
from ete3 import EvolTree
tree = EvolTree ("data/S_example/measuring_S_tree.nw")
tree.link_to_alignment ("data/S_example/alignment_S_measuring_evol.fasta")
tree.run_model ('SLR')
slr = tree.get_evol_model ('SLR')
slr.set_histface (up=False, kind='curve',errors=True,
hlines = [1.0,0.3], hlines_col=['black','grey'])
tree.show (histfaces=['SLR'])
'can be run from within a paml directory' from ete3 import EvolTree import os tree_file = "testTree.tre" alignment_file = "testAlignment.fasta" model = "./model/out" model_name = "bsD.bl_0.2w" # model_name = os.path.basename(os.getcwd()) testTree = EvolTree(tree_file) testTree.link_to_alignment(alignment_file) testTree.link_to_evol_model(model, model_name) testTree.show()
print(tree.write())
# to organize a bit, we name model with the name of the marked node
# any character after the dot, in model name, is not taken into account
# for computation. (have a look in /tmp/ete3.../bsA.. directory)
print('running model bsA and bsA1')
tree.run_model('bsA.' + leaf.name)
tree.run_model('bsA1.' + leaf.name)
print('p-value of positive selection for sites on this branch is: ')
ps = tree.get_most_likely('bsA.' + leaf.name, 'bsA1.' + leaf.name)
rx = tree.get_most_likely('bsA1.' + leaf.name, 'M0')
print(str(ps))
print('p-value of relaxation for sites on this branch is: ')
print(str(rx))
model = tree.get_evol_model("bsA." + leaf.name)
if ps < 0.05 and float(model.classes['foreground w'][2]) > 1:
print('we have positive selection on sites on this branch')
tree.show(histfaces=['bsA.' + leaf.name])
elif rx < 0.05 and ps >= 0.05:
print('we have relaxation on sites on this branch')
else:
print('no signal detected on this branch, best fit for M0')
print('\nclean tree, remove marks')
tree.mark_tree(map(lambda x: x.node_id, tree.get_descendants()),
marks=[''] * len(tree.get_descendants()),
verbose=True)
# nothing working yet to get which sites are under positive selection/relaxation,
# have to look at the main outfile or rst outfile
print('The End.')
#tree.run_model("bsA." + chimaeriformes)
#tree.mark_tree([leaf.node_id], marks = ["#1"])
print("Running")
print(tree.write())
tree.run_model('bsA.Chimaeriformes')
tree.run_model("bsA1.Chimaeriformes")
print('p-value of positive selection for sites on this branch is: ')
ps = tree.get_most_likely('bsA.Chimaeriformes', 'bsA1.Chimaeriformes')
print(str(ps))
rx = tree.get_most_likely('bsA1.Chimaeriformes', 'M0')
print(str(rx))
model = tree.get_evol_model("bsA.Chimaeriformes")
if ps < 0.05 and float(model.classes['foreground w'][2]) > 1:
print('we have positive selection on sites on this branch')
tree.show(histfaces=['bsA1.Chimaeriformes'])
elif rx < 0.05 and ps >= 0.05:
print('we have relaxation on sites on this branch')
else:
print('no signal detected on this branch, best fit for M0')
#tree.show(histfaces=['bsA1.'])
for models in tree._models:
print(tree.get_evol_model(models))
from _pickle import dump
#out = open('my_tree.pik', 'w')
#dump(tree, out)
#out.close()