__licence__ = "GPLv3"
__version__ = "0.0"
from ete2 import TreeStyle
from ete2 import EvolTree
from ete2 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/ete2-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."
def run_branch_test(cluster_name, treefile, alignment, folder_temp, folder_plots):
from ete2 import EvolTree
from ete2.treeview.layouts import evol_clean_layout
import os
from collections import defaultdict
import math
from scipy.stats import chi2
print "Processing cluster: " + cluster_name
tree = EvolTree(treefile)
tree.link_to_alignment(alignment, alg_format="fasta", nucleotides=True)
#Create temporal folder
temp_cluster_folder = folder_temp + "/" + cluster_name
if not os.path.exists(temp_cluster_folder):
os.makedirs(temp_cluster_folder)
tree.workdir = temp_cluster_folder
#Run M0 as the null model
tree.run_model("M0")
#Look at the site selection on each branch
printed_tree = 0
i = 0
#Output list with the results
output_list = []
for node in tree.iter_descendants():
#Mark the tree for the leaf under analysis
tree.mark_tree([node.node_id], marks=["#1"])
#Use the node id as folder name
temp_leaf_name = str(node.node_id)
print "Processing: " + cluster_name + " " + temp_leaf_name + " " + ",".join(node.get_leaf_names())
#Run computation of each model.
#From the notes on ETE:
# 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/ete2.../bsA.. directory)
tree.run_model("bsA." + temp_leaf_name)
tree.run_model("bsA1." + temp_leaf_name)
bsA = tree.get_evol_model("bsA." + temp_leaf_name)
bsA1 = tree.get_evol_model("bsA1." + temp_leaf_name)
ps_sites = defaultdict()
total_sites = 0
sites_over_95 = 0
for s in range(len(bsA.sites['BEB']['aa'])):
p_value_site = float(bsA.sites['BEB']['p2'][s])
if p_value_site > 0.50:
ps_sites[s] = [bsA.sites['BEB']['aa'][s], bsA.sites['BEB']['p2'][s]]
total_sites += 1
if p_value_site > 0.95:
sites_over_95 += 1
#ps = float(tree.get_most_likely("bsA." + temp_leaf_name, "bsA1." + temp_leaf_name))
rx = float(tree.get_most_likely("bsA1." + temp_leaf_name, "M0"))
lrt_value = 2 * math.fabs(bsA1.lnL - bsA.lnL) # LRT test value
ps = 1 - chi2.cdf(lrt_value, 1) # p-value based on chi-square
test_status = None
#Evidence of positive selection in the branch
omega_value = float(bsA.classes['foreground w'][2])
proportion_sites = float(bsA.classes['proportions'][2])
#Plot file
plot_file = folder_plots + "/" + cluster_name
if ps < 0.05 and omega_value > 1:
#Save plots, both in jpg and svg of the clusters with evidence of positive selection
test_status = "Positive"
if printed_tree == 0:
#tree.render(plot_file + ".svg", layout=evol_clean_layout)
#tree.render(plot_file + ".jpg", layout=evol_clean_layout)
printed_tree = 1
else:
continue
elif rx < 0.05 and ps >= 0.05:
test_status = "Relaxed"
else:
#print "no signal"
test_status = None
#Remove marks on the tree
tree.mark_tree(map(lambda x: x.node_id, tree.get_descendants()), marks=[''] * len(tree.get_descendants()),
verbose=False)
result_entry = [cluster_name, node.node_id, omega_value, proportion_sites, ps, test_status,
total_sites, sites_over_95, ",".join(node.get_leaf_names())]
# print result_entry
#print ps_sites
#node_results[node.node_id] = [result_entry, ps_sites]
output_list = [result_entry, ps_sites]
return output_list
def run_site_tests(cluster_name, treefile, alignment, folder_temp, folder_plots):
from ete2 import EvolTree
from ete2.treeview.layouts import evol_clean_layout
import os
from collections import defaultdict
import math
from scipy.stats import chi2
print "Processing cluster: " + cluster_name
tree = EvolTree(treefile)
tree.link_to_alignment(alignment, alg_format="fasta", nucleotides=True)
# Create temporal folder
temp_cluster_folder = folder_temp + "/" + cluster_name
if not os.path.exists(temp_cluster_folder):
os.makedirs(temp_cluster_folder)
tree.workdir = temp_cluster_folder
# Run M1 as the null model
tree.run_model("M1")
# Run M2 as the alternative model
tree.run_model("M2")
model1 = tree.get_evol_model("M1")
model2 = tree.get_evol_model("M2") # Get the results of the model
# Run the LRT test, using ETE
# pval = tree.get_most_likely("M2", "M1")
# Get the positive selected sites
ps_sites = defaultdict()
total_sites = 0
sites_over_95 = 0
for s in range(len(model2.sites["BEB"]["aa"])):
p_value_site = float(model2.sites["BEB"]["p2"][s])
if p_value_site > 0.50:
ps_sites[s] = [model2.sites["BEB"]["aa"][s], model2.sites["BEB"]["p2"][s]]
total_sites += 1
if p_value_site > 0.95:
sites_over_95 += 1
# LRT Test
lrt_value = 2 * math.fabs(model1.lnL - model2.lnL) # LRT test value
pval = 1 - chi2.cdf(lrt_value, 2) # p-value based on chi-square
test_status = None
# Evidence of positive selection in the branch
omega_value = float(model2.classes["w"][2])
proportion_sites = float(model2.classes["proportions"][2])
# Plot file
plot_file = folder_plots + "/" + cluster_name
col2 = {"NS": "black", "RX": "black", "RX+": "black", "CN": "black", "CN+": "black", "PS": "black", "PS+": "black"}
if pval < 0.05 and omega_value > 1:
# Save plots, both in jpg and svg of the clusters with evidence of positive selection
test_status = "Positive"
model2.set_histface(
up=False,
kind="curve",
colors=col2,
ylim=[0, 4],
hlines=[2.5, 1.0, 4.0, 0.5],
hlines_col=["orange", "yellow", "red", "cyan"],
errors=True,
)
tree.render(plot_file + ".svg", layout=evol_clean_layout, histfaces=["M2"])
# tree.render(plot_file + ".jpg", layout=evol_clean_layout, histfaces=['M2'])
else:
# print "no signal"
test_status = None
result_entry = [cluster_name, omega_value, proportion_sites, pval, test_status, total_sites, sites_over_95]
# print result_entry
# print ps_sites
# node_results[node.node_id] = [result_entry, ps_sites]
output_list = [result_entry, ps_sites]
return output_list
__licence__ = "GPLv3"
__version__ = "0.0"
from ete2 import EvolTree
tree = EvolTree ("data/S_example/measuring_S_tree.nw")
tree.link_to_alignment ('data/S_example/alignment_S_measuring_evol.fasta')
print tree
raw_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')
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')
print 'running M1 (all branches have the save value of omega)...'
tree.run_model ('M1')
#print 'running M3 (all branches have the save value of omega)...'
#tree.run_model ('M3')
print '''p-value that, in marked clade, we have one class of site
specifically evolving at a different rate:'''
print tree.get_most_likely ('bsC.137', 'M1')
#print 'p-value representing significance that omega is different of 1:'
#print tree.get_most_likely ('bsD.137', 'M3')
print 'The End.'
def run_site_tests(cluster_name, treefile, alignment, folder_temp,
folder_plots):
from ete2 import EvolTree
from ete2.treeview.layouts import evol_clean_layout
import os
from collections import defaultdict
import math
from scipy.stats import chi2
print "Processing cluster: " + cluster_name
tree = EvolTree(treefile)
tree.link_to_alignment(alignment, alg_format="fasta", nucleotides=True)
#Create temporal folder
temp_cluster_folder = folder_temp + "/" + cluster_name
if not os.path.exists(temp_cluster_folder):
os.makedirs(temp_cluster_folder)
tree.workdir = temp_cluster_folder
#Run M1 as the null model
tree.run_model("M1")
#Run M2 as the alternative model
tree.run_model("M2")
model1 = tree.get_evol_model("M1")
model2 = tree.get_evol_model("M2") # Get the results of the model
#Run the LRT test, using ETE
#pval = tree.get_most_likely("M2", "M1")
#Get the positive selected sites
ps_sites = defaultdict()
total_sites = 0
sites_over_95 = 0
for s in range(len(model2.sites['BEB']['aa'])):
p_value_site = float(model2.sites['BEB']['p2'][s])
if p_value_site > 0.50:
ps_sites[s] = [
model2.sites['BEB']['aa'][s], model2.sites['BEB']['p2'][s]
]
total_sites += 1
if p_value_site > 0.95:
sites_over_95 += 1
#LRT Test
lrt_value = 2 * math.fabs(model1.lnL - model2.lnL) # LRT test value
pval = 1 - chi2.cdf(lrt_value, 2) # p-value based on chi-square
test_status = None
#Evidence of positive selection in the branch
omega_value = float(model2.classes['w'][2])
proportion_sites = float(model2.classes['proportions'][2])
#Plot file
plot_file = folder_plots + "/" + cluster_name
col2 = {
'NS': 'black',
'RX': 'black',
'RX+': 'black',
'CN': 'black',
'CN+': 'black',
'PS': 'black',
'PS+': 'black'
}
if pval < 0.05 and omega_value > 1:
#Save plots, both in jpg and svg of the clusters with evidence of positive selection
test_status = "Positive"
model2.set_histface(up=False,
kind='curve',
colors=col2,
ylim=[0, 4],
hlines=[2.5, 1.0, 4.0, 0.5],
hlines_col=['orange', 'yellow', 'red', 'cyan'],
errors=True)
tree.render(plot_file + ".svg",
layout=evol_clean_layout,
histfaces=['M2'])
#tree.render(plot_file + ".jpg", layout=evol_clean_layout, histfaces=['M2'])
else:
#print "no signal"
test_status = None
result_entry = [
cluster_name, omega_value, proportion_sites, pval, test_status,
total_sites, sites_over_95
]
# print result_entry
#print ps_sites
#node_results[node.node_id] = [result_entry, ps_sites]
output_list = [result_entry, ps_sites]
return output_list
# 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')
raw_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)
p-value ofthis comparison is:''')
print tree.get_most_likely ('b_free.137', 'M0')
raw_input ('''
Now test if foreground rate is significantly different of 1.
(b_free with significantly better likelihood than b_neut)
if significantly different, and higher than one, we will be under
__licence__ = "GPLv3"
__version__ = "0.0"
from ete2 import EvolTree
tree = EvolTree("data/S_example/measuring_S_tree.nw")
tree.link_to_alignment('data/S_example/alignment_S_measuring_evol.fasta')
print tree
raw_input('\n tree and alignment loaded\nHit some key, to start computation of branch site models A and A1 on each branch.\n')
print 'running model M0, for comparison with branch-site models...'
tree.run_model('M0')
# each node/leaf has two kind of identifiers node_id and paml_id, to mark nodes we have to specify
# the node_id of the nodes we want to mark, and the kind of mark in this way:
for leaf in tree:
leaf.node_id
print '\n---------\nNow working with leaf ' + leaf.name
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/ete2.../bsA.. directory)
print 'running model bsA and bsA1'
tree.run_model('bsA.'+ leaf.name)
tree.run_model('bsA1.' + leaf.name)
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: '
print fb.get_ctrl_string()
raw_input ('hit some key...')
print 'Have a look to run message of codeml: '
print fb.run
raw_input ('hit some key...')
print 'Have a look to log likelihood value of this model, and number of parameters:'
