def showTreeWithPictures(tree = None, alignment=None, branchLengths=True, bootstrapSupport=True, tolabel=None):
print("ShowTreeWithPictures",tree, alignment, branchLengths,bootstrapSupport, tolabel)
if alignment:
t = EvolTree(tree, alignment,alg_format="paml")
t.link_to_alignment(alignment,alg_format="paml")
else:
t = EvolTree(tree)
nsFG = NodeStyle()
nsFG["fgcolor"] = "darkgreen"
nsFG["size"] = 8
for node in t.traverse():
print(node.node_id)
if tolabel:
if str(node.node_id) in tolabel:
node.set_style(nsFG)
ts = TreeStyle()
ts.show_leaf_name = True
ts.show_branch_length = branchLengths
ts.show_branch_support = bootstrapSupport
out = FILE
if branchLengths:
out+="_Len"
if bootstrapSupport:
out+="_Boot"
t.render(out+"_tree.pdf",tree_style=ts)
t.render(out+"_tree.png",tree_style=ts)
if INTERACTIVE:
t.show(tree_style=ts)
def labelForPaml(unlabelledTreeAsString,listOfNodes, tree):
t = EvolTree(unlabelledTreeAsString)
marks = []
count = 1
for i in listOfNodes:
marks.append("#"+str(count))
count+=1
t.mark_tree(listOfNodes, marks=marks)
print(t.write())
outfile = tree+"."+"_".join(listOfNodes)
with open(outfile, 'w') as out:
out.write(t.write())
def showTreeNodes(unlabelledTreeAsString):
t = EvolTree(unlabelledTreeAsString)
for node in t.traverse():
#print(node)
#print(node.node_id, node.name)
#if (node.name.split("_")[0] in GENES):
# print(node.name, node.node_id)
leaves = node.get_leaf_names()
#leaves = [l for l in leaves if l.split("_")[0] in GENES ]
if leaves !=[]:
print(node)
print(leaves, node.name, node.node_id)
print("\n")
def showAlignmentWithTree(unlabelledTreeAsString,alignment):
t = EvolTree(unlabelledTreeAsString, alignment,alg_format="paml")
t.link_to_alignment(alignment, alg_format="paml")
for node in t.traverse():
print(node)
print(node.node_id, node.name)
print(t.write())
#print(t)
t.show() #layout=evol_clean_layout)
def showAlignmentWithTree(tree,alignment):
print(tree)
t = EvolTree(tree, alignment,alg_format="paml")
nsFG = NodeStyle()
nsFG["fgcolor"] = "darkgreen"
nsFG["size"] = 15
#print(t)
#t.run_model ('fb.example')
# t.show()
t.link_to_alignment(alignment, alg_format="paml")
for node in t.traverse():
print(node)
#if (node.name.split("_")[0] in GENES):
# print(node.name, node.node_id)
# if (node.name.split("_")[0] == "GRMZM2G083841"):
# node.add_face(ImgFace("83841.1.png", height=50, width=50), column=1, position="aligned")
# if (node.name.split("_")[0] == "GRMZM2G473001"):
# node.add_face(ImgFace("473001.png", height=50, width=50), column=1, position="aligned")
node.add_face(TextFace(str(node.node_id)),column=0)
#node.add_face(ImgFace("tux.png", height=50), column=1)
# node.set_style(nsFG)
leaves = node.get_leaf_names()
#leaves = [l for l in leaves if l.split("_")[0] in GENES ]
if leaves !=[]:
print(node.name, node.node_id)
#print(node.node_id)
#t.mark_tree([8], marks=["#1"])
#print(t.write())
#print(alignment)
#print(t)
#t.show() #layout=evol_clean_layout)
ts = TreeStyle()
ts.show_leaf_name = True
ts.show_branch_length = True
ts.show_branch_support = True
t = fakeUnroot(t)
t.show(tree_style=ts)
t.render("tree.pdf", tree_style=ts)
def showAlignmentWithTree(unlabelledTreeAsString,alignment):
t = EvolTree(unlabelledTreeAsString, alignment,alg_format="paml")
#print(t)
#t.run_model ('fb.example')
# t.show()
t.link_to_alignment(alignment, alg_format="paml")
for node in t.traverse():
print(node)
print(node.node_id, node.name)
#t.mark_tree([8], marks=["#1"])
#print(t.write())
print(alignment)
#print(t)
t.show() #layout=evol_clean_layout)
example of computation and display of an ancestral sequence
computed under free'ratio model.
"""
__author__ = "Francois-Jose Serra"
__email__ = "*****@*****.**"
__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"
def showTreeNodes(unlabelledTreeAsString):
t = EvolTree(unlabelledTreeAsString)
for node in t.traverse():
print(node)
print(node.node_id, node.name)
print "Alignment missing: " + align_file
no_results_file.write(cluster + "\n")
#Check alignment length. If only two sequences, move to the next one
fasta_count = 0
for line in open(align_file, 'r'):
line = line.strip()
if line.startswith(">"):
fasta_count += 1
if not fasta_count > 2:
continue
node_id_2_names = defaultdict()
for descend in EvolTree(tree_file).iter_descendants():
node_id_2_names[descend.node_id] = descend.get_leaf_names()
#Results, the first element has:
#The second is a dictionary with the positive selected sites
#results_dict[cluster] = run_site_branch(cluster, tree_file, align_file, temp_folder, plot_folder)
p = pool.apply_async(run_site_branch,
args=(
cluster,
tree_file,
align_file,
temp_folder,
plot_folder,
),
"""
15 Nov 2010
example to illustrate use of sites model, displaying and comparison
"""
__author__ = "Francois-Jose Serra"
__email__ = "*****@*****.**"
__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()
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
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 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
"""
15 Nov 2010
first example, load a tree and compute free ratios model,
to find omega value of each branch.
"""
__author__ = "Francois-Jose Serra"
__email__ = "*****@*****.**"
__licence__ = "GPLv3"
__version__ = "0.0"
from ete2 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
def labelForPamlRegex(unlabelledTreeAsString, regex, tree):
pattern = re.compile(regex)
t = EvolTree(unlabelledTreeAsString)
marks = []
count = 1
outfiles = []
#nsFG=TreeStyle()
nsMatch = NodeStyle() #match
nsMatch["fgcolor"] = "blue"
nsMatch["size"] = 10
nsBG = NodeStyle()
nsBG["fgcolor"] = "black"
nsBG["size"] = 0
nsFG = []
tolabelreg = []
for i in range(0,MAX_PARENT):
nsFG.append( NodeStyle())
nsFG[i]["size"] = 10
nsFG[i]["fgcolor"] = NODE_COLORS[i]
isroot=True
for node in t.traverse():
node.set_style(nsBG)
if node.is_root():
print("root")
node.unroot()
node._support = None
for node in t.get_descendants():
node.add_face(TextFace(node.node_id), column=0)
#traverse and match
for node in t.traverse():
if re.match(pattern, node.name):
print("MATCH", node.name, node.node_id)
node.set_style(nsMatch)
n = node
try:
for i in range(0,MAX_PARENT):
n = n.up
n.set_style(nsFG[i])
marks.append("#"+str(count))
print(count)
t.mark_tree([str(count)], marks=marks)
#just label everything with #1
print(t.write())
tolabelreg.append(str(n.node_id))
outfile = tree+"."+"_".join([str(n.node_id)])
with open(outfile, 'w') as out:
out.write(t.write())
outfiles.append(outfile)
except AttributeError:
pass
marks.append("#"+str(count))
print(count)
t.mark_tree([str(count)], marks=marks)
#just label everything with #1
print(t.write())
outfile = tree+"."+"_".join([str(node.node_id)])
with open(outfile, 'w') as out:
out.write(t.write())
outfiles.append(outfile)
#t.show()
t.render(tree+".png")
return(outfiles, tolabelreg)
def showTreeWithPictures(tree = None, alignment=None, branchLengths=True, bootstrapSupport=True, tolabel=None,showZScores=False,showLogs=False ):
print(PICS)
print("ShowTreeWithPictures",tree, alignment, branchLengths,bootstrapSupport, tolabel,showZScores,showLogs )
if not alignment:
nsFG = NodeStyle()
nsFG["fgcolor"] = "darkgreen"
nsFG["size"] = 8
t = EvolTree(tree)
#todo:label
#
for node in t.traverse():
print(node.node_id)
if tolabel:
if str(node.node_id) in tolabel:
node.set_style(nsFG)
#q'n'd
if (node.name.split("_")[0]+".png" in PICS):
print(node.name.split("_")[0]+".png")
node.add_face(ImgFace(PICDIR+os.sep+node.name.split("_")[0]+".png", height=50), column=1, position="aligned")
#non GRZM identifier
elif (node.name+".png" in PICS):
print(node.name+".png")
node.add_face(ImgFace(PICDIR+os.sep+node.name+".png", height=50), column=1, position="aligned")
ts = TreeStyle()
ts.show_leaf_name = True
ts.show_branch_length = branchLengths
ts.show_branch_support = bootstrapSupport
out = FILE
if branchLengths:
out+="_Len"
if bootstrapSupport:
out+="_Boot"
if Z:
out+="_Z"
if L:
out+="_L"
t.render(out+"_tree.pdf",tree_style=ts)
t.render(out+"_tree.png",tree_style=ts)
if INTERACTIVE:
t.show(tree_style=ts)
else:
t = EvolTree(tree, alignment,alg_format="paml")
t.link_to_alignment(alignment,alg_format="paml")
#todo label
#todo check treestyle
#ts = TreeStyle()
#ts.show_leaf_name = True
#ts.show_branch_length = branchLength
#ts.show_branch_support = bootstrapSupport
t.show()
def label_regex(unlabeled_tree, regex, treefile, depth=4,
model_list=None, paml_msa=None, outfile=None):
pattern = re.compile(regex)
t = EvolTree(unlabeled_tree)
marks = []
count = 1
outfiles = []
ts = TreeStyle()
ts.mode = "c"
nsMatch = NodeStyle() # match
nsMatch["fgcolor"] = "blue"
nsMatch["size"] = 10
nsBG = NodeStyle()
nsBG["fgcolor"] = "black"
nsBG["size"] = 0
nsFG = []
tolabelreg = []
for i in range(0, depth):
nsFG.append(NodeStyle())
nsFG[i]["size"] = 10
nsFG[i]["fgcolor"] = "blue"
#isroot = True
#for node in t.traverse():
# node.set_style(nsBG)
# if node.is_root():
# print("root")
# node.unroot()
# node._support = None
for node in t.get_descendants():
node.add_face(TextFace(node.node_id), column=0)
# traverse and match
for node in t.traverse():
if re.match(pattern, node.name):
node.set_style(nsMatch)
n = node
try:
for i in range(0, depth):
n = n.up
n.set_style(nsFG[i])
marks.append("#" + str(count))
#print(count)
t.mark_tree([str(count)], marks=marks)
#just label everything with #1
tolabelreg.append(str(n.node_id))
outfile = treefile + "." + "_".join([str(n.node_id)])
with open(outfile, 'w') as out:
out.write(t.write())
outfiles.append(outfile)
except AttributeError:
pass
else:
node.set_style(nsBG)
for f in tolabelreg:
print(f,"FFF")
for m in model_list:
generateCtl(model=m, treefile=treefile+"."+f, seqfile=paml_msa, outfile=treefile+"."+f,
generateOther=False)
t = fakeUnroot(t)
t.render(treefile+".png", tree_style=ts)
return outfiles, tolabelreg
results_list.append(entry_results)
site_file.close()
#Create the pool of processors
pool = multiprocessing.Pool(args.num_processors)
run_results = []
for cluster in clusters_to_analyze:
tree_file = args.tree_folder + "/" + cluster + ".tre"
align_file = args.align_folder + "/" + cluster + ".fna"
node_id_2_names = defaultdict()
for entry in EvolTree(tree_file).iter_descendants():
node_id_2_names[entry.node_id] = entry.get_leaf_names()
#Check that the files exists
if not os.path.exists(tree_file):
print "Tree file missing: " + tree_file
no_results_file.write(cluster + "\n")
continue
if not os.path.exists(align_file):
print "Alignment missing: " + align_file
no_results_file.write(cluster + "\n")
#Results, the first element has:
#The second is a dictionary with the positive selected sites
15 Nov 2010
example of tests for different rates among sites in clades
"""
__author__ = "Francois-Jose Serra"
__email__ = "*****@*****.**"
__licence__ = "GPLv3"
__version__ = "0.0"
from ete2 import EvolTree
from ete2 import NodeStyle
tree = EvolTree ("data/S_example/measuring_S_tree.nw")
tree.link_to_alignment ('data/S_example/alignment_S_measuring_evol.fasta')
print tree
print 'Tree and alignment loaded.'
raw_input ('Tree will be mark in order to contrast Gorilla and Chimpanzee as foreground \nspecies.')
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'):
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
"""
15 Nov 2010
simple example to mark a tree and compute branch-site test of positive selection
"""
__author__ = "Francois-Jose Serra"
__email__ = "*****@*****.**"
__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