def run_evol_py(tree,
alg,
branch_model,
site_models,
workir='data/evol_output',
tool_dir="ete3_apps/bin"):
print(tree, alg, branch_model, site_models)
builtin_apps_path = None
builtin_apps_path = os.path.join(os.path.split(ete3_path)[0], tool_dir)
tree = EvolTree(tree, binpath=builtin_apps_path)
tree.link_to_alignment(alg)
tree.workdir = workir
###branch model
if branch_model:
branch_model = str(branch_model)
tree.run_model(branch_model)
print(tree.get_evol_model(branch_model))
### site model
for site_model in site_models:
tree.run_model(site_model)
#tree.run_model('SLR.lele')
global evol_output_dir, final_evol_tree
evol_output_dir = workir
final_evol_tree = evol_output_dir + '/tree_evol_result.png'
tree.render(final_evol_tree,
layout=evol_clean_layout,
histfaces=site_models)
return tree
def run(self, pamlsrc, output_folder, model='M1'):
"""Run PAML using ETE.
The default model is M1 as it is best for orthology inference in
our case. You can use models `M2`, `M0`, `M3`.
Ensure that you have the correct path to your codeml binary. It should
be in the paml `/bin`.
:param pamlsrc: Path to the codemly binary.
:param output_folder: The name of the output folder.
:param model: The model to be used. (Default value = 'M1')
"""
# Import the newick tree
tree = EvolTree('temptree.nw')
# Import the alignment
tree.link_to_alignment(self.alignmentfile)
tree.workdir = self.workdir
# Set the binpath of the codeml binary
tree.execpath = pamlsrc
# Run the model M1, M2, M3, or M0
model_path = model + '.' + output_folder
tree.run_model(model_path)
self.ete3paml_log.info('Codeml is generating data in %s.' % model_path)
def main(self):
"""The main function for running the test."""
print("Running model %s paml on input." % str(self.defaultmodel))
tree = EvolTree(self.tree) # Import the newick tree
tree.link_to_alignment(self.alignment) # Import the alignment
tree.workdir = self.workdir # Set the working directory
tree.execpath = self.pamlpath # Set the binpath of the codeml binary
tree.run_model(self.defaultmodel) # Run the codeml model
def count_omega(align_file, gene_name):
print(gene_name)
tree = EvolTree(tree_file)
tree.link_to_alignment(align_file)
#
# #free branch ratio count
tree.run_model('fb')
fb_results = tree.get_evol_model('fb')
print(fb_results)
with open(temp, 'w') as temp_file:
temp_file.write(str(fb_results))
write_in_table(gene_name)
def ete3paml(gene, paml_path, workdir='data/paml-output/', model='M1'):
""" Use ETE3's integration with PAML"""
# Import the species tree to compare species that are present in alignment
# file
t = Tree('data/initial-data/species_tree.nw', format=1)
orgsfile = pd.read_csv('data/initial-data/organisms.csv', header=None)
# Create a list name/variable and use list()
orgs = list(orgsfile[0])
organismslist = formatlist(orgs)
# Import alignment file as string
alignment_file = open(
'data/clustal-output/' + gene + '_Aligned/' + gene +
'_aligned_cds_nucl.fasta', 'r')
alignment_str = alignment_file.read()
alignment_file.close()
# Keep the branches in the species tree for species in the alignment file
# Some species may not be present in the alignment file
branches2keep = []
for organism in organismslist:
if organism in alignment_str:
#print('Yup.')
branches2keep.append(organism)
else:
pass
#print('Nope.') Make an error code in the log
# Input a list of branches to keep on the base tree
speciestree = t.prune(branches2keep, preserve_branch_length=True)
# Import the newick tree
tree = EvolTree(speciestree)
# Import the alignment
tree.link_to_alignment('data/clustal-output/' + gene + '_Aligned/' + gene +
'_aligned_cds_nucl.fasta')
tree.workdir = workdir
# Set the binpath of the codeml binary
tree.execpath = paml_path
# Run the codeml model
tree.run_model(model + '.' + gene)
def pamlSite(alnFile, treeFile, lModels, pamlParams, outDir, baseName, logger):
tree = EvolTree(treeFile)
os.mkdir(outDir + "paml_site/")
tree.workdir = outDir + "paml_site/"
tree.link_to_alignment(alnFile, "Fasta")
logger.info("PAML codeml")
dModelRun = {}
for model in lModels:
if model in ["M0", "M1", "M2", "M7", "M8"]:
logger.info("Running {:s}".format(model))
dModelRun[model] = tree.run_model(model)
if "M1" and "M2" in dModelRun:
p12 = tree.get_most_likely("M2", "M1")
logger.info("LRT of M1 vs M2 = {}".format(p12))
if "M7" and "M8" in dModelRun:
p78 = tree.get_most_likely("M8", "M7")
logger.info("LRT of M7 vs M8 = {}".format(p78))
"""
def run_codeml(mark_id, aln_file, tree_file, sleep):
logger.info('sub-process: {0}'.format(str(mark_id)))
time.sleep(round(sleep / args.threads, 2))
run_dir = os.path.join(output_dir, str(mark_id))
os.makedirs(run_dir)
tree = EvolTree(tree_file, format=0)
tree.link_to_alignment(aln_file)
tree.run_model('M0')
tree.workdir = run_dir
tree.mark_tree([mark_id], marks=['#1'])
tree.run_model('bsA.' + str(mark_id))
tree.run_model('bsA1.' + str(mark_id))
ps = tree.get_most_likely('bsA.' + str(mark_id), 'bsA1.' + str(mark_id))
rx = tree.get_most_likely('bsA1.' + str(mark_id), 'M0')
bsA = tree.get_evol_model('bsA.' + str(mark_id))
p_bsA = bsA.classes['proportions'][2]
wfrg2a = bsA.classes['foreground w'][2]
if ps < 0.05 and float(wfrg2a) > 1:
result = [mark_id, ps, rx, p_bsA, 'positive selection']
elif rx < 0.05 and ps >= 0.05:
result = [mark_id, ps, rx, p_bsA, 'relaxation']
else:
result = [mark_id, ps, rx, p_bsA, 'no signal']
return result
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'))
import sys, os, subprocess
import argparse
from ete3 import EvolTree
tree = EvolTree("tree.nw",
binpath="/home/edu/miniconda3/envs/ete3/bin/ete3_apps/bin")
tree.link_to_alignment("infile.phy", alg_format="phylip")
tree.workdir = os.getcwd()
print(tree)
print('running model M0, for comparison with branch-site models...')
tree.run_model('M0', keep=True)
#tree.link_to_evol_model("/home/edu/Desktop/Bioinformatica/Mitogenomics/Chondrichthyes/Phylogenetic_Tree","M0")
chimaeriformes = tree.get_common_ancestor("HM147138.1", "HM147135.1")
#chimaeriformes =tree.get_common_ancestor("Human_ECP","Goril_ECP")
for leaf in chimaeriformes:
tree.mark_tree([leaf.node_id], marks=["#1"])
#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 '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:'
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')
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.')
__email__ = "*****@*****.**"
__licence__ = "GPLv3"
__version__ = "0.0"
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.')
__licence__ = "GPLv3"
__version__ = "0.0"
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)
# 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
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.'
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')
print (tree)
try:
input = raw_input
except NameError:
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')
# 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)
p-value ofthis comparison is:''')
print (tree.get_most_likely ('b_free.137', 'M0'))
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
if starting_branch_length_option == 1:
branch_estimation = 'bl'
elif starting_branch_length_option == -1:
branch_estimation = 'random'
for initial_omega in [0.2, 0.7, 1.2]:
if model == 'bsA1':
initial_omega = 1.0
model_specifications = model + '.' + branch_estimation + '_' + \
str(initial_omega) + 'w'
print 'Testing model ' + model + ' on ' + alignment_name + \
' using starting branch length option ' + \
branch_estimation + ' and initial omega: ' + \
str(initial_omega) + 'w'
if model == 'XX':
tree.run_model(model_specifications, \
fix_blength=starting_branch_length_option, \
omega=initial_omega, NSsites=22, ncatG=3)
# Here's the garbage I wrote to make sure that it parses the out files correctly
tree.get_evol_model(
model_specifications).properties['typ'] = 'branch-site'
tree.get_evol_model(model_specifications)._load(
model_specifications + '/out')
else:
tree.run_model(model_specifications, \
fix_blength=starting_branch_length_option, \
omega=initial_omega)
current_model = tree.get_evol_model(model_specifications)
print 'The fitting of model ' + model + ' on ' + alignment_name + \
' using starting branch length option ' + \
from ete3 import EvolTree
from string import ascii_letters
# CREATE TREE
fasta_lines = open("./whales.fasta", "r").readlines()
taxa = [l.replace('>', '').strip() for l in fasta_lines if l.startswith('>')]
taxa_map = { t: ascii_letters[i] for i, t in enumerate(taxa) }
taxa_string = '(' * (len(taxa) - 1) + '%s,%s)' % (ascii_letters[0], ascii_letters[1])
for t in ascii_letters[2:len(taxa)]:
taxa_string = taxa_string + ',%s)' % t
taxa_string = taxa_string + ';'
align = ''.join(fasta_lines)
for t in taxa:
align = align.replace(t, taxa_map[t])
tree = EvolTree(taxa_string)
tree.link_to_alignment(align)
#tree.link_to_evol_model("M2")
#tree.get_evol_model("M2")
print(tree.run_model.__doc__)
tree.run_model("fb")
omega_list.append("NA")
continue
else:
evotree.link_to_alignment(subfasta)
workdirname = './codeml_' + "__".join(closest_seq_ids)
evotree.workdir = workdirname
list_of_tempdirs.append(workdirname)
# mark the foreground branch
foreground_leafnode = evotree & seqid
# print (seqid)
# print(foreground_leafnode.node_id)
# print (evotree.write())
evotree.mark_tree([foreground_leafnode.node_id], ['#1'])
# print (evotree.write())
evotree.run_model('b_free.run')
b_free_fit = evotree.get_evol_model('b_free.run')
out_branches_dict = b_free_fit.branches
for b in out_branches_dict:
if out_branches_dict[b]["mark"] == " #1":
# check if there are at least 1 synonymous substitutions expected on this branch... otherwise not very meaningful to estimate omega (it will be very high).
if out_branches_dict[b]["S"] * out_branches_dict[b][
"dS"] >= 1.0:
omega = out_branches_dict[b]["w"]
else:
omega = "NA"
break
omega_list.append(omega)
numeric_omegas = [float(x) for x in omega_list if not x == "NA"]
try:
avg_omega = sum(numeric_omegas) / float(len(numeric_omegas))
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'])
__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')
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/ete3.../bsA.. directory)
print 'running model bsA and bsA1'
tree.run_model('bsA.'+ leaf.name)
tree.run_model('bsA1.' + leaf.name)
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")
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())
input("hit some key...")
print("Have a look to run message of codeml: ")
print(fb.run)
input("hit some key...")
print("Have a look to log likelihood value of this model, and number of parameters:")
