1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
|
from __future__ import absolute_import
# #START_LICENSE###########################################################
#
#
# This file is part of the Environment for Tree Exploration program
# (ETE). http://etetoolkit.org
#
# ETE is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ETE is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
# License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ETE. If not, see <http://www.gnu.org/licenses/>.
#
#
# ABOUT THE ETE PACKAGE
# =====================
#
# ETE is distributed under the GPL copyleft license (2008-2015).
#
# If you make use of ETE in published work, please cite:
#
# Jaime Huerta-Cepas, Joaquin Dopazo and Toni Gabaldon.
# ETE: a python Environment for Tree Exploration. Jaime BMC
# Bioinformatics 2010,:24doi:10.1186/1471-2105-11-24
#
# Note that extra references to the specific methods implemented in
# the toolkit may be available in the documentation.
#
# More info at http://etetoolkit.org. Contact: huerta@embl.de
#
#
# #END_LICENSE#############################################################
import copy
from .evolevents import EvolEvent
def get_reconciled_tree(node, sptree, events):
""" Returns the recoliation gene tree with a provided species
topology """
if len(node.children) == 2:
# First visit childs
morphed_childs = []
for ch in node.children:
mc, ev = get_reconciled_tree(ch, sptree, events)
morphed_childs.append(mc)
# morphed childs are the reconciled children. I trust its
# topology. Remember tree is visited on recursive post-order
sp_child_0 = morphed_childs[0].get_species()
sp_child_1 = morphed_childs[1].get_species()
all_species = sp_child_1 | sp_child_0
# If childs represents a duplication (duplicated species)
# Check that both are reconciliated to the same species
if len(sp_child_0 & sp_child_1) > 0:
newnode = copy.deepcopy(node)
newnode.up = None
newnode.children = []
template = _get_expected_topology(sptree, all_species)
# replaces child0 partition on the template
newmorphed0, matchnode = _replace_on_template(template, morphed_childs[0])
# replaces child1 partition on the template
newmorphed1, matchnode = _replace_on_template(template, morphed_childs[1])
newnode.add_child(newmorphed0)
newnode.add_child(newmorphed1)
newnode.add_feature("evoltype", "D")
node.add_feature("evoltype", "D")
e = EvolEvent()
e.etype = "D"
e.inparalogs = node.children[0].get_leaf_names()
e.outparalogs = node.children[1].get_leaf_names()
e.in_seqs = node.children[0].get_leaf_names()
e.out_seqs = node.children[1].get_leaf_names()
events.append(e)
return newnode, events
# Otherwise, we need to reconciliate species at both sides
# into a single partition.
else:
# gets the topology expected by the observed species
template = _get_expected_topology(sptree, all_species)
# replaces child0 partition on the template
template, matchnode = _replace_on_template(template, morphed_childs[0] )
# replaces child1 partition on the template
template, matchnode = _replace_on_template(template, morphed_childs[1])
template.add_feature("evoltype","S")
node.add_feature("evoltype","S")
e = EvolEvent()
e.etype = "S"
e.inparalogs = node.children[0].get_leaf_names()
e.orthologs = node.children[1].get_leaf_names()
e.in_seqs = node.children[0].get_leaf_names()
e.out_seqs = node.children[1].get_leaf_names()
events.append(e)
return template, events
elif len(node.children)==0:
return copy.deepcopy(node), events
else:
raise ValueError("Algorithm can only work with binary trees.")
def _replace_on_template(orig_template, node):
template = copy.deepcopy(orig_template)
# detects partition within topo that matchs child1 species
nodespcs = node.get_species()
spseed = list(nodespcs)[0] # any sp name woulbe ok
# Set an start point
subtopo = template.search_nodes(children=[], name=spseed)[0]
# While subtopo does not cover all child species
while len(nodespcs - set(subtopo.get_leaf_names() ) )>0:
subtopo= subtopo.up
# Puts original partition on the expected topology template
nodecp = copy.deepcopy(node)
if subtopo.up is None:
return nodecp, nodecp
else:
parent = subtopo.up
parent.remove_child(subtopo)
parent.add_child(nodecp)
return template, nodecp
def _get_expected_topology(t, species):
missing_sp = set(species) - set(t.get_leaf_names())
if missing_sp:
raise KeyError("* The following species are not contained in the species tree: "+ ','.join(missing_sp) )
node = t.search_nodes(children=[], name=list(species)[0])[0]
sps = set(species)
while sps-set(node.get_leaf_names()) != set([]):
node = node.up
template = copy.deepcopy(node)
# make get_species() to work
#template._speciesFunction = _get_species_on_TOL
template.set_species_naming_function(_get_species_on_TOL)
template.detach()
for n in [template]+template.get_descendants():
n.add_feature("evoltype","L")
n.dist = 1
return template
def _get_species_on_TOL(name):
return name
def get_reconciled_tree_zmasek(gtree, sptree, inplace=False):
"""
Reconciles the gene tree with the species tree
using Zmasek and Eddy's algorithm. Details can be
found in the paper:
Christian M. Zmasek, Sean R. Eddy: A simple algorithm
to infer gene duplication and speciation events on a
gene tree. Bioinformatics 17(9): 821-828 (2001)
:argument gtree: gene tree (PhyloTree instance)
:argument sptree: species tree (PhyloTree instance)
:argument False inplace: if True, the provided gene tree instance is
modified. Otherwise a reconciled copy of the gene tree is returned.
:returns: reconciled gene tree
"""
# some cleanup operations
def cleanup(tree):
for node in tree.traverse(): node.del_feature("M")
if not inplace:
gtree = gtree.copy('deepcopy')
# check for missing species
missing_sp = gtree.get_species() - sptree.get_species()
if missing_sp:
raise KeyError("* The following species are not contained in the species tree: "+ ', '.join(missing_sp))
# initialization
sp2node = dict()
for node in sptree.get_leaves(): sp2node[node.species] = node
# set/compute the mapping function M(g) for the
# leaf nodes in the gene tree (see paper for details)
species = sptree.get_species()
for node in gtree.get_leaves():
node.add_feature("M",sp2node[node.species])
# visit each internal node in the gene tree
# and detect its event (duplication or speciation)
for node in gtree.traverse(strategy="postorder"):
if len(node.children) == 0:
continue # nothing to do for leaf nodes
if len(node.children) != 2:
cleanup(gtree)
raise ValueError("Algorithm can only work with binary trees.")
lca = node.children[0].M.get_common_ancestor(node.children[1].M) # LCA in the species tree
node.add_feature("M",lca)
node.add_feature("evoltype","S")
if id(node.children[0].M) == id(node.M) or id(node.children[1].M) == id(node.M):
node.evoltype = "D"
cleanup(gtree)
return gtree
|
