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
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
|
#!/usr/bin/python3
# encoding: utf-8
from __future__ import (absolute_import, division,
print_function, unicode_literals)
import os, sys, re
import logging
import TGraph
import TNode
import Node_alignment
from Topological_sort import Topological_sort
import TGLOBALS
logger = logging.getLogger(__name__)
MAX_MM_RATE = 0.05
class Compact_graph_whole:
def __init__(self):
pass
def compact_unbranched(self, tgraph):
all_nodes = Topological_sort.topologically_sort(tgraph.get_all_nodes())
for node in all_nodes:
if node.is_dead():
continue
logger.debug("compact_unbranched( {} )".format(node.toString()))
prev_nodes = list(node.get_prev_nodes())
if len(prev_nodes) == 1:
# merge them
prev_node = prev_nodes[0]
if len(prev_node.get_next_nodes()) != 1:
# prev node is downward branched... no dice
logger.debug("prev node: {} id downward branched, so can't compact. {}".format(prev_node, prev_node.toString()))
continue
logger.debug("linear compaction of nodes {} and {}".format(prev_node, node))
node.add_transcripts(prev_node.get_transcripts())
node.set_seq(prev_node.get_seq() + node.get_seq())
tgraph.add_edges(prev_node.get_prev_nodes(), [node])
tgraph.prune_node(prev_node)
def compact_graph(self, tgraph, num_allowed_variants):
compacted_flag = True
round = 0
while compacted_flag:
round += 1
logger.debug("\n\t### Num allowed variants: {}, Round: {}".format(num_allowed_variants, round))
tgraph.clear_touch_settings() # start fresh
compacted_flag = False # reset for this round
sorted_nodes = Topological_sort.topologically_sort(tgraph.get_all_nodes())
if TGLOBALS.DEBUG:
dot_filename = "ladeda.compacted_whole.BEGIN.num_allowed_{}.round_{}.dot".format(num_allowed_variants, round)
tgraph.draw_graph(dot_filename)
logger.debug("-wrote dot: {}".format(dot_filename))
for node in sorted_nodes:
if node.get_touched_val() > 0:
continue
logger.debug("\n\n///////// R[{}] Exploring compaction at node: {}\n\n".format(round, node.toString()))
# try compact upward
prev_nodes = node.get_prev_nodes()
if len(prev_nodes) > 1 and self.untouched(prev_nodes) and self.all_have_lower_topological_orderings(node, prev_nodes):
if self.compact_upward(prev_nodes, num_allowed_variants):
compacted_flag = True
next_nodes = node.get_next_nodes()
if len(next_nodes) > 1 and self.untouched(next_nodes) and self.all_have_higher_topological_orderings(node, next_nodes):
if self.compact_downward(next_nodes, num_allowed_variants):
compacted_flag = True
if compacted_flag:
if TGLOBALS.DEBUG:
dot_filename = "ladeda.compacted_whole.END.num_allowed_{}.round_{}.dot".format(num_allowed_variants, round)
tgraph.draw_graph(dot_filename)
logger.debug("-wrote dot: {}".format(dot_filename))
self.compact_unbranched(tgraph)
def untouched(self, node_list):
for node in node_list:
if node.get_touched_val() > 1:
return False
return True
def all_have_lower_topological_orderings(self, node, other_nodes_list):
topo_order = node.get_topological_order()
if topo_order < 0:
raise RuntimeError("Error, must run topological ordering first")
for other_node in other_nodes_list:
if other_node.get_topological_order() > topo_order:
return False
return True
def all_have_higher_topological_orderings(self, node, other_nodes_list):
topo_order = node.get_topological_order()
if topo_order < 0:
raise RuntimeError("Error, must run topological ordering first")
for other_node in other_nodes_list:
if other_node.get_topological_order() < topo_order:
return False
return True
####################
## Upward Compaction
def compact_upward(self, prev_nodes, num_allowed_variants):
logger.debug("compact_upward: {} max_allowed_variants: {}".format(prev_nodes, num_allowed_variants))
prev_nodes = list(prev_nodes)
for i in range(0, len(prev_nodes)-1):
for j in range(i+1, len(prev_nodes)):
if self.compact_upward_node_pair(prev_nodes[i], prev_nodes[j], num_allowed_variants):
return True
return False
def compact_upward_node_pair(self, node_A, node_B, num_allowed_variants):
logger.debug("compact_upward_node_pair({}, {}, num_allowed_variants: {}".format(node_A, node_B, num_allowed_variants))
# ensure they're on parallel paths in the graph.
if (node_A.is_ancestral(node_B) or node_B.is_ancestral(node_A)):
logger.debug("-not parallel paths. excluding compaction of {} and {}".format(node_A, node_B))
return False
logger.debug("-not ancestral: {}, {}".format(node_A, node_B))
# switch A,B so A is the shorter sequence node
if len(node_B.get_seq()) < len(node_A.get_seq()):
(node_A, node_B) = (node_B, node_A)
seqA = node_A.get_seq()
seqB = node_B.get_seq()
shorter_len = len(seqA)
# reverse the strings and check number of mismatches
seqA_rev = seqA[::-1]
seqB_rev = seqB[::-1]
num_mm = 0
for i in range(0, shorter_len):
if seqA_rev[i] != seqB_rev[i]:
num_mm += 1
if num_mm > num_allowed_variants:
logger.debug("num_mm: {} exceeds {} for node pair: {} and {}".format(num_mm, num_allowed_variants, node_A, node_B))
return False
if num_mm / shorter_len > MAX_MM_RATE:
logger.debug("num_mm: {} / len: {} exceeds max mm rate: {} for node pair: {} and {}".format(num_mm, shorter_len,
MAX_MM_RATE, node_A, node_B))
return False
# go ahead and merge the two in their region of common overlap
tgraph = node_A.get_graph()
##### operations needed:
#
# A
# \
# -- X
# /
# B
#
# -add B transcripts to A
# -adjust all B-downward edges to A
# -if B is longer than A, set upward to A and trim B part to unique region
# otherwise, remove B altogether.
#
logger.debug("node_A before mods: {}".format(node_A.toString()))
logger.debug("node_B before mods: {}".format(node_B.toString()))
node_A.add_transcripts(node_B.get_transcripts())
tgraph.add_edges( [node_A], node_B.get_next_nodes() )
tgraph.prune_edges( [node_B], node_B.get_next_nodes() )
node_A.touch()
node_B.touch()
if len(seqB) > shorter_len:
delta = len(seqB) - shorter_len
seqB = seqB[0:delta]
node_B.set_seq(seqB)
tgraph.add_edges([node_B], [node_A])
else:
# remove node_B prev edges and attach them to A
tgraph.add_edges(node_B.get_prev_nodes(), [node_A])
# remove B altogether
tgraph.prune_node(node_B)
logger.debug("node_A after mods: {}".format(node_A.toString()))
logger.debug("node_B after mods: {}".format(node_B.toString()))
logger.debug("\n\n\t*** compacted nodes: {} and {} ***".format(node_A, node_B))
return True
######################
## Downward compaction
def compact_downward(self, next_nodes, num_allowed_variants):
logger.debug("compact_downward: {} max_allowed_variants: {}".format(next_nodes, num_allowed_variants))
next_nodes = list(next_nodes)
for i in range(0, len(next_nodes)-1):
for j in range(i+1, len(next_nodes)):
if self.compact_downward_node_pair(next_nodes[i], next_nodes[j], num_allowed_variants):
return True
return False
def compact_downward_node_pair(self, node_A, node_B, num_allowed_variants):
# ensure they're on parallel paths in the graph.
if (node_A.is_descendant(node_B) or node_B.is_descendant(node_A)):
logger.debug("-not parallel paths. excluding compaction of {} and {}".format(node_A, node_B))
return False
# switch A,B so A is the shorter sequence node
if len(node_B.get_seq()) < len(node_A.get_seq()):
(node_A, node_B) = (node_B, node_A)
seqA = node_A.get_seq()
seqB = node_B.get_seq()
shorter_len = len(seqA)
num_mm = 0
for i in range(0, shorter_len):
if seqA[i] != seqB[i]:
num_mm += 1
if num_mm > num_allowed_variants:
logger.debug("num_mm: {} exceeds {} for node pair: {} and {}".format(num_mm, num_allowed_variants, node_A, node_B))
return False
if num_mm / shorter_len > MAX_MM_RATE:
logger.debug("num_mm: {} / len: {} exceeds max mm rate: {} for node pair: {} and {}".format(num_mm, shorter_len,
MAX_MM_RATE, node_A, node_B))
return False
# go ahead and merge the two in their region of common overlap
tgraph = node_A.get_graph()
##### operations needed:
#
# A
# /
# -- X
# \
# B
#
# -add B transcripts to A
# -adjust all B-downward edges to A
# -if B is longer than A, set downward to A and trim B part to unique region
# otherwise, remove B altogether.
#
logger.debug("node_A before mods: {}".format(node_A.toString()))
logger.debug("node_B before mods: {}".format(node_B.toString()))
node_A.add_transcripts(node_B.get_transcripts())
tgraph.add_edges(node_B.get_prev_nodes(), [node_A])
tgraph.prune_edges(node_B.get_prev_nodes(), [node_B])
node_A.touch()
node_B.touch()
if len(seqB) > shorter_len:
seqB = seqB[shorter_len:]
node_B.set_seq(seqB)
tgraph.add_edges([node_A], [node_B])
else:
# remove node_B prev edges and attach them to A
tgraph.add_edges([node_A], node_B.get_next_nodes())
# remove B altogether
tgraph.prune_node(node_B)
logger.debug("node_A after mods: {}".format(node_A.toString()))
logger.debug("node_B after mods: {}".format(node_B.toString()))
logger.debug("\n\n\t*** compacted nodes: {} and {} ***".format(node_A, node_B))
return True
|
