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
|
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot
import os
from multiprocessing import Pool
from subprocess import *
import numpy as np
import pdb
fg_mers = {}
bg_mers = {}
# empty class to fill up mer information with
class Mer:
pass
class Score:
pass
# import our variables
min_mer_range = int(os.getenv("min_mer_range"));
max_mer_range = int(os.getenv("max_mer_range"));
min_mer_count = int(os.getenv("min_mer_count"));
max_select = int(os.getenv("max_select"));
def populate_locations(input_fn, mers, selected):
mer_str = ' '.join(selected)
cmd = './strstream ' + mer_str + " < " + input_fn
strstream = Popen(cmd, stdout=PIPE, shell=True)
for line in strstream.stdout:
(index, pos) = line.split(' ')
mers[selected[int(index)]].pts.append(int(pos))
return None
def select_mers(selected):
from itertools import combinations
scores = []
p = Pool()
for select_n in range(1, max_select):
print "scoring size ", select_n
scores_it = []
scores_it = p.map(score, combinations(selected, select_n))
scores = scores + scores_it
p.close()
scores = sorted(scores, key=lambda score: score.score)
return scores
def score(combination):
# print "scoring", combination
fg_pts_arr = []
fg_dist_arr = []
bg_pts_arr = []
bg_dist_arr = []
for mer in combination:
fg_pts_arr = fg_pts_arr + fg_mers[mer].pts
bg_pts_arr = bg_pts_arr + bg_mers[mer].pts
fg_pts_arr.sort()
bg_pts_arr.sort()
# remove any exact duplicates
fg_pts_arr = list(set(fg_pts_arr))
bg_pts_arr = list(set(bg_pts_arr))
# distances
for i in range(1, len(fg_pts_arr)):
fg_dist_arr.append(abs(fg_pts_arr[i-1] - fg_pts_arr[i]));
for i in range(1, len(bg_pts_arr)):
bg_dist_arr.append(abs(bg_pts_arr[i-1] - bg_pts_arr[i]));
fg_dist_arr = np.array(fg_dist_arr)
bg_dist_arr = np.array(bg_dist_arr)
nb_primers = len(combination)
fg_mean_dist = np.mean(fg_dist_arr)
fg_variance_dist = np.var(fg_dist_arr)
bg_mean_dist = np.mean(bg_dist_arr)
bg_variance_dist = np.var(bg_dist_arr)
# this is our equation
score = Score()
score.mers = combination
score.score = (nb_primers * (fg_mean_dist * fg_variance_dist)) / (bg_mean_dist * bg_variance_dist)
# score.bg_dist_arr = bg_dist_arr
# score.fg_dist_arr = fg_dist_arr
return score
def most_frequent(mers, select_nb):
print "selecting the top ", select_nb, " mers"
selected = []
for mer in mers.keys():
selected.append([mer, mers[mer].count])
selected = sorted(selected, key=lambda mer: mer[1])
selected = selected[-select_nb:]
selected = list(row[0] for row in selected)
return selected
def main():
fg_count_fn = sys.argv[1]
fg_fasta_fn = sys.argv[2]
bg_count_fn = sys.argv[3]
bg_fasta_fn = sys.argv[4]
fg_count_fh = open(fg_count_fn, "r")
bg_count_fh = open(bg_count_fn, "r")
# get our genome length
fg_genome_length = os.path.getsize(fg_fasta_fn)
bg_genome_length = os.path.getsize(bg_fasta_fn)
# copy in our fg_mers and counts
for mers,fh in [(fg_mers, fg_count_fh), (bg_mers, bg_count_fh)]:
for line in fh:
(mer, count) = line.split()
count = int(count)
mers[mer] = Mer()
mers[mer].count = count
mers[mer].pts = []
# if min_mer_count is less than one, then we are looking at a bottom cutoff threshold
if(min_mer_count < 1 and min_mer_count > 0):
pass
# counts = []
# for mer in fg_mers.keys():
# counts.append(fg_mers[mer]['count'])
#
# counts = counts.sort()
#
# count_set = set(counts):
# len(count_set)
# count_set(
# if it is 1 or great then use it as a numerical cutoff
print "removing mers that don't meet our minimum count"
if min_mer_count >= 1:
for mer in fg_mers.keys():
if(fg_mers[mer].count < min_mer_count):
del fg_mers[mer]
if mer in bg_mers:
del bg_mers[mer]
print "removing useless mers from the background"
for mer in bg_mers.keys():
if mer not in fg_mers:
del bg_mers[mer]
print "adding empty mers to the background"
for mer in fg_mers:
if mer not in bg_mers:
bg_mers[mer] = Mer()
bg_mers[mer].count = 2
bg_mers[mer].pts = [0, bg_genome_length]
exhaustive = True
if exhaustive:
selected = fg_mers.keys()
if not exhaustive:
selected = most_frequent(fg_mers, max_select)
print "selected the top ", max_select, " mers"
print "selected:", ", ".join(selected)
print "Populating foreground locations"
populate_locations(fg_fasta_fn, fg_mers, selected)
print "Populating background locations"
populate_locations(bg_fasta_fn, bg_mers, selected)
scores = select_mers(selected)
print "fg_genome_length", fg_genome_length
print "bg_genome_length", bg_genome_length
for score in scores:
sys.stdout.write(str(score.score) + "\t")
sys.stdout.write(", ".join(list(score.mers)) + "\t")
# sys.stdout.write("\t".join(score.dist_arr));
sys.stdout.write("\n");
#matplotlib.pyplot.hist(score.fg_dist_arr)
#matplotlib.pyplot.savefig('graph/' + str(x) + ".png")
#matplotlib.pyplot.clf()
import pdb
pdb.set_trace()
if __name__ == "__main__":
sys.exit(main())
|
