-
Notifications
You must be signed in to change notification settings - Fork 17
/
Copy pathmake_bed.py
executable file
·203 lines (186 loc) · 10.2 KB
/
make_bed.py
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
#!/usr/bin/env python
import sys
import numpy as np
from Bio import SeqIO
from scipy import stats
from scipy.cluster.hierarchy import fcluster,linkage
import scipy.spatial.distance as ssd
from extract_contexts import revcomp
from plotlib import plot_w_labels
import os
import pandas as pd
def make_pos_set(pos_list):
pos_set = set()
with open(pos_list,'r') as fi:
for line in fi:
if len(line) > 3:
pos_set.add(tuple(line.strip().split('\t')[:4]))
return pos_set
def check_thresh(locus_list,mod_thresh,depth_thresh,control):
if len(locus_list) >= depth_thresh:
if not control and np.mean(locus_list) >= mod_thresh:
return True
elif control and np.mean(locus_list) < mod_thresh:
return True
else:
return False
def write_gff(outfi,line_info):
#scf7180000000004|quiver kinModCall m4C 6644 6644 20 + . coverage=16;context=CGAAATCATTTCTCAGCAGGCGGTAAAAATGGCGGTTTTCG;IPDRatio=3.13;frac=1.000;fracLow=1.000;fracUp=1.000;identificationQv=7
csome,nextpos,strand,deets = line_info
line = '\t'.join([csome,"kinModCall","m6A",nextpos,nextpos,'10',strand,'.',deets])+'\n' #10 for all positions, giving a "log10 score" of 1 per read for motif calling with MotifMaker
outfi.write(line)
def ref2context(ref,pos_dict):
ref_dict = {}
pos_context_dict = {}
for contig in SeqIO.parse(ref,"fasta"):
ref_dict[contig.id] = str(contig.seq)
for pos in pos_dict:
if pos[0] in ref_dict:
cx = ref_dict[pos[0]][int(pos[1])-20:int(pos[1])+21].upper()
if pos[4] == '-':
cx = revcomp(cx)
pos_context_dict[pos] = cx #make adjustable length of context?
ref_dict = {}
return pos_context_dict
def cluster(currents,context,original_labels,chrom,pos1,plot,plotdir,cluster=False):
colours = {'m6A':'#B4656F','A':'#55B196'} #TODO update for other labels
if len(currents) > 1 and cluster :
pdistance = ssd.pdist(currents,metric='correlation')
dm = ssd.squareform(pdistance)
link = linkage(dm,method='complete',metric='correlation')
klabels = fcluster(link,2,'maxclust') #1,'inconsistent') #2,'maxclust')
#klabels = [1 if x == 1 else 0 for x in klabels]
#labels = ['m6A']*len(klabels)
strategy = 'correlation'
else:
klabels = [1 if x==1 else 0 for x in original_labels]
strategy = 'classifierProb'
if plot:
plot_w_labels(klabels,original_labels,currents,strategy,context,'chrom.'+chrom+'.pos.'+pos1,plotdir,colours)
#for cluster in clusters:
def aggregate_by_pos(meth_fi,aggfi,depth_thresh,mod_thresh,pos_list,control,verbose_results,gff,ref,plot,plotdir,plotsummary):
pos_dict = {}
if verbose_results:
pos_dict_verbose = {}
if pos_list:
pos_set = make_pos_set(pos_list)
values_dict = {}
for line in open(meth_fi,'r'):
#try:
#print line
try:
csome,read,pos,context,values,strand,label,prob = tuple(line.split('\t'))
except: #for backwards compatibility; does not work with verbose results
csome,read,pos,context,values,strand,label = tuple(line.split('\t'))
nextpos = str(int(pos)+1)
if (pos_list and (csome,pos,nextpos,strand) not in pos_set) or (context[int(len(context)/2)] != 'M'):
continue
if (csome,pos,nextpos,context,strand) not in pos_dict:
pos_dict[(csome,pos,nextpos,context,strand)] = []
values_dict[(csome,pos,nextpos,context,strand)] = []
if verbose_results:
pos_dict_verbose[(csome,pos,nextpos,context,strand)] = []
if (pos_list and (csome,pos,nextpos,strand) in pos_set) or (not pos_list and plot):
values_dict[(csome,pos,nextpos,context,strand)].append([float(v) for v in values.split(',')][:-1])
if label[0] == 'm':
pos_dict[(csome,pos,nextpos,context,strand)].append(1)
else:
pos_dict[(csome,pos,nextpos,context,strand)].append(0)
if verbose_results:
pos_dict_verbose[(csome,pos,nextpos,context,strand)].append(prob.strip())
#except:
# pass
print(values_dict)
if plotsummary:
print('plotting all current deviations...')
num2lab = {0:'A',1:'m6A'}
curlab = [(val,num2lab[lab],lab) for pos_tup in values_dict for val,lab in zip(values_dict[pos_tup],pos_dict[pos_tup])]
currents,labels,klabels = zip(*curlab)
colours = {'m6A':'#B4656F','A':'#55B196'}
plot_w_labels(klabels,labels,currents,'classifierProb','allpos','allpos',plotdir,colours,alpha=0.3)
print('finished plotting.')
if plot:
for locus in values_dict:
cluster(values_dict[locus],locus[3],['m6A' if x == 1 else 'A' for x in pos_dict[locus]],locus[0],locus[1],plot,plotdir)
if pos_list:
for locus in values_dict:
values_df = pd.DataFrame(values_dict[locus])
tvals = []
pvals = []
for i in values_df.columns: #[:-1]:
#for j in values_df.columns[i+1:]:
ttest = stats.ttest_1samp(values_df[i],0)
#ttest = stats.ttest_rel(values_df[i],values_df[i+1])
#tvals.append(ttest[0])
pvals.append((ttest[1],ttest[0]))
pval = (sum([-np.log10(x[0]) for x in pvals]), max([x[1] for x in pvals])) #min(pvals)
values_dict[locus] = [np.round(x,3) for x in [pval[1],pval[0]]]
if ref:
context_dict = ref2context(ref,pos_dict)
count = 0
outfi = open(aggfi,'w')
for locus in pos_dict.keys():
a = (not pos_list) and check_thresh(pos_dict[locus],mod_thresh,depth_thresh,control)
b = pos_list and (locus[0],locus[1],locus[2],locus[4]) in pos_set #and #'A' not in set(locus[4])
if ref:
cx = context_dict[locus]
else:
cx = locus[3]
if a or b:
count+=1
frac = np.mean(pos_dict[locus])
if gff:
deets = 'coverage='+str(len(pos_dict[locus]))+';context='+cx+';IPDRatio=5;frac='+str(frac)
if verbose_results:
probs = [float(x) for x in pos_dict_verbose[locus]]
se_95 = 2*stats.sem(probs)
deets = deets + ';fracLow='+str(frac-se_95)+';fracUp='+str(frac+se_95)+';identificationQv='+str(int(100*np.mean([float(x) for x in pos_dict_verbose[locus]])))
gff_info = (locus[0],locus[2],locus[4],deets)
write_gff(outfi,gff_info)
else:
print(aggfi)
out_line = '\t'.join(list(locus)[:-1]+[str(np.mean(pos_dict[locus]))]+[locus[-1]]+[str(len(pos_dict[locus]))]) #+[str(x) for x in values_dict[locus]])
if pos_list:
out_line = out_line + '\t' + '\t'.join([str(x) for x in values_dict[locus]])
if verbose_results:
out_line = out_line + '\t'+','.join(pos_dict_verbose[locus])
outfi.write(out_line+'\n')
if not pos_list:
if not control:
print(count, 'methylated loci found with min depth', depth_thresh, 'reads')
else:
print(count, 'unmethylated loci found with min depth', depth_thresh, 'reads')
def main():
#parse command line options
from argparse import ArgumentParser
parser = ArgumentParser(description='Produce bed file of methylated positions based on mCaller output')
parser.add_argument('-d','--min_read_depth',type=int,required=False,help='minimum coverage of position to determine methylation (default = 15)',default=15)
parser.add_argument('-t','--mod_threshold',type=float,required=False,help='minimum fraction of observations with probability of methylation >=50%% at a position to include in report (default = 0.5)',default=0.5)
parser.add_argument('-f','--mCaller_file',type=str,required=True,help='the output file from mCaller to summarize')
parser.add_argument('-p','--positions',type=str,required=False,help='~bed file of positions for which to calculate %% of methylated reads (chromosome,start,end,strand); ignores other thresholds')
parser.add_argument('--control',action='store_true',required=False,help='take unmethylated positions as a control for motif detection')
parser.add_argument('--gff',action='store_true',required=False,help='output PacBio-style gff instead of bed ("identificationQv" score will be average probability of methylation)')
parser.add_argument('--ref',type=str,required=False,help='use reference fasta to output longer contexts surrounding a base, from -20 to +20')
parser.add_argument('--plot',action='store_true',required=False,help='plot currents deviations at the positions included (not recommended for many positions)')
parser.add_argument('--plotsummary',action='store_true',required=False,help='plot currents deviations summarized across the positions included')
parser.add_argument('--plotdir',type=str,required=False,default='mCaller_position_plots',help='output directory for plots, default=mCaller_position_plots')
parser.add_argument('--vo',action='store_true',required=False,help='verbose output including probabilities for each position')
parser.add_argument('-v','--version',action='version',help='print version',version='%(prog)s v1.0')
args = parser.parse_args()
assert os.path.isfile(args.mCaller_file), 'file not found at '+args.mCaller_file
if args.positions:
output_file = args.mCaller_file.split('.')[0]+'.methylation.positions.summary'
elif not args.control:
output_file = args.mCaller_file.split('.')[0]+'.methylation.summary'
else:
output_file = args.mCaller_file.split('.')[0]+'.methylation.control.summary'
if args.gff:
output_file = output_file+'.gff'
else:
output_file = output_file+'.bed'
if not os.path.isdir(args.plotdir):
os.mkdir(args.plotdir)
print(args.mCaller_file)
aggregate_by_pos(args.mCaller_file,output_file,args.min_read_depth,args.mod_threshold,args.positions,args.control,args.vo,args.gff,args.ref,args.plot,args.plotdir,args.plotsummary)
if __name__ == "__main__":
main()