-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathprocess_reads.py
157 lines (144 loc) · 6.36 KB
/
process_reads.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
#!/bin/env python3
import os,sys
import numpy as np
import pandas as pd
import argparse
from utils import *
from function import *
# CpG context
CG = ['Z','z']
CHG = ['X','x']
CHH = ['H','h']
XXX = ['U','u']
C_context_include = []
C_context = ['CG']
if "CG" in C_context: C_context_include.extend(CG)
if "CHG" in C_context: C_context_include.extend(CHG)
if "CHH" in C_context: C_context_include.extend(CHH)
if 'XXX' in C_context: C_context_include.extend(XXX)
C_context_exclude = list(set(['Z','z','H','h','X','x','U','u']).difference(set(C_context_include)))
# debug variable
infile = sys.argv[1]
contig = sys.argv[2]
overlap = False
outfile = sys.argv[3]
reads = pd.read_csv(infile,sep="\t",header=None)
reads.columns = ["chr","read1_start","read1_strand","read1_cigar","read1","read2_start","read2_strand","read2_cigar","read2"]
data = reads[reads["chr"] == contig]
data.reset_index(drop=True, inplace=True)
def correct_read_cigar(cigar,reads_Cseq):
cigar_list = re.split('(\d+)',cigar)
for C_context in C_context_exclude:
reads_Cseq = reads_Cseq.replace(C_context,".")
# # read doesn't contain C or G
# if not reads_Cseq.replace(".","") :
# return False
if len(cigar_list) == 3 and cigar_list[2] == "M" :
seq = reads_Cseq
else:
pos = 0
seq = ""
for process in range(2,len(cigar_list),2):
# 0 MEANS: MATCH M
cigar_list[process-1] = int(cigar_list[process-1])
if cigar_list[process] == "M" :
# pdb.set_trace()
seq += reads_Cseq[pos:(pos + cigar_list[process-1])]
pos += cigar_list[process-1]
# 1 MEANS: INSERT I
elif cigar_list[process] == "I" :
pos += cigar_list[process-1]
# 2 MEANS: DEL D
elif cigar_list[process] == "D" :
seq += "." * cigar_list[process-1]
# 3 MEANS： similarlly like D, skipped N bases
elif cigar_list[process] == "N" :
seq += "." * cigar_list[process-1]
else:
print("There was a character not MIDN in CIGAR!")
return False
return seq
## 00 Initialization
# disp("process contig " + args.contig + " start")
contig_methylation = {}
contig_length = 250000000
for element in ['coverage','meth','CHALM_meth','CAMDA_meth']:
# contig_methylation[element] = np.zeros(contigs_length[args.contig], dtype=int)
contig_methylation[element] = np.zeros(contig_length, dtype=int)
start_pos_list = list()
end_pos_list = list()
seq_list = list()
strand_list = list()
# C_incontext_dis_up_list = list()
# C_incontext_dis_down_list = list()
C_incontext_pos_list = list()
for index in range(0,data.shape[0]):
if index % 10000 == 0:
disp("hit 10000 times")
read1_seq = correct_read_cigar(data["read1_cigar"][index],data["read1"][index])
read2_seq = correct_read_cigar(data["read2_cigar"][index],data["read2"][index])
read1_start = data["read1_start"][index]
read2_start = data["read2_start"][index]
seq_read1_read2 = {'read1':read1_seq,'read2':read2_seq}
start_read1_read2 = {'read1':read1_start,'read2':read2_start}
if read1_start <= read2_start :
read_front_str = "read1"
read_behind_str = "read2"
else:
read_front_str = "read2"
read_behind_str = "read1"
read_len = len(seq_read1_read2[read_front_str])
overlap = read_len + start_read1_read2[read_front_str] - start_read1_read2[read_behind_str]
# read position1: overlap
if overlap > 0:
# using the calls from the first read which is presumably the one with a lowest error rate
if read_front_str == "read1":
seq = read1_seq + read2_seq[overlap:]
else:
gap1_end = read_len - overlap
# gap1 = seq_read1_read2[read_front_str][0:gap1_end]
seq = read2_seq[0:gap1_end] + read1_seq
# read position2: no overlap between read and mate
else:
gap_dis = -overlap
gap2 = "." * gap_dis
seq = seq_read1_read2[read_front_str] + gap2 + seq_read1_read2[read_behind_str]
# bam file and python and bed file are different based
start_pos = start_read1_read2[read_front_str] - 1
# end_pos = start_pos + len(seq) - 1
# 0-base for further python script use
C_incontext_pos = [ start_pos + index for index in range(0,len(seq)) if seq[index].isalpha() ]
if len(C_incontext_pos) != 0 :
C_incontext_pos_list.append(C_incontext_pos)
for C_context in C_context_exclude:
seq = seq.replace(C_context,".")
# relative pos
C_incontext_pos = [ index for index in range(0,len(seq)) if seq[index].isalpha() ]
# 0-based
start_pos_list.append(start_pos)
# 1-based
end_pos_list.append(start_pos + len(seq))
# calculate distance
# C_incontext_pos_up = C_incontext_pos[1:]
# C_incontext_pos_down = C_incontext_pos[:-1]
# C_incontext_dis = [ C_incontext_pos_up[index] - C_incontext_pos_down[index] for index in range(0,(len(C_incontext_pos_up)))]
# C_incontext_dis.insert(0,np.nan)
# C_incontext_dis.append(np.nan)
# C_incontext_dis_up = C_incontext_dis[:-1]
# C_incontext_dis_up_list.append(C_incontext_dis_up)
# C_incontext_dis_down = C_incontext_dis[1:]
# C_incontext_dis_down_list.append(C_incontext_dis_down)
seq = seq.replace(".","")
for C_context in C_context_include:
if C_context.isupper():
seq = seq.replace(C_context,"1")
else:
seq = seq.replace(C_context,"0")
seq_list.append([int(char) for char in seq])
strand_list.append(data["read1_strand"][index])
# out = pd.DataFrame({"chr":contig,"start":start_pos_list,"end":end_pos_list,"strand":strand_list,"seq":seq_list,"C_pos":C_incontext_pos_list,"dis_up":C_incontext_dis_up_list,"dis_down":C_incontext_dis_down_list})
out = pd.DataFrame({"chr":contig,"start":start_pos_list,"end":end_pos_list,"strand":strand_list,"seq":seq_list,"C_pos":C_incontext_pos_list})
# reads must include C incontext pos
out = out[out["seq"] != ""]
out.reset_index(drop=True, inplace=True)
out.to_csv(outfile,index=False,sep="\t",encoding='gbk',na_rep='NaN',header=None)