-
Notifications
You must be signed in to change notification settings - Fork 0
/
parse_netMHC.py
191 lines (178 loc) · 7.43 KB
/
parse_netMHC.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
#!/usr/bin/python
# -*- coding: UTF-8 -*-
###########netMHC result parsing and filter based on binding affinity and FPKM #########
import collections,getopt,math,os,sys
import pandas as pd
def correct_RNA_quant(df1):
df = df1
mtpep_idx = df.columns.get_loc("MT_pep")
quant_idx = df.columns.get_loc("Quantification")
mtpep2quant = collections.defaultdict(int)
for idx in range(len(df)):
mtpep = df.iloc[idx,mtpep_idx]
quant = df.iloc[idx,quant_idx]
mtpep2quant[mtpep] += int(quant)
for idx in range(len(df)):
mtpep = df.iloc[idx,mtpep_idx]
df.iloc[idx,quant_idx] = mtpep2quant[mtpep]
return df
opts,args=getopt.getopt(sys.argv[1:],"i:g:o:b:l:p:",["input_dir","input_fasta","out_dir","binding_affinity_cutoff","hla_str","prefix"])
input_dir=""
input_fasta=""
out_dir=""
binding_affinity_cutoff=500
hla_str=""
prefix=""
USAGE='''usage: python parse_netMHC.py -i <input_dir> -g <input_fasta> -o <out_dir>
-b <binding_affinity_cutoff> -l <hla_str> -p <prefix>
required argument:
-i | --input_dir : input file,result from netMHC
-g | --input_fasta : input fasta file for netMHC
-o | --out_dir : output directory
-b | --binding_affinity_cutoff : pipetide binding affinity cutoff , default: 500 nM
-l | --hla_str : hla type string derived from opitype
-p | --prefix : prefix of output file'''
for opt,value in opts:
if opt =="h":
print (USAGE)
sys.exit(2)
elif opt in ("-i","--input_dir"):
input_dir=value
elif opt in ("-g","--input_fasta"):
input_fasta=value
elif opt in ("-o","--out_dir"):
out_dir =value
elif opt in ("-b","--binding_affinity_cutoff"):
binding_affinity_cutoff =value
elif opt in ("-l","hla_str"):
hla_str=value
elif opt in ("-p","prefix"):
prefix=value
if (input_dir =="" or input_fasta =="" or out_dir =="" or hla_str==""):
print (USAGE)
sys.exit(2)
#######extract full animo acid change##
Full_type_mutation=[]
Full_identity=[]
with open(input_fasta) as f:
data=f.read()
mutID2tpm = {}
for rawline in data.strip().split('\n'):
line = rawline.strip().split()[0]
if not line.startswith('>'): continue
full_type_mutation=line.split('_')[0].split('>')[1]
full_identity=line.split('>')[1]
Full_type_mutation.append(full_type_mutation)
Full_identity.append(full_identity)
mutID = '_'.join(line[1:].split('_')[0:2])
for i, subline in enumerate(rawline.split()):
if i > 0 and len(subline.split('=')) == 2:
key, val = subline.split('=')
if key == 'TPM': mutID2tpm[mutID] = float(val)
#print(mutID2tpm)
dup_type_mutation=[]
dup_full_identity=[]
hla_num=len(hla_str.split(','))
i=0
while i<hla_num:
for j in range(len(Full_identity)):
dup_type_mutation.append(Full_type_mutation[j])
dup_full_identity.append(Full_identity[j])
i=i+1
######## extract candidate neoantigens####
with open(input_dir+"/"+prefix+"_bindaff_raw.tsv") as f:
data = f.read()
nw_data = data.split('-----------------------------------------------------------------------------------\n')
WT_header = []
MT_header = []
WT_neo = []
MT_neo = []
for i in range(len(nw_data)):
if i%4 == 3:
mt_pro_name = nw_data[i].strip('\n').split('.')[0]
MT_header.append(mt_pro_name)
elif i%4 == 2:
mt_neo_data = nw_data[i].strip().split('\n')
MT_neo.append(mt_neo_data)
WB_SB_MT_record = []
Identity = []
count=0
for i in range(len(MT_neo)):
for j in range(len(MT_neo[i])):
if MT_neo[i][j] == '----------------------------------------':
continue
is_SNV_or_INDEL = (("SNV_" in MT_neo[i][j]) or ("INDEL" in MT_neo[i][j]) or ("INS_" in MT_neo[i][j]) or ("DEL_" in MT_neo[i][j]))
if MT_neo[i][j].endswith('WB') or MT_neo[i][j].endswith('SB'):
if is_SNV_or_INDEL:
Identity.append(str(count))
else:
Identity.append("NA")
WB_SB_MT_record.append(MT_neo[i][j])
if is_SNV_or_INDEL:
count+=1
with open(input_dir+"/"+prefix+"_snv_indel_bindaff_wt.tsv") as f:
data = f.read()
nw_data = data.split('-----------------------------------------------------------------------------------\n')
WT_header = []
WT_neo = []
for i in range(len(nw_data)):
if i%4 == 3:
wt_pro_name = nw_data[i].strip('\n').split('.')[0]
WT_header.append(wt_pro_name)
elif i%4 == 2:
wt_neo_data = nw_data[i].strip().split('\n')
WT_neo.append(wt_neo_data)
wt_bindaff_list=[]
wt_list=[]
for i in range(len(WT_neo)):
for j in range(len(WT_neo[i])):
if "----" in WT_neo[i][j]:
continue
wt_bindaff_list.append(WT_neo[i][j].strip().split()[15])
wt_list.append(WT_neo[i][j].strip().split()[2])
data_form = []
data_form_tmp = []
for i in range(len(WB_SB_MT_record)):
line=[]
mt_record = [line for line in WB_SB_MT_record[i].split(' ') if line!='']
assert len(mt_record) > 10, F'The {i}-th mutation peptide {mt_record} is invalid!'
HLA_tp = mt_record[1]
mt_pep = mt_record[2]
wt_pep = mt_record[3]
mt_binding_aff= mt_record[15]
mt_binding_level_des = mt_record[-1]
iden = mt_record[10]
wt_binding_aff = ""
if "SNV" in mt_record[10] or "INDEL" in mt_record[10] or "INS" in mt_record[10] or "DEL" in mt_record[10]:
wt_pos = int(Identity[i])
wt_pep = wt_list[wt_pos]
wt_binding_aff = wt_bindaff_list[wt_pos]
if wt_pep == mt_pep:
continue
assert len(str(mt_record[10]).strip().split('_')) > 2, F'The {i}-th mutation peptide {mt_record} is invalid (error-2)!'
#tpm = float(str(mt_record[10]).strip().split('_')[2])/10
iden = iden.split("_")[0]+"_"+iden.split("_")[1]
tpm = mutID2tpm[iden]
line = [HLA_tp,mt_pep,wt_pep,float(mt_binding_aff),mt_binding_level_des,iden,tpm]
line_tmp = [HLA_tp,mt_pep,wt_pep,float(mt_binding_aff),mt_binding_level_des,iden,tpm,wt_binding_aff]
data_form.append(line)
data_form_tmp.append(line_tmp)
if (i % 200) == 0: print("finish append")
f=lambda x: x.split('.')[0]
fields = ['HLA_type','MT_pep','WT_pep','BindAff','BindLevel','Identity','Quantification']
######neoantigens filtering binding affinity#####
data= pd.DataFrame(data_form)
data.columns=fields
final_filter_data=data[(data.BindAff<float(binding_affinity_cutoff))] # filter binding affinity
final_filter_data = correct_RNA_quant(final_filter_data)
final_filter_data=final_filter_data.drop_duplicates(subset=['HLA_type','MT_pep','WT_pep','BindAff','BindLevel'])
final_filter_data.to_csv(out_dir+"/"+prefix+"_bindaff_filtered.tsv",header=1,sep='\t',index=0)
data_tmp= pd.DataFrame(data_form_tmp)
fields.append("WT_BindAff")
data_tmp.columns=fields
final_filter_data_tmp=data_tmp[(data_tmp.BindAff<float(binding_affinity_cutoff))] # filter binding affinity
final_filter_data_tmp = correct_RNA_quant(final_filter_data_tmp)
final_filter_data_tmp=final_filter_data_tmp.drop_duplicates(subset=['HLA_type','MT_pep','WT_pep','BindAff','BindLevel'])
if not os.path.exists(out_dir+"/tmp_identity"):
os.mkdir(out_dir+"/tmp_identity")
final_filter_data_tmp.to_csv(out_dir+"/tmp_identity/"+prefix+"_bindaff_filtered.tsv",header=1,sep='\t',index=0)