-
Notifications
You must be signed in to change notification settings - Fork 0
/
ParseCMscan1.6.py
205 lines (180 loc) · 10.8 KB
/
ParseCMscan1.6.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
204
205
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 26 07:57:27 2020
@author: mcveigh
"""
import pandas as pd
import Bio
import os
import sys
inputfile = sys.argv[1]
outputfile = sys.argv[2]
#Parse the final results of CMscan, sort and write fasta files
CMscan_output = (r'cmscan_final.tblout')
CMscan_df = pd.read_csv(CMscan_output,
sep='\t',
index_col=None,
low_memory=False,
usecols=[0,2,3,5,6,7,8,9,10,11,12,13,14,15,16,17],
header=None,
names=["gene", "accession","model", "mdl_from",
"mdl_to", "seq_from", "seq_to", "strand",
"trunc", "pass", "gc", "bias", "score",
"E-value", "Inc", "Length"])
#CMscan_df = CMscan_df[1:]
#print(CMscan_df.head(10))
#Remove 5S model rows
#Parse the final results of CMscan, sort and write fasta files
CMscan_df2 = CMscan_df[CMscan_df['gene'] != "5S_rRNA"]
#print(CMscan_df2.head(20))
#Find sequences on the minus strand
#NEEDS add a step to flip any sequences found on the minus strain
#Find sequences that have truncated models
#truncatedSSU = CMscan_df2[CMscan_df2['trunc'] == "5'&3'"]
truncated=CMscan_df2.loc[(CMscan_df2['gene'] != "5_8S_rRNA") & (CMscan_df2['trunc'] == "5'&3'")]
print("I found truncated models suggesting the presence of an intron\n ", truncated)
#truncatedLSU = CMscan_df2[CMscan_df2['trunc'] == "5'&3'"]
#print("I found truncated LSU models suggesting the presence of an intron\n ", truncatedLSU)
#Find sequences that do not pass CMscan tests
fail_test = CMscan_df2[CMscan_df2['Inc'] == "?"]
print("Sequences that have a ? are\n ", fail_test)
#show rows containing SSU or LSU
SSU_RNA_df = CMscan_df2[CMscan_df2['gene'] == "SSU_rRNA_eukarya"]
#print("these sequences have SSU\n ", SSU_RNA_df)
Five_RNA_df = CMscan_df2[CMscan_df2['gene'] == "5_8S_rRNA"]
FiveComplete = Five_RNA_df[Five_RNA_df['trunc'] == "no"]
FiveComplete['score'] = pd.to_numeric(FiveComplete['score'])
FiveCompleteStrongHit = FiveComplete[FiveComplete['score'] > 50]
FiveFivePartial = Five_RNA_df[Five_RNA_df['trunc'] == "5'"]
FiveThreePartial = Five_RNA_df[Five_RNA_df['trunc'] == "3'"]
FiveBothPartial = Five_RNA_df[Five_RNA_df['trunc'] == "5'&3'"]
LSUpartial = CMscan_df2.loc[(CMscan_df2['gene'] == "LSU_rRNA_eukarya") & (CMscan_df2['trunc'] != "no")]
LSUcomplete = CMscan_df2.loc[(CMscan_df2['gene'] == "LSU_rRNA_eukarya") & (CMscan_df2['trunc'] == "no")]
SSUpartial = CMscan_df2.loc[(CMscan_df2['gene'] == "SSU_rRNA_eukarya") & (CMscan_df2['trunc'] != "no")]
SSUcomplete = CMscan_df2.loc[(CMscan_df2['gene'] == "SSU_rRNA_eukarya") & (CMscan_df2['trunc'] == "no")]
five_minus_strand = FiveCompleteStrongHit[FiveCompleteStrongHit['strand'] != "+"]
LSUpartial_minus_strand = LSUpartial[LSUpartial['strand'] != "+"]
LSUcomplete_minus_strand = LSUcomplete[LSUcomplete['strand'] != "+"]
LSUframes = [LSUpartial_minus_strand, LSUcomplete_minus_strand]
LSUminus = pd.concat(LSUframes)
SSUpartial_minus_strand = SSUpartial[SSUpartial['strand'] != "+"]
SSUcomplete_minus_strand = SSUcomplete[SSUcomplete['strand'] != "+"]
SSUframes = [SSUpartial_minus_strand, SSUcomplete_minus_strand]
SSUminus = pd.concat(SSUframes)
print("I found 5.8S sequences on the minus strand\n ", five_minus_strand)
print("I found LSU sequences on the minus strand\n ", LSUminus)
print("I found SSU sequences on the minus strand\n ", SSUminus)
#print("LSU partial\n", LSUcomplete)
print("SSU complete\n", SSUcomplete)
print("LSU complete\n", LSUcomplete)
#print("These 5.8S rRNA are 5'&3'\n", FiveBothPartial)
#Sequences with extra sequence on the 5' the extends beyond position 1 of the SSU model
SSU_not5_end = SSU_RNA_df[SSU_RNA_df['seq_from'] != 1]
SSUextra = SSU_not5_end[SSU_not5_end['mdl_from'] == 1]
print("sequences with extra data on the 5' end \n", SSUextra)
#Sequnces with extra data on the 3' end beyond the end of LSU
LSU_RNA_df = CMscan_df2[CMscan_df2['gene'] == "LSU_rRNA_eukarya"]
LSUextra=LSU_RNA_df.loc[(LSU_RNA_df['seq_to'] != LSU_RNA_df['Length']) & (LSU_RNA_df['mdl_to'] == 3401) & (LSU_RNA_df['mdl_from'] == 1)]
print("sequences with extra data on the 3' end \n", LSUextra)
#SSU_LSUexact=LSU_RNA_df.loc[(LSU_RNA_df['seq_to'] == LSU_RNA_df['Length']) & (LSU_RNA_df['mdl_to'] == 3401) & (SSU_RNA_df['mdl_from'] == 1) & (SSU_RNA_df['seq_from'] == 1)]
#SSU_LSUpartial=LSU_RNA_df.loc[(LSU_RNA_df['seq_to'] == LSU_RNA_df['Length']) & (LSU_RNA_df['mdl_to'] < 3401) & (SSU_RNA_df['mdl_from'] > 1)]
#Trim the sequences with extra sequence flanking the SSU and LSU then rewrite the editted fasta to a new file
from Bio import SeqIO
sequences = []
for seq_record in SeqIO.parse("ribo-out/ribo-out.ribodbmaker.final.fa", "fasta"):
s = seq_record
if seq_record.id not in Five_RNA_df['accession'].tolist():
#sequences.remove(seq_record)
print("No 5.8S rRNA was found in ", seq_record.id)
#missingRNA.append(seq_record)
#elif seq_record.id in FiveBothPartial['accession'].tolist():
#print("Truncated 5.8S rRNA was found in ", seq_record.id)
#elif seq_record.id in FiveFivePartial['accession'].tolist():
#print("5' partial 5.8S rRNA was found in ", seq_record.id)
#elif seq_record.id in FiveThreePartial['accession'].tolist():
#print("3' partial 5.8S rRNA was found in ", seq_record.id)
if seq_record.id in FiveCompleteStrongHit['accession'].tolist():
if seq_record.id in LSUextra['accession'].tolist():
#start_df = CMscan_df2[(CMscan_df2['accession'] == seq_record.id) & (CMscan_df2['gene'] == 'SSU_rRNA_eukarya')]
#start = start_df['seq_from'].iloc[0]
to_df = CMscan_df2[(CMscan_df2['accession'] == seq_record.id) & (CMscan_df2['gene'] == 'LSU_rRNA_eukarya')]
to = to_df['seq_to'].iloc[0]
s.seq = s.seq[0:int(to)]
#seq_record.description = seq_record.description + " small subunit ribosomal RNA, internal transcribed spacer 1, 5.8S ribosomal RNA, internal transcribed spacer 2 and large subunit ribosomal RNA, complete sequence"
#seq_record.description = seq_record.description + " COMPLETE LSU"
#print(seq_record.id,seq_record.description)
if seq_record.id in SSUextra['accession'].tolist():
start_df = CMscan_df2[(CMscan_df2['accession'] == seq_record.id) & (CMscan_df2['gene'] == 'SSU_rRNA_eukarya')]
start = start_df['seq_from'].iloc[0]
to = start_df['Length'].iloc[0]
s.seq = s.seq[start-1:to]
#Check for Mixed Strand Sequences
if seq_record.id in FiveCompleteStrongHit['accession'].tolist():
if seq_record.id in five_minus_strand['accession'].tolist():
if seq_record.id in SSUpartial['accession'].tolist():
if seq_record.id not in SSUminus['accession'].tolist():
print("I found a mixed strand sequence", seq_record.id)
elif seq_record.id in SSUcomplete['accession'].tolist():
if seq_record.id not in SSUminus['accession'].tolist():
print("I found a mixed strand sequence", seq_record.id)
if seq_record.id in LSUpartial['accession'].tolist():
if seq_record.id not in LSUminus['accession'].tolist():
print("I found a mixed strand sequence", seq_record.id)
elif seq_record.id in LSUcomplete['accession'].tolist():
if seq_record.id not in LSUminus['accession'].tolist():
print("I found a mixed strand sequence", seq_record.id)
else: #Must be Plus Strand
if seq_record.id in SSUpartial['accession'].tolist():
if seq_record.id in SSUminus['accession'].tolist():
print("I found a mixed strand sequence", seq_record.id)
elif seq_record.id in SSUcomplete['accession'].tolist():
if seq_record.id in SSUminus['accession'].tolist():
print("I found a mixed strand sequence", seq_record.id)
if seq_record.id in LSUpartial['accession'].tolist():
if seq_record.id in LSUminus['accession'].tolist():
print("I found a mixed strand sequence", seq_record.id)
elif seq_record.id in LSUcomplete['accession'].tolist():
if seq_record.id in LSUminus['accession'].tolist():
print("I found a mixed strand sequence", seq_record.id)
if seq_record.id in FiveCompleteStrongHit['accession'].tolist():
if seq_record.id in five_minus_strand['accession'].tolist():
print("I reverse complemented ", seq_record.id)
s.seq = s.seq.reverse_complement()
if seq_record.id in SSUcomplete['accession'].tolist():
seq_record.description = seq_record.description + " SSU"
elif seq_record.id in SSUpartial['accession'].tolist():
seq_record.description = seq_record.description + " <SSU"
#if seq_record.id in FiveComplete['accession'].tolist():
if seq_record.id in SSU_RNA_df['accession'].tolist():
seq_record.description = seq_record.description + " ITS1 5.8S ITS2"
else:
seq_record.description = seq_record.description + " <ITS1 5.8S ITS2"
if seq_record.id in LSUcomplete['accession'].tolist():
seq_record.description = seq_record.description + " LSU"
elif seq_record.id in LSUpartial['accession'].tolist():
seq_record.description = seq_record.description + " LSU>"
else:
seq_record.description = seq_record.description + ">"
sequences.append(s)
SeqIO.write(sequences, outputfile, "fasta")
#SeqIO.write(missingRNA, "missing5_8.fsa", "fasta")
#Print seq_record.id and sequence length from output file
from Bio import SeqIO
for record in SeqIO.parse(outputfile, "fasta"):
tmp = LSUextra[LSUextra['accession'] == record.id]
tmp2 = SSUextra[SSUextra['accession'] == record.id]
tmpframe = [tmp, tmp2]
extra = pd.concat(tmpframe)
if not extra.empty:
length = len(record)
print("Sequences that were trimmed are: ", record.id, length)
#Add the new definition lines to the outputfile by appending text to seq_record.description
#Count the number of sequences in the final output file
fh = open(outputfile)
n = 0
for line in fh:
if line.startswith(">"):
n += 1
fh.close()
print("The number of sequences in the final output file is: ", n)
print ("processITS script is done and output is saved in:", outputfile)