-
Notifications
You must be signed in to change notification settings - Fork 0
/
processITS1.5.1.py
219 lines (192 loc) · 10.8 KB
/
processITS1.5.1.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
206
207
208
209
210
211
212
213
214
215
216
217
218
219
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 24 08:39:05 2020
@author: mcveigh
"""
#
# processITS designed to validate ITS sequences. Sequences that pass all tests are saved to a outfile file in fasta format
#Selectively sort GenBank flatfiles removing selected seqeunces and saving them to a new fasta file
# User must specify the input filename and the outputfile name
import pandas as pd
import Bio
import os
import sys
from datetime import datetime
startTime = datetime.now()
print("Start time is ", startTime)
inputfile = sys.argv[1]
outputfile = sys.argv[2]
#print(inputfile)
#print(outputfile)
#Read in the reject list and find the accession
reject_file_name = (r'ITS_reject_seqs3.txt')
#reject_file_name = (r'/panfs/pan1.be-md.ncbi.nlm.nih.gov/dnaorg/ITS/ITS_reject_seqs')
rejectlist_df = pd.read_csv(reject_file_name, sep='\t', index_col=None, low_memory=False, header=None, names=["accession", "type", "reason"])
rejectlist = rejectlist_df['accession']
reject_list = set(rejectlist_df['accession'].tolist())
#Parse the GenBank file and remove any sequences found on the reject list
from Bio import SeqIO
sequences = []
found = []
#missingRNA = []
rejectTime = datetime.now()
print("Reject time is ", rejectTime)
for seq_record in SeqIO.parse(inputfile, "genbank"):
str_id = seq_record.id
#print(seq_record.id)
#if str_id.find('.') != -1:
# str_id = str_id[:str_id.find('.')]
if seq_record.name not in reject_list:
seq_record.description = seq_record.annotations["organism"]
sequences.append(seq_record)
else:
found.append(seq_record)
print("I found this accession on the reject list and wrote the sequence to found.fsa: ", seq_record.id)
SeqIO.write(sequences, "stripped.fsa", "fasta")
SeqIO.write(found, "found.fsa", "fasta")
#Run short version of ribodbmaker.pl
os.system("ribodbmaker.pl -f --skipfribo1 --skipfribo2 --skipfmspan --skipingrup --skipclustr --skiplistms --skipmstbl --taxin /panfs/pan1/dnaorg/rrna/git-ncbi-rrna-project/taxonomy-files/ncbi-taxonomy-tree.ribodbmaker.txt stripped.fsa ribo-out")
riboTime = datetime.now()
print("ribodbmaker time is ", riboTime)
#Run CMscan using the output of ribodbmaker.pl
print('cmscan1')
os.system("cmscan --cpu 4 --mid -T 20 --verbose --tblout tblout.df.txt rrna.cm ribo-out/ribo-out.ribodbmaker.final.fa > cmscanOUTPUT.df.txt &")
print('cmscan2')
os.system("cmscan --cpu 4 --mid -T 20 --verbose --anytrunc --tblout tblout.at.txt rrna.cm ribo-out/ribo-out.ribodbmaker.final.fa > cmscanOUTPUT.at.txt")
cmscanTime = datetime.now()
print("CMscan time is ", cmscanTime)
os.system("cat tblout.df.txt tblout.at.txt > tblout.both.txt")
os.system("perl cmsearch_tblout_deoverlap/cmsearch-deoverlap.pl --maxkeep -s --cmscan tblout.both.txt")
os.system("head -n2 tblout.both.txt > final.tblout")
os.system("cat tblout.both.txt.deoverlapped >> final.tblout")
#Add seq length to cmscan output
os.system("esl-seqstat -a ribo-out/ribo-out.ribodbmaker.final.fa | grep ^\\= | awk '{ printf(\"%s %s\\n\", $2, $3); }' > my.seqlen")
os.system("perl tblout-add.pl -t final.tblout 18 my.seqlen 3 > cmscan_final.tblout")
#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
CMscan_df2 = CMscan_df[CMscan_df['gene'] != "5S_rRNA"]
#print(CMscan_df2.head(20))
#Find sequences on the minus strand
minus_strand = CMscan_df2[CMscan_df2['strand'] != "+"]
print("I found sequences on the minus strand ", minus_strand)
#NEEDS add a step to flip any sequences found on the minus strain
#Find sequences that have truncated models
truncated = CMscan_df2[CMscan_df2['trunc'] == "5'&3'"]
print("I found truncated models suggesting the presence of an intron ", truncated)
#print(truncated['accession'])
#Possible action needed here
#Find sequences that do not pass CMscan tests
fail_test = CMscan_df2[CMscan_df2['Inc'] == "?"]
print("Sequences that have a ? are ", fail_test)
#Possible action needed here to exclude some sequences
#show rows containing SSU or LSU
SSU_RNA_df = CMscan_df2[CMscan_df2['gene'] == "SSU_rRNA_eukarya"]
#print("these sequences have SSU ", SSU_RNA_df)
Five_RNA_df = CMscan_df2[CMscan_df2['gene'] == "5_8S_rRNA"]
FiveComplete = Five_RNA_df[Five_RNA_df['trunc'] == "no"]
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")]
print("These 5.8S rRNA are 5' partial\n", FiveFivePartial)
print("These 5.8S rRNA are 3' partial\n", FiveThreePartial)
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)
else:
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: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():
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]
s.seq = s.seq[start-1:to]
else:
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]
#seq_record.description = seq_record.description + " COMPLETE SSU"
#else:
if seq_record.id in 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 FiveFivePartial['accession'].tolist():
seq_record.description = seq_record.description + " <5.8S ITS2"
if seq_record.id in FiveThreePartial['accession'].tolist():
seq_record.description = seq_record.description + " ITS1 5.8S"
elif seq_record.id in FiveBothPartial['accession'].tolist():
seq_record.description = seq_record.description + " 5.8S truncated"
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")
#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]
if not tmp.empty:
length = len(record)
print("Sequences that were trimmed are: ", record.id, length)
#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)