def designer2():
if request.method == 'POST':
old_seq = request.form['input']
new_seq = Seq(old_seq)
new_seq = str(new_seq.reverse_complement())
new_seq = new_seq.replace('T', 'U').upper()
output = "Here is your resulting sequence: 3'-ACAACAUUAUCGGGGGUUUUGACCUGGAAGGUGUUG" + new_seq + "-5'"
#this script implements your input seqn into the chassis of the crRNA
return render_template('designer.html', output=output)
def generate_se_sequence_combination(df_SE, dfname, dbname, rna_db, het,
exclude, output_name):
version = get_version(rna_db)
if (version == 'swissprot'):
db = 'sp'
else:
db = version
trans_seq_dict, trans_coding_dict = generate_all_se_db.get_trans_seq_dict(
rna_db)
print("transcript sequence dict ready")
print(len(list(trans_seq_dict.items())))
df = pd.read_csv(dfname, sep='\t', header=None)
change_df = generate_all_mutation_db.extract_transcript_change(df)
trans_index_dict = {}
for i in range(0, change_df.shape[0]):
if (change_df.iloc[i]['mrna'] not in trans_index_dict.keys()):
trans_index_dict[change_df.iloc[i]['mrna']] = [i]
else:
trans_index_dict[change_df.iloc[i]['mrna']].append(i)
trans_records, strand_dict, protein_id_dict = generate_all_se_db.get_trans_records(
dbname, trans_seq_dict)
splicing_event_dict = generate_all_se_db.get_splicing_event_dict(
df_SE, trans_records)
print("splicing events dict ready")
trans_exon_dict = generate_all_se_db.get_transcript_exon_dict(
trans_records)
print("transcript exon dict ready")
middle = time.time()
print("---- %s minutes ----" % ((middle - begin) / 60))
trans_db_annotation_dict = {} ##output
cnt = 0
trans_records = trans_records.sort_values(by=['exon_id'], axis=0)
for k in splicing_event_dict.keys():
cnt += 1
if (cnt % 1000 == 0):
print(cnt)
down_id = splicing_event_dict[k][0]
skip_id = splicing_event_dict[k][1]
up_id = splicing_event_dict[k][2]
for key in trans_exon_dict.keys():
T = trans_exon_dict[key]
if ((down_id in T) and (up_id in T)):
#print(key)
down_index = T.index(down_id)
#print(down_index)
up_index = T.index(up_id)
low_index = min(down_index, up_index)
high_index = max(down_index, up_index)
tmp_exon_coordinate_dict = {}
tmp_exon_position_dict = {}
tmp_pos = 0
for item in range(1, len(T)):
ei = generate_all_se_db.find_key_index(
'exon_id', T[item], trans_records)
if (ei == -1):
print("the exon id cannot be found")
else:
tmp_start = trans_records.iloc[ei]['start']
tmp_end = trans_records.iloc[ei]['end']
tmp_exon_coordinate_dict[T[item]] = (
trans_records.iloc[ei]['chr'], tmp_start, tmp_end)
tmp_exon_position_dict[item] = (tmp_pos,
tmp_pos + tmp_end -
tmp_start + 1)
tmp_pos += tmp_end - tmp_start + 1
tmp_seq = trans_seq_dict[key]
coding_start = trans_coding_dict[key][0]
coding_end = trans_coding_dict[key][1]
het_list = []
hom_position_list = []
if ((key in trans_index_dict.keys())
and (key in strand_dict.keys())):
shift = 0
transcript = tmp_seq
hom_des = []
for i in trans_index_dict[key]:
if (change_df.iloc[i]['mutation_type'].find('snv') !=
-1):
if (change_df.iloc[i]['snp_type'] == 'hom'):
hom_position_list.append(
(int(change_df.iloc[i]['c_start']),
change_df.iloc[i]['mutation_type']))
if (strand_dict[key] == '-'):
tmp = Seq(
str(tmp_seq),
IUPAC.ambiguous_dna).complement()
tmp = str(tmp)
transcript=generate_all_mutation_db.change_seq(tmp,int(change_df.iloc[i]['c_start'])+shift+coding_start-1,\
int(change_df.iloc[i]['c_end'])+shift+coding_start-1,str(Seq(str(change_df.iloc[i]['c_content'])).complement()),\
change_df.iloc[i]['mutation_type'])
else:
transcript=generate_all_mutation_db.change_seq(tmp_seq,int(change_df.iloc[i]['c_start'])+shift+coding_start-1,\
int(change_df.iloc[i]['c_end'])+shift+coding_start-1,change_df.iloc[i]['c_content'],\
change_df.iloc[i]['mutation_type'])
hom_des.append(change_df.iloc[i]['mutation_type']+":"+str(change_df.iloc[i]['c_start'])+'-'+\
str(change_df.iloc[i]['c_end'])+str(change_df.iloc[i]['c_content'])+'_')
# if(change_df.iloc[i]['mutation_type'].find('del')!=-1):
# shift-=(int(change_df.iloc[i]['c_end'])-int(change_df.iloc[i]['c_start'])+1)
# elif(change_df.iloc[i]['mutation_type'].find('ins')!=-1):
# shift+=(int(change_df.iloc[i]['c_end'])-int(change_df.iloc[i]['c_start'])+1)
else:
het_list.append(i)
het_number = len(het_list)
het_des = []
het_seqs = []
if (len(hom_position_list) != 0):
if (strand_dict[key] == '-'):
tmp = Seq(str(transcript),
IUPAC.ambiguous_dna).complement()
tmp = str(tmp)
else:
tmp = transcript
het_seqs.append(tmp)
else:
if (strand_dict[key] == '-'):
transcript = Seq(str(transcript),
IUPAC.ambiguous_dna).complement()
transcript = str(transcript)
het_seqs.append(transcript)
het_des.append("")
if (het == 1):
for n in range(0, het_number):
tmp_het_des = []
new_sequence = ""
splicing_position = 0
if ((high_index - low_index) == 1):
splicing_position = tmp_exon_position_dict[
low_index][1]
elif (T[low_index + 1] == skip_id):
splicing_position = tmp_exon_position_dict[
low_index + 1][0]
if (abs(
int(change_df.iloc[het_list[n]]['c_start'])
- splicing_position) <= 900):
if ((int(change_df.iloc[het_list[n]]
['c_start']) >= coding_start - 1) &
(int(change_df.iloc[het_list[n]]
['c_start']) <= coding_end)):
if (strand_dict[key] == '-'):
new_sequence=generate_all_mutation_db.change_seq(transcript,int(change_df.iloc[het_list[n]]['c_start'])+shift+coding_start-1,\
int(change_df.iloc[het_list[n]]['c_end'])+shift+coding_start-1,\
str(Seq(str(change_df.iloc[het_list[n]]['c_content'])).complement()),\
change_df.iloc[het_list[n]]['mutation_type'])
else:
new_sequence=generate_all_mutation_db.change_seq(transcript,int(change_df.iloc[het_list[n]]['c_start'])+shift+coding_start-1,\
int(change_df.iloc[het_list[n]]['c_end'])+shift+coding_start-1,change_df.iloc[het_list[n]]['c_content'],\
change_df.iloc[het_list[n]]['mutation_type'])
tmp_het_des.append(change_df.iloc[het_list[n]]['mutation_type']+":"+\
str(change_df.iloc[het_list[n]]['c_start'])+'-'+str(change_df.iloc[het_list[n]]['c_end'])+\
str(change_df.iloc[het_list[n]]['c_content']))
# if(change_df.iloc[i]['mutation_type'].find('del')!=-1):
# shift-=(int(change_df.iloc[i]['c_end'])-int(change_df.iloc[i]['c_start'])+1)
# elif(change_df.iloc[i]['mutation_type'].find('ins')!=-1):
# shift+=(int(change_df.iloc[i]['c_end'])-int(change_df.iloc[i]['c_start'])+1)
het_des.append(tmp_het_des)
if (strand_dict[key] == '-'):
new_seq = Seq(
str(new_sequence),
IUPAC.ambiguous_dna).complement()
new_seq = str(new_seq)
else:
new_seq = new_sequence
het_seqs.append(new_seq)
elif (het == 2):
for n in range(0, het_number):
tmp_het_des = []
new_sequence = ""
splicing_position = 0
if ((high_index - low_index) == 1):
splicing_position = tmp_exon_position_dict[
low_index][1]
elif (T[low_index + 1] == skip_id):
splicing_position = tmp_exon_position_dict[
low_index + 1][0]
if (abs(
int(change_df.iloc[het_list[n]]['c_start'])
- splicing_position) <= 900):
if ((int(change_df.iloc[het_list[n]]
['c_start']) >= coding_start - 1) &
(int(change_df.iloc[het_list[n]]
['c_start']) <= coding_end)):
if (strand_dict[key] == '-'):
new_sequence=generate_all_mutation_db.change_seq(transcript,int(change_df.iloc[het_list[n]]['c_start'])+shift+coding_start-1,\
int(change_df.iloc[het_list[n]]['c_end'])+shift+coding_start-1,\
str(Seq(str(change_df.iloc[het_list[n]]['c_content'])).complement()),\
change_df.iloc[het_list[n]]['mutation_type'])
else:
new_sequence=generate_all_mutation_db.change_seq(transcript,int(change_df.iloc[het_list[n]]['c_start'])+shift+coding_start-1,\
int(change_df.iloc[het_list[n]]['c_end'])+shift+coding_start-1,change_df.iloc[het_list[n]]['c_content'],\
change_df.iloc[het_list[n]]['mutation_type'])
tmp_het_des.append(change_df.iloc[het_list[n]]['mutation_type']+":"+\
str(change_df.iloc[het_list[n]]['c_start'])+'-'+str(change_df.iloc[het_list[n]]['c_end'])+\
str(change_df.iloc[het_list[n]]['c_content']))
# if(change_df.iloc[i]['mutation_type'].find('del')!=-1):
# shift-=(int(change_df.iloc[i]['c_end'])-int(change_df.iloc[i]['c_start'])+1)
# elif(change_df.iloc[i]['mutation_type'].find('ins')!=-1):
# shift+=(int(change_df.iloc[i]['c_end'])-int(change_df.iloc[i]['c_start'])+1)
het_des.append(tmp_het_des)
for j in range(n, len(het_list)):
tmp_new_sequence = new_sequence
tmp_new_des = tmp_het_des
if (j != n):
if (abs(
int(change_df.iloc[het_list[j]]
['c_start']) -
splicing_position) <= 900):
if ((int(change_df.iloc[het_list[j]]
['c_start']) >=
coding_start - 1) &
(int(change_df.iloc[het_list[j]]
['c_start']) <= coding_end)):
if (strand_dict[key] == '-'):
tmp_new_sequence=generate_all_mutation_db.change_seq(tmp_new_sequence,int(change_df.iloc[het_list[j]]['c_start'])+shift,\
int(change_df.iloc[het_list[j]]['c_end'])+shift,\
str(Seq(str(change_df.iloc[het_list[j]]['c_content'])).complement()),\
change_df.iloc[het_list[j]]['mutation_type'])
else:
tmp_new_sequence=generate_all_mutation_db.change_seq(tmp_new_sequence,int(change_df.iloc[het_list[j]]['c_start'])+shift,\
int(change_df.iloc[het_list[j]]['c_end'])+shift,change_df.iloc[het_list[j]]['c_content'],\
change_df.iloc[het_list[j]]['mutation_type'])
tmp_new_des.append(change_df.iloc[het_list[j]]['mutation_type']+":"+\
str(change_df.iloc[het_list[j]]['c_start'])+'-'+str(change_df.iloc[het_list[j]]['c_end'])+\
str(change_df.iloc[het_list[j]]['c_content']))
het_des.append(tmp_new_des)
if (strand_dict[key] == '+'):
new_seq = tmp_new_sequence
else:
new_seq = str(
Seq(str(tmp_new_sequence),
IUPAC.ambiguous_dna).
complement())
het_seqs.append(new_seq)
for c in range(
0, len(het_seqs)
): ##tmp_cnt is the number of modified sequences
tmp_seq = het_seqs[c]
if ((high_index - low_index) == 1):
position = tmp_exon_position_dict[low_index][1]
if (position >= coding_start):
db_seq = tmp_seq[coding_start - 1:]
if (db_seq not in
trans_db_annotation_dict.keys()):
trans_db_annotation_dict[db_seq] = [
key,
str(k) + "_exclusive_from_" +
str(position - coding_start + 1)
]
else:
des_tmp = str(
k) + "_exclusive_from_" + str(
position - coding_start + 1)
if (des_tmp not in
trans_db_annotation_dict[db_seq]):
trans_db_annotation_dict[
db_seq].append(des_tmp)
tmp_anno = ""
for d in hom_des:
tmp_anno += d
for d in het_des[c]:
tmp_anno += d
trans_db_annotation_dict[db_seq].append(
tmp_anno)
#tmp_info=exon_coordinate_dict[skip_id]
tmp_skip_id_index = generate_all_se_db.find_key_index(
'exon_id', skip_id, trans_records)
tmp_info=(trans_records.iloc[tmp_skip_id_index]['chr'],trans_records.iloc[tmp_skip_id_index]['start'],\
trans_records.iloc[tmp_skip_id_index]['end'])
skip_seq = generate_all_se_db.fetch_exon_seq(
tmp_info[0], tmp_info[1], tmp_info[2],
T[0])
trans_prime_seq = generate_all_se_db.add_exon(
tmp_seq, skip_seq, position)
db_prime_seq = trans_prime_seq[coding_start -
1:]
if (db_prime_seq not in
trans_db_annotation_dict.keys()):
trans_db_annotation_dict[db_prime_seq]=[key,"modified_"+str(k)+"_inclusive_from_"\
+str(position-coding_start+1)+"_to_"+\
str(position-coding_start+1+len(skip_seq))]
else:
des_tmp="modified_"+str(k)+"_inclusive_from_"+\
str(position-coding_start+1)+"_to_"+\
str(position-coding_start+1+len(skip_seq))
if (des_tmp
not in trans_db_annotation_dict[
db_prime_seq]):
trans_db_annotation_dict[
db_prime_seq].append(des_tmp)
tmp_anno = ""
for d in hom_des:
tmp_anno += d
for d in het_des[c]:
tmp_anno += d
trans_db_annotation_dict[db_prime_seq].append(
tmp_anno)
elif (T[low_index + 1] == skip_id):
skip_exon_start = tmp_exon_position_dict[low_index
+ 1][0]
skip_exon_end = tmp_exon_position_dict[low_index +
1][1]
if (skip_exon_start >= coding_start):
db_seq = tmp_seq[coding_start - 1:]
if (db_seq not in
trans_db_annotation_dict.keys()):
trans_db_annotation_dict[db_seq]=[key,str(k)+"_inclusive_from_"+str(skip_exon_start-coding_start+1)\
+"_to_"+str(skip_exon_end-coding_start+1)]
else:
des_tmp=str(k)+"_inclusive_from_"+str(skip_exon_start-coding_start+1)\
+"_to_"+str(skip_exon_end-coding_start+1)
if (des_tmp not in
trans_db_annotation_dict[db_seq]):
trans_db_annotation_dict[
db_seq].append(des_tmp)
tmp_anno = ""
for d in hom_des:
tmp_anno += d
for d in het_des[c]:
tmp_anno += d
trans_db_annotation_dict[db_seq].append(
tmp_anno)
trans_prime_seq = generate_all_se_db.remove_exon(
tmp_seq, skip_exon_start, skip_exon_end)
db_prime_seq = trans_prime_seq[coding_start -
1:]
if (db_prime_seq not in
trans_db_annotation_dict.keys()):
trans_db_annotation_dict[db_prime_seq] = [
key, "modified_" + str(k) +
"_exclusive_from_" +
str(skip_exon_start - coding_start + 1)
]
else:
des_tmp="modified_"+str(k)+"_exclusive_from_"+\
str(skip_exon_start-coding_start+1)
if (des_tmp
not in trans_db_annotation_dict[
db_prime_seq]):
trans_db_annotation_dict[
db_prime_seq].append(des_tmp)
tmp_anno = ""
for d in hom_des:
p = get_variant_pos_from_des(d)
if p not in range(skip_exon_start,
skip_exon_end):
tmp_anno += d
for d in het_des[c]:
p = get_variant_pos_from_des(d)
if p not in range(skip_exon_start,
skip_exon_end):
tmp_anno += d
trans_db_annotation_dict[db_prime_seq].append(
tmp_anno)
if ((len(hom_position_list) == 0) and (len(het_list) == 0)):
coding_start = trans_coding_dict[key][0]
tmp_exon_coordinate_dict = {}
tmp_exon_position_dict = {}
tmp_pos = 0
for item in range(1, len(T)):
ei = generate_all_se_db.find_key_index(
'exon_id', T[item], trans_records)
if (ei == -1):
print("the exon id cannot be found")
else:
tmp_start = trans_records.iloc[ei]['start']
tmp_end = trans_records.iloc[ei]['end']
tmp_exon_coordinate_dict[T[item]] = (
trans_records.iloc[ei]['chr'], tmp_start,
tmp_end)
tmp_exon_position_dict[item] = (tmp_pos,
tmp_pos + tmp_end -
tmp_start + 1)
tmp_pos += tmp_end - tmp_start + 1
if ((high_index - low_index) == 1):
position = tmp_exon_position_dict[low_index][1]
if (position >= coding_start):
db_seq = tmp_seq[coding_start - 1:]
if (db_seq not in trans_db_annotation_dict.keys()):
trans_db_annotation_dict[db_seq] = [
key,
str(k) + "_exclusive_from_" +
str(position - coding_start + 1)
]
else:
des_tmp = str(k) + "_exclusive_from_" + str(
position - coding_start + 1)
if (des_tmp not in
trans_db_annotation_dict[db_seq]):
trans_db_annotation_dict[db_seq].append(
des_tmp)
tmp_skip_id_index = generate_all_se_db.find_key_index(
'exon_id', skip_id, trans_records)
tmp_info=(trans_records.iloc[tmp_skip_id_index]['chr'],trans_records.iloc[tmp_skip_id_index]['start'],\
trans_records.iloc[tmp_skip_id_index]['end'])
skip_seq = generate_all_se_db.fetch_exon_seq(
tmp_info[0], tmp_info[1], tmp_info[2], T[0])
trans_prime_seq = generate_all_se_db.add_exon(
tmp_seq, skip_seq, position)
db_prime_seq = trans_prime_seq[coding_start - 1:]
if (db_prime_seq
not in trans_db_annotation_dict.keys()):
trans_db_annotation_dict[db_prime_seq]=[key,"modified_"+str(k)+"_inclusive_from_"\
+str(position-coding_start+1)+"_to_"+\
str(position-coding_start+1+len(skip_seq))]
else:
des_tmp="modified_"+str(k)+"_inclusive_from_"+\
str(position-coding_start+1)+"_to_"+\
str(position-coding_start+1+len(skip_seq))
if (des_tmp not in trans_db_annotation_dict[
db_prime_seq]):
trans_db_annotation_dict[
db_prime_seq].append(des_tmp)
elif (T[low_index + 1] == skip_id):
skip_exon_start = tmp_exon_position_dict[low_index +
1][0]
skip_exon_end = tmp_exon_position_dict[low_index +
1][1]
if (skip_exon_start >= coding_start):
db_seq = tmp_seq[coding_start - 1:]
if (db_seq not in trans_db_annotation_dict.keys()):
trans_db_annotation_dict[db_seq]=[key,str(k)+"_inclusive_from_"+str(skip_exon_start-coding_start+1)\
+"_to_"+str(skip_exon_end-coding_start+1)]
else:
des_tmp=str(k)+"_inclusive_from_"+str(skip_exon_start-coding_start+1)\
+"_to_"+str(skip_exon_end-coding_start+1)
if (des_tmp not in
trans_db_annotation_dict[db_seq]):
trans_db_annotation_dict[db_seq].append(
des_tmp)
trans_prime_seq = generate_all_se_db.remove_exon(
tmp_seq, skip_exon_start, skip_exon_end)
db_prime_seq = trans_prime_seq[coding_start - 1:]
if (db_prime_seq
not in trans_db_annotation_dict.keys()):
trans_db_annotation_dict[db_prime_seq] = [
key,
"modified_" + str(k) + "_exclusive_from_" +
str(skip_exon_start - coding_start + 1)
]
else:
des_tmp="modified_"+str(k)+"_exclusive_from_"+\
str(skip_exon_start-coding_start+1)
if (des_tmp not in trans_db_annotation_dict[
db_prime_seq]):
trans_db_annotation_dict[
db_prime_seq].append(des_tmp)
trans_records = trans_records.sort_values(by=['trans_id'])
my_seqs = []
# my_transcripts=[]
modified_trans = []
mutation_seq_cnt = 0
for k in trans_db_annotation_dict.keys():
annotation_list = trans_db_annotation_dict[k]
trans_id = annotation_list[0]
modified_trans.append(trans_id)
#coding_start=trans_coding_dict[trans_id][0]
#print(trans_id)
pid = protein_id_dict[trans_id][0]
gid = protein_id_dict[trans_id][1]
tmp_id = db + "|" + pid + '|' + trans_id + '|' + gid
des = ''
flag = 0
for an in range(1, len(annotation_list)):
if (annotation_list[an].find('snv') != -1):
flag = 1
des += annotation_list[an] + '|'
# my_transcripts.append(SeqRecord(Seq(str(k),IUPAC.ambiguous_dna),id=tmp_id,description=des))
if (flag == 1):
mutation_seq_cnt += 1
new_seq = str(
Seq(str(k),
IUPAC.ambiguous_dna).transcribe().translate(to_stop=True))
while (new_seq.find('None') != -1):
new_seq = new_seq.replace('None', '')
my_seqs.append(
SeqRecord(Seq(str(new_seq), IUPAC.protein),
id=tmp_id,
description=des))
print("The number of sequences with mutation generated is " +
str(mutation_seq_cnt))
if (exclude != True):
original_cnt = 0
for t in trans_seq_dict.keys():
if t not in modified_trans:
coding_start = int(trans_coding_dict[t][0])
coding_end = int(trans_coding_dict[t][1])
trans_seq = str(trans_seq_dict[t])
trans_seq = trans_seq[coding_start - 1:]
if (trans_seq not in trans_db_annotation_dict.keys()):
pid = protein_id_dict[t][0]
gid = protein_id_dict[t][1]
new_sequence = trans_seq
new_seq = str(
Seq(str(new_sequence),
IUPAC.ambiguous_dna).transcribe().translate(
to_stop=True))
# else:
# new_seq=str(Seq(str(new_sequence),IUPAC.ambiguous_dna).complement().transcribe().translate(to_stop=True))
my_seqs.append(SeqRecord(Seq(str(new_seq),IUPAC.protein),\
id=db+'|'+pid+'|'+t+'|'+gid+'_0:'+str(coding_start)+'-'+str(coding_end),\
description="no splicing"))
original_cnt += 1
print("The number of original sequences is " + str(original_cnt))
print("The number of sequences generated is " + str(len(my_seqs)))
handle = open(output_name + ".fasta", "w")
#handle=open(dataset_name+"_all_se_db"+".fasta","w")
for sequence in my_seqs:
SeqIO.write(sequence, handle, "fasta")
handle.close()
# handle_trans=open("../data/"+dataset_name+"/"+dataset_name+"_all_se_transcripts"+".fasta","w")
# for sequence in my_transcripts:
# SeqIO.write(sequence,handle_trans,"fasta")
# handle_trans.close()
#return my_seqs
#dbname="../data/gencode.v28.basic.annotation.gff3"
#SE_name=argv[1]
#dfname=argv[2]
#rna_db=argv[3]
#output_name=argv[4]
#
#df_SE=pd.read_csv(SE_name,sep='\t')
#generate_se_sequence_combination(df_SE,dfname,dbname,rna_db,output_name)
##
#finish=time.time()
#print("---- %s minutes ----" % ((finish-begin)/60))
else:
start = "<" + col4
ext = "5' Partial CDS"
stop = col5
if col7 == "-":
if dico_end[col9] in stopCodons:
stop = col4
else:
stop = ">" + col4
if ext != "":
ext = "Partial CDS"
else:
ext = "3' Partial CDS"
start = col5
list_tmp_gene.append(
int(start.replace("<", "").replace(">", "")))
list_tmp_gene.append(
int(stop.replace("<", "").replace(">", "")))
list_tmp_cds.append(start + "\t" + stop + "\t" +
"CDS")
dico_tmp_gff[
int(col4) +
1] = col1 + "\t" + col2 + "\t" + "CDS" + "\t" + col4 + "\t" + col5 + "\t" + col6 + "\t" + col7 + "\t" + col8 + "\t" + "Parent=" + col9 + ".1 CDS " + ext
if c > 1 and c < int(dicog.get(col9)):
if size % 3 == 0:
if col7 == "+":
start = col4
stop = col5
if col7 == "-":
start = col5
PlusTwoORFs.remove(orf)
elif orf2[1] in orf[1]:
if orf2 in PlusTwoORFs:
PlusTwoORFs.remove(orf2)
# The user can choose to have the number of ORFs found in each reading frame
# printed for inspection
#print('Number of +1 ORFs: %s' %len(PlusOneORFs))
#print('Number of +2 ORFs: %s' %len(PlusTwoORFs))
# The results are printed for the user. The files can be saved to a tab-delimited file
# by the python3 GeneratePreexistingORFControls.py >[filename] in the terminal.
for orf in PlusOneORFs:
orfSeq = orf[1]
protein = Seq(orfSeq).translate()
protein = str(protein)
noCysProtein = protein.replace('C', '')
noCysProteinLen = len(noCysProtein)
output = [orf[0], orf[1], protein, noCysProtein, '+1', noCysProteinLen]
print(*output, sep='\t')
for orf in PlusTwoORFs:
orfSeq = orf[1]
protein = Seq(orfSeq).translate()
protein = str(protein)
noCysProtein = protein.replace('C', '')
noCysProteinLen = len(noCysProtein)
output = [orf[0], orf[1], protein, noCysProtein, '+2', noCysProteinLen]
#print(*output,sep='\t')
