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RhoTermPredict_algorithm.py
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RhoTermPredict_algorithm.py
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import numpy as np
import re
from Bio import SeqIO
from Bio.Seq import Seq
import csv
def palindrome_finder(sequence, file=None, gc_genome=None, strand=None, score=None, calc="no"):
n_rut = 0
list_scores = []
sequence = Seq(sequence)
sequence_r = str(sequence.reverse_complement())
_pattern_pause_site1 = "GG\D{8}[C,T]G"
_pattern_pause_site2 = "C[G,A]\D{8}CC"
for k in range(4, 8):
for i in range(150):
_x1 = i
_y1 = i + k
if _y1 > len(sequence):
break
else:
window = sequence[_x1:_y1]
window = str(window)
gc_content = 100*(window.count("C")+window.count("G"))/len(window)
score_p = score
sub_sequence_r = sequence_r
_start = 0
while True:
if re.search(window, sub_sequence_r):
_research = re.search(window, sub_sequence_r)
_positions = _research.span()
_x2 = _positions[0]
_y2 = _positions[1]
sub_sequence_r = sub_sequence_r[_y2:]
_x2 += _start
_y2 += _start
_start += _y2
if 4 <= len(sequence)-_y2-_y1+1 <= 8:
if calc == "yes":
loop = len(sequence)-_y2-_y1
file.write("\nPalindromic sequences found at coordinates ")
file.write(str(_x1))
file.write("-")
file.write(str(_y1-1))
file.write(" e ")
file.write(str(len(sequence)-_y2))
file.write("-")
file.write(str(len(sequence)-_x2-1))
file.write("(Sequence: ")
file.write(str(window))
file.write(")")
score_p += 3
if gc_content > gc_genome + 20:
score_p += 2
elif gc_content > gc_genome + 10:
score_p += 1
if len(window) > 4:
score_p += 1
if loop < 6:
score_p += 1
if strand == 1:
a = _x1-5
b = len(sequence)-_x2+5
seq_pause = str(sequence[a:b])
if re.search(_pattern_pause_site1, seq_pause):
score_p += 3
file.write(" (PAUSE-CONSENSUS PRESENT)")
if strand == -1:
a = _x1-5
b = len(sequence)-_x2+5
seq_pause = str(sequence[a:b])
if re.search(_pattern_pause_site2, seq_pause):
score_p += 3
file.write(" (PAUSE-CONSENSUS PRESENT)")
file.write(" (SCORE: ")
file.write(str(score_p))
file.write(")")
list_scores.append(score_p)
else:
n_rut += 1
else:
break
if calc == "yes":
if len(list_scores) > 0:
return np.max(list_scores)
else:
return 0
elif calc == "no":
return n_rut
sequences_to_analyze = {}
print("RhoTermPredict is a genome-wide predictor of transcription Rho-dependent terminators "
"in bacterial genomes. It analyzes both the strands.\n\n")
print("Input: Genome sequences file")
print("Output: a xlsx file containing Rho-dependent terminators coordinates and a txt file "
"containing informations about them")
print("Genome file must be in fasta format")
file_genome = input("Enter the input genome file name: ")
try:
for seq_record in SeqIO.parse(file_genome, "fasta"):
sequences_to_analyze[seq_record.id] = str(seq_record.seq)
num_sequences = len(sequences_to_analyze)
print(f"Sequences to analyze: {num_sequences}\n\n"
f" RhoTermPredict is working, please wait...")
for sq in sequences_to_analyze:
genome = sequences_to_analyze[sq]
genome = genome.upper()
gc_whole_genome = 100*(genome.count("G")+genome.count("C"))/len(genome)
pattern1 = "C\D{11,13}C\D{11,13}C\D{11,13}C\D{11,13}C\D{11,13}C"
pattern2 = "G\D{11,13}G\D{11,13}G\D{11,13}G\D{11,13}G\D{11,13}G"
pattern_pause_site1 = "GG\D{8}[C,T]G"
pattern_pause_site2 = "C[G,A]\D{8}CC"
predictions = 0
cg_value = []
scores = []
num = 1
cod = 1
predictions_file = open(f'predictions_coordinates_{sq}.csv', mode='a')
writer = csv.writer(predictions_file, delimiter='\t')
writer.writerow(['Region', 'Start RUT', 'End RUT', 'Strand'])
p = open(f"info_about_predictions_{sq}.txt", "a")
p.write("Sequences of predicted Rho-dependent terminators")
# positive strand
scale = 0
for j in range(len(genome)):
x1 = scale + j
x2 = scale + j + 78
if x2 > len(genome):
break
else:
w = genome[x1:x2]
if w.count("G") > 0:
numG = w.count("G")
c_over_g = w.count("C")/numG
else:
numG = 1
c_over_g = (w.count("C")+1)/numG
if c_over_g > 1:
if re.search(pattern1, w):
data = []
for g in range(50):
if x2+g <= len(genome):
w = genome[x1+g:x2+g]
if w.count("G") > 0:
numG = w.count("G")
c_over_g = w.count("C")/numG
else:
numG = 1
c_over_g = (w.count("C")+1)/numG
if c_over_g > 1:
if re.search(pattern1, w):
data.append(c_over_g)
else:
data.append(0)
else:
data.append(0)
else:
break
maxP = np.argmax(data)
c_over_g = np.max(data)
x1 = x1 + maxP
x2 = x2 + maxP
scale = x2 - j - 1
s = genome[x2:x2+150]
w = genome[x1:x2]
score = 3
ctrl = palindrome_finder(s)
if ctrl > 0 or re.search(pattern_pause_site1, s):
writer.writerow([f'T{cod}', x1, x2, 'plus'])
num += 1
predictions += 1
scale = x2 + 150 - j - 1
cg_value.append(c_over_g)
if c_over_g > 2:
score += 3
elif c_over_g > 1.50:
score += 2
elif c_over_g > 1.25:
score += 1
p.write("\n\n\nPREDICTED REGION NUMBER ")
p.write(str(cod))
p.write(" (STRAND POSITIVE) ")
p.write("\n\nGenomic sequence of putative RUT site (Coordinates: ")
p.write(str(x1))
p.write("-")
p.write(str(x2))
p.write(", c/g = ")
p.write(str(c_over_g))
p.write(" ): ")
p.write(str(w))
p.write("\n\nThe 150 nt long genomic region immediately downstream is ")
p.write(str(s))
p.write("\n")
cod += 1
final_score = palindrome_finder(s, p, gc_whole_genome, 1, score, calc="yes")
start = 0
while True:
if re.search(pattern_pause_site1, s):
research = re.search(pattern_pause_site1, s)
positions = research.span()
x2 = positions[0]
y2 = positions[1]
s = s[y2:]
p.write("\n\nPAUSE-CONSENSUS present at the coordinates ")
p.write(str(x2))
p.write("-")
p.write(str(y2))
x2 += start
y2 += start
else:
break
if final_score == 0:
final_score = score + 3
scores.append(final_score)
# negative strand
scale = 0
for j in range(len(genome)):
x1 = scale + j
x2 = scale + j + 78
if x2 > len(genome):
break
else:
w = genome[x1:x2]
if w.count("C") > 0:
numC = w.count("C")
c_over_g = w.count("G")/numC
else:
numC = 1
c_over_g = (w.count("G")+1)/numC
if c_over_g > 1:
if re.search(pattern2, w):
data = []
for g in range(50):
if x2+g <= len(genome):
w = genome[x1+g:x2+g]
if w.count("C") > 0:
numC = w.count("C")
c_over_g = w.count("G")/numC
else:
numC = 1
c_over_g = (w.count("G")+1)/numC
if c_over_g > 1:
if re.search(pattern2, w):
data.append(c_over_g)
else:
data.append(0)
else:
data.append(0)
else:
break
maxP = np.argmax(data)
c_over_g = np.max(data)
x1 = x1 + maxP
x2 = x2 + maxP
scale = x2 - j - 1
s = genome[x1-150:x1]
w = genome[x1:x2]
score = 3
ctrl = palindrome_finder(s)
if ctrl > 0 or re.search(pattern_pause_site2, s):
writer.writerow([f'T{cod}', x1, x2, 'minus'])
num += 1
predictions += 1
scale = x2 + 150 - j - 1
cg_value.append(c_over_g)
if c_over_g > 2:
score += 3
elif c_over_g > 1.50:
score += 2
elif c_over_g > 1.25:
score += 1
p.write("\n\n\nPREDICTED REGION NUMBER ")
p.write(str(cod))
p.write(" (STRAND NEGATIVE)")
p.write("\n\nGenomic sequence of putative RUT site (Coordinates: ")
p.write(str(x1))
p.write("-")
p.write(str(x2))
p.write(", c/g = ")
p.write(str(c_over_g))
p.write(" ): ")
p.write(str(w))
p.write("\n\nThe 150 nt long genomic region immediately downstream is ")
p.write(str(s))
p.write("\n")
cod += 1
final_score = palindrome_finder(s, p, gc_whole_genome, -1, score, calc="yes")
start = 0
while True:
if re.search(pattern_pause_site2, s):
research = re.search(pattern_pause_site2, s)
positions = research.span()
x2 = positions[0]
y2 = positions[1]
s = s[y2:]
p.write("\n\nPAUSE-CONSENSUS present at the coordinates ")
p.write(str(x2))
p.write("-")
p.write(str(y2))
x2 += start
y2 += start
else:
break
if final_score == 0:
final_score = score + 3
scores.append(final_score)
p.write("\n\n\n\n\n\nTotal number of predicted Rho-dependent terminators: ")
p.write(str(predictions))
p.write("\n\n\nMean C/G content of predicted terminators: ")
p.write(str(np.mean(cg_value)))
p.write("\nStandard deviation: ")
p.write(str(np.std(cg_value)))
p.close()
predictions_file.close()
print("Work finished, see output files in the current directory")
except IOError:
print(f"File {file_genome} not existent in the current directory!")