encodings = []
header = ['#']
for g in range(gap + 1):
for f in features:
header.append(f + '.gap' + str(g))
encodings.append(header)
for i in fastas:
name, sequence = i[0], re.sub('-', '', i[1])
code = [name]
if len(sequence) < 2 * gap + 3:
print(
'Error: for "KSCTriad" encoding, the input fasta sequences should be greater than (2*gap+3). \n\n'
)
return 0
code = code + CalculateKSCTriad(sequence, gap, features, AADict)
encodings.append(code)
return encodings
if __name__ == '__main__':
if len(sys.argv) == 1:
print(USAGE)
sys.exit(1)
fastas = readFasta.readFasta(sys.argv[1])
k = int(sys.argv[2]) if len(sys.argv) >= 3 else 5
output = sys.argv[3] if len(sys.argv) >= 4 else 'encoding.tsv'
encodings = KSCTriad(fastas, k)
saveCode.savetsv(encodings, output)
def CTDCClass(fastas, groups):
encodings = []
header = ['#']
for g in range(len(groups)):
header.append('g.'+str(g+1))
encodings.append(header)
for i in fastas:
name, sequence = i[0], re.sub('-', '', i[1])
code = [name]
for group in groups:
code.append(Count(group, sequence) / len(sequence))
encodings.append(code)
return encodings
if __name__ == '__main__':
if len(sys.argv) < 5:
print(USAGE)
sys.exit(1)
groups = sys.argv[3:]
myStr = ''.join(groups)
myStr = re.sub('[^ACDEFGHIKLMNPQRSTVWY]', '', myStr)
if len(myStr) != 20 or len(set(myStr)) != 20:
print('\nERROR: The amino acid must be no-repeat in each groups and the sum is 20!\n')
fastas = readFasta.readFasta(sys.argv[1])
encodings = CTDCClass(fastas, groups)
saveCode.savetsv(encodings, sys.argv[2])
sum([
Rvalue(sequence[j], sequence[j + n], AADict, AAProperty1)
for j in range(len(sequence) - n)
]) / (len(sequence) - n) if (len(sequence) - n) != 0 else 0)
myDict = {}
for aa in AA:
myDict[aa] = sequence.count(aa)
code = code + [myDict[aa] / (1 + w * sum(theta)) for aa in AA]
code = code + [(w * j) / (1 + w * sum(theta)) for j in theta]
encodings.append(code)
return encodings
fastas = readFasta.readFasta('datasets_fasta/dataset_balanceado.txt')
encodings = PAAC(fastas)
saveCode.savetsv(encodings, 'BBDD/encodings_dataset_balanceado.tsv')
fastas = readFasta.readFasta('datasets_fasta/All_peptides.txt')
encodings = PAAC(fastas)
saveCode.savetsv(encodings, 'BBDD/encodings_Allpeptides.tsv')
fastas = readFasta.readFasta('datasets_fasta/dataset_ejemplo.csv')
encodings = PAAC(fastas)
saveCode.savetsv(encodings, 'BBDD/encodings_dataset_ejemplo.tsv')
