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FourierClass.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import numpy as np
import scipy.stats
import statistics
import argparse
from Bio import SeqIO
from scipy.fftpack import fft, ifft
import warnings
warnings.filterwarnings("ignore")
#############################################################################
#############################################################################
def header():
dataset = open(foutput, 'a')
dataset.write("nameseq,average,median,maximum,minimum,peak,"
+ "none_levated_peak,sample_standard_deviation,population_standard_deviation,"
+ "percentile15,percentile25,percentile50,percentile75,amplitude,"
+ "variance,interquartile_range,semi_interquartile_range,"
+ "coefficient_of_variation,skewness,kurtosis,label")
dataset.write("\n")
return
def file_record():
dataset = open(foutput, 'a')
dataset.write("%s," % (str(name_seq)))
for metric in features:
dataset.write("%s," % (metric))
# dataset.write("{0:.4f},".format(metric))
dataset.write(label_dataset)
dataset.write("\n")
print("Sequence Analyzed!!")
return
def feature_extraction():
global features
features = []
average = sum(spectrum)/len(spectrum)
features.append(average)
###################################
median = np.median(spectrum)
features.append(median)
###################################
maximum = np.max(spectrum)
features.append(maximum)
###################################
minimum = np.min(spectrum)
features.append(minimum)
###################################
peak = (len(spectrum)/3)/(average)
features.append(peak)
###################################
peak_two = (len(spectrumTwo)/3)/(np.mean(spectrumTwo))
features.append(peak_two)
###################################
standard_deviation = np.std(spectrum) # standard deviation
features.append(standard_deviation)
###################################
standard_deviation_pop = statistics.stdev(spectrum) # population sample standard deviation
features.append(standard_deviation_pop)
###################################
percentile15 = np.percentile(spectrum, 15)
features.append(percentile15)
###################################
percentile25 = np.percentile(spectrum, 25)
features.append(percentile25)
###################################
percentile50 = np.percentile(spectrum, 50)
features.append(percentile50)
###################################
percentile75 = np.percentile(spectrum, 75)
features.append(percentile75)
###################################
amplitude = maximum - minimum
features.append(amplitude)
###################################
# mode = statistics.mode(spectrum)
###################################
variance = statistics.variance(spectrum)
features.append(variance)
###################################
interquartile_range = np.percentile(spectrum, 75) - np.percentile(spectrum, 25)
features.append(interquartile_range)
###################################
semi_interquartile_range = (np.percentile(spectrum, 75) - np.percentile(spectrum, 25))/2
features.append(semi_interquartile_range)
###################################
coefficient_of_variation = standard_deviation/average
features.append(coefficient_of_variation)
###################################
skewness = (3 * (average - median))/standard_deviation
features.append(skewness)
###################################
kurtosis = (np.percentile(spectrum, 75) - np.percentile(spectrum, 25)) / (2 * (np.percentile(spectrum, 90) - np.percentile(spectrum, 10)))
features.append(kurtosis)
###################################
return
def binary_fourier():
header()
global spectrum, spectrumTwo, name_seq
for seq_record in SeqIO.parse(finput, "fasta"):
seq = seq_record.seq
seq = seq.upper()
name_seq = seq_record.name
spectrum = []
spectrumTwo = []
A = []
C = []
T = []
G = []
for nucle in seq:
if nucle == "A":
A.append(1)
else:
A.append(0)
if nucle == "C":
C.append(1)
else:
C.append(0)
if nucle == "T" or nucle =="U":
T.append(1)
else:
T.append(0)
if nucle == "G":
G.append(1)
else:
G.append(0)
FA = fft(A)
FC = fft(C)
FT = fft(T)
FG = fft(G)
for i in range(len(seq)):
specTotal = (abs(FA[i])**2) + (abs(FC[i])**2) + (abs(FT[i])**2) + (abs(FG[i])**2)
specTwo = (abs(FA[i])) + (abs(FC[i])) + (abs(FT[i])) + (abs(FG[i]))
spectrum.append(specTotal)
spectrumTwo.append(specTwo)
feature_extraction()
file_record()
###################################
########## Debug ##################
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# print(seq)
# print("\n")
# print("A -- %s" % (A))
# print("C -- %s" % (C))
# print("T -- %s" % (T))
# print("G -- %s" % (G))
# print("\n")
# print("A -- %s" % (abs(FA)))
# print("C -- %s" % (abs(FC)))
# print("T -- %s" % (abs(FT)))
# print("G -- %s" % (abs(FG)))
# print("\n")
# print(spectrumTwo)
# fig = plt.figure()
# ax = plt.axes()
# ax.plot(spectrum)
###################################
###################################
return
def zcurve_fourier():
header()
global spectrum, spectrumTwo, name_seq
for seq_record in SeqIO.parse(finput, "fasta"):
seq = seq_record.seq
seq = seq.upper()
name_seq = seq_record.name
spectrum = []
spectrumTwo = []
###################################
###################################
R = 0 # x[n] = (An + Gn) − (Cn + Tn) ≡ Rn − Yn
Y = 0
M = 0 # y[n] = (An + Cn) − (Gn + Tn) ≡ Mn − Kn
K = 0
W = 0 # z[n] = (An + Tn) − (Cn + Gn) ≡ Wn − Sn
S = 0
###################################
###################################
x = []
y = []
z = []
for nucle in seq:
if nucle == "A" or nucle == "G":
R += 1
x.append((R)-(Y))
else:
Y += 1
x.append((R)-(Y))
if nucle == "A" or nucle == "C":
M += 1
y.append((M)-(K))
else:
K += 1
y.append((M)-(K))
if nucle == "A" or nucle == "T" or nucle == "U":
W += 1
z.append((W)-(S))
else:
S += 1
z.append((W)-(S))
FX = fft(x)
FY = fft(y)
FZ = fft(z)
for i in range(len(seq)):
specTotal = (abs(FX[i])**2) + (abs(FY[i])**2) + (abs(FZ[i])**2)
specTwo = (abs(FX[i])) + (abs(FY[i])) + (abs(FZ[i]))
spectrum.append(specTotal)
spectrumTwo.append(specTwo)
feature_extraction()
file_record()
###################################
########## Debug ##################
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# print (seq)
# print("\n")
# print("X -- %s" % (x))
# print("Y -- %s" % (y))
# print("Z -- %s" % (z))
# print("\n")
# print("X -- %s" % (abs(FX)))
# print("Y -- %s" % (abs(FY)))
# print("Z -- %s" % (abs(FZ)))
# print("\n")
# print(spectrum)
# print("\n")
# print(spectrumTwo)
# fig = plt.figure()
# ax = plt.axes()
# ax.plot(spectrum)
###################################
###################################
return
def integer_fourier():
header()
global spectrum, spectrumTwo, name_seq
for seq_record in SeqIO.parse(finput, "fasta"):
seq = seq_record.seq
seq = seq.upper()
name_seq = seq_record.name
spectrum = []
spectrumTwo = []
integer = []
for nucle in seq:
if nucle == "T" or nucle == "U":
integer.append(0)
elif nucle == "C":
integer.append(1)
elif nucle == "A":
integer.append(2)
else:
integer.append(3)
FI = fft(integer)
for i in range(len(seq)):
specTotal = (abs(FI[i])**2)
specTwo = (abs(FI[i]))
spectrum.append(specTotal)
spectrumTwo.append(specTwo)
feature_extraction()
file_record()
###################################
########## Debug ##################
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# print(seq)
# print("\n")
# print("I -- %s" % (integer))
# print("\n")
# print("I -- %s" % (abs(FI)))
# print("\n")
# print(spectrum)
# fig = plt.figure()
# ax = plt.axes()
# ax.plot(spectrum)
###################################
###################################
return
def real_fourier():
header()
global spectrum, spectrumTwo, name_seq
for seq_record in SeqIO.parse(finput, "fasta"):
seq = seq_record.seq
seq = seq.upper()
name_seq = seq_record.name
spectrum = []
spectrumTwo = []
real = []
for nucle in seq:
if nucle == "T" or nucle == "U":
real.append(1.5)
elif nucle == "C":
real.append(0.5)
elif nucle == "A":
real.append(-1.5)
else:
real.append(-0.5)
FR = fft(real)
for i in range(len(seq)):
specTotal = (abs(FR[i])**2)
specTwo = (abs(FR[i]))
spectrum.append(specTotal)
spectrumTwo.append(specTwo)
feature_extraction()
file_record()
###################################
########## Debug ##################
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# print(seq)
# print("\n")
# print("R -- %s" % (real))
# print("\n")
# print("R -- %s" % (abs(FR)))
# print("\n")
# print(spectrum)
# fig = plt.figure()
# ax = plt.axes()
# ax.plot(spectrum)
###################################
###################################
return
def eiip_fourier():
header()
global spectrum, spectrumTwo, name_seq
for seq_record in SeqIO.parse(finput, "fasta"):
seq = seq_record.seq
seq = seq.upper()
name_seq = seq_record.name
spectrum = []
spectrumTwo = []
eiip = []
for nucle in seq:
if nucle == "T" or nucle == "U":
eiip.append(0.1335)
elif nucle == "C":
eiip.append(0.1340)
elif nucle == "A":
eiip.append(0.1260)
else:
eiip.append(0.0806)
Feiip = fft(eiip)
for i in range(len(seq)):
specTotal = (abs(Feiip[i])**2)
specTwo = (abs(Feiip[i]))
spectrum.append(specTotal)
spectrumTwo.append(specTwo)
feature_extraction()
file_record()
###################################
########## Debug ##################
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# print(seq)
# print("\n")
# print("R -- %s" % (eiip))
# print("\n")
# print("R -- %s" % (abs(Feiip)))
# print("\n")
# print(spectrum)
# fig = plt.figure()
# ax = plt.axes()
# ax.plot(spectrum)
###################################
###################################
return
def complex_number():
header()
global spectrum, spectrumTwo, name_seq
for seq_record in SeqIO.parse(finput, "fasta"):
seq = seq_record.seq
seq = seq.upper()
name_seq = seq_record.name
spectrum = []
spectrumTwo = []
complexNumber = []
for nucle in seq:
if nucle == "T" or nucle == "U":
complexNumber.append(1-1j)
elif nucle == "C":
complexNumber.append(-1+1j)
elif nucle == "A":
complexNumber.append(1+1j)
else:
complexNumber.append(-1-1j)
FcomplexNumber = fft(complexNumber)
for i in range(len(seq)):
specTotal = (abs(FcomplexNumber[i])**2)
specTwo = (abs(FcomplexNumber[i]))
spectrum.append(specTotal)
spectrumTwo.append(specTwo)
feature_extraction()
file_record()
###################################
########## Debug ##################
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# print(seq)
# print("\n")
# print("R -- %s" % (complexNumber))
# print("\n")
# print("R -- %s" % (abs(FcomplexNumber)))
# print("\n")
# print(spectrum)
# fig = plt.figure()
# ax = plt.axes()
# ax.plot(spectrum)
###################################
###################################
return
def atomic_number():
header()
global spectrum, spectrumTwo, name_seq
for seq_record in SeqIO.parse(finput, "fasta"):
seq = seq_record.seq
seq = seq.upper()
name_seq = seq_record.name
spectrum = []
spectrumTwo = []
atomicNumber = []
for nucle in seq:
if nucle == "T" or nucle == "U":
atomicNumber.append(66)
elif nucle == "C":
atomicNumber.append(58)
elif nucle == "A":
atomicNumber.append(70)
else:
atomicNumber.append(78)
FatomicNumber = fft(atomicNumber)
for i in range(len(seq)):
specTotal = (abs(FatomicNumber[i])**2)
specTwo = (abs(FatomicNumber[i]))
spectrum.append(specTotal)
spectrumTwo.append(specTwo)
feature_extraction()
file_record()
###################################
########## Debug ##################
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# spectrum.pop(0)
# spectrum.pop(len(spectrum)-1)
# print(seq)
# print("\n")
# print("R -- %s" % (atomicNumber))
# print("\n")
# print("R -- %s" % (abs(FcomplexNumber)))
# print("\n")
# print(spectrum)
# fig = plt.figure()
# ax = plt.axes()
# ax.plot(spectrum)
###################################
###################################
return
#############################################################################
if __name__ == "__main__":
print("\n")
print("###################################################################################")
print("########## Feature Extraction: A Fourier and Numerical Mapping Approach ###########")
print("########## Arguments: -i input -o output -l label -r representation ###########")
print("########## -r: 1 = Binary, 2 = Z-curve, 3 = Real, 4 = Integer, 5 = EIIP #########")
print("########## 6 = ComplexNumber, 7 = Atomic Number ###########")
print("########## Author: Robson Parmezan Bonidia ###########")
print("###################################################################################")
print("\n")
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', help='Fasta format file | E.g., train.fasta')
parser.add_argument('-o', '--output', help='Csv format file | E.g., train.csv')
parser.add_argument('-l', '--label', help='Dataset Label | E.g., lncRNA, mRNA, sncRNA ...')
parser.add_argument('-r', '--representation', help='1 = Binary, 2 = Z-curve, 3 = Real, 4 = Integer, 5 = EIIP, 6 = Complex Number, 7 = Atomic Number')
args = parser.parse_args()
finput = str(args.input)
foutput = str(args.output)
label_dataset = str(args.label)
representation = int(args.representation)
if representation == 1:
binary_fourier()
elif representation == 2:
zcurve_fourier()
elif representation == 3:
real_fourier()
elif representation == 4:
integer_fourier()
elif representation == 5:
eiip_fourier()
elif representation == 6:
complex_number()
elif representation == 7:
atomic_number()
else:
print("This package does not contain this representation")
#############################################################################