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wiNNerprediction.py
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wiNNerprediction.py
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r"""To predict YB spectra and outputs TSV predicted Intensities.
This script reads data from a text file containing at least three columns:
Peptide sequence, Charge and Fragmentation.
It can predict y and b ions intensities for tryptic unmodified peptide, For this use wiNNer_model for predictions.
It can also predict y and b ions intensities for non-tryptic peptides with modifications specific to ancient samples, for this use ancient_model for predictions.
Example usage for the ancient model:
python wiNNerprediction.py \
-i exampleset.txt \
-o output.txt \
-d ancient_model \
-s ModifiedSequence
"""
import numpy as np
import keras as K
from keras.models import load_model
import keras.backend as K
import sys, getopt
import re
import pandas as pd
import os
_MOL_WEIGHTS = {
'-': 0.0,
'A': 71.03711,
'C': 103.00919 + 57.02146, # Add fixed CME modification to the Cys mass.
'E': 129.04259,
'D': 115.02694,
'G': 57.02146,
'F': 147.06841,
'I': 113.08406,
'H': 137.05891,
'K': 128.09496,
'M': 131.04049,
'L': 113.08406,
'N': 114.04293,
'Q': 128.05858,
'P': 97.05276,
'S': 87.03203,
'R': 156.10111,
'T': 101.04768,
'W': 186.07931,
'V': 99.06841,
'Y': 163.06333,
'M(ox)': 15.994915,
'P(ox)': 15.994915,
'W(ox)': 15.994915,
'S(ph)': 79.966331,
'T(ph)': 79.966331,
'Y(ph)': 79.966331,
'H(hiasp)': -22.031969,
'H(higlu)': -25.016319,
'(gl)E': -18.010565,
'(gl)Q': -17.026549,
'R(ar)': -42.021798,
'(gl)Q(de)': 67,
'C(ca)': 57.0214637236,
'N(de)': 0.9840155848,
'Q(de)': 0.9840155848,
'M(di)': 31.9898292442,
'W(di)': 31.9898292442}
#_RES = re.compile(r'(\((\w+)\))[A-Z]?')
_RESIDUE = re.compile(r'(\(gl\))Q(\(de\))|[A-Z](\((\w+)\))?|(\((\w+)\))[A-Z]?')
mol_weights = pd.Series(_MOL_WEIGHTS)
alphabet = [k for k in mol_weights.keys()]
one_hot_encoding = pd.get_dummies(alphabet).astype(int).to_dict(orient='list')
one_hot_encoding_re = dict((one_hot_encoding[i].index(1), i) for i in one_hot_encoding)
class createWindowData(object):
_daa = dict()
_sequence = ""
_yIons = []
_bIons = []
_yIonsNorm = []
_bIonsNorm = []
_matrix = []
def __init__(self, sequence, ions, intensities):
self._sequence = sequence
# Creating Amino Matrix
seq = []
for residue in re.finditer(_RESIDUE, self._sequence):
# print(residue.group())
sequence_list = residue.group()
seq.append(sequence_list)
self.yIons = [0] * len(seq)
self.bIons = [0] * len(seq)
lstions = ions.split(";")
lstintensities = intensities.split(";")
yIonsreg = re.compile('^y[0-9]+$')
bIonsreg = re.compile('^b[0-9]+$')
self._yIons = [0] * len(seq)
self._bIons = [0] * len(seq)
for index, ion in enumerate(lstions):
if yIonsreg.match(ion):
self._yIons[int(ion.split("y")[1])] = float(lstintensities[index])
if bIonsreg.match(ion):
self._bIons[int(ion.split("b")[1])] = float(lstintensities[index])
list_b_ions = self._bIons[1:]
list_b_ions.append(0)
list_y_ions = self._yIons[::-1]
self._bIons = list_b_ions
self._yIons = list_y_ions
self._matrix = []
def GenerateMatrix(self, size):
dictFeature = dict()
order = []
size = int(size)
for index in range(((size) // 2) - 1):
key = "Sj-" + str(index + 1)
dictFeature[key] = ["?"] * 20
order.append((index + 1) * -1)
##############
dictFeature["Sj"] = ["?"] * 20
##############
for index in range((size) // 2):
key = "Sj+" + str(index + 1)
dictFeature[key] = ["?"] * 20
order.append(index + 1)
##########################
dictFeature["Sj+1"] = ["?"] * 20
dictFeature["Sj+2"] = ["?"] * 20
############################
dictFeature["S1"] = ["?"] * 20
dictFeature["SN"] = ["?"] * 20
dictFeature["length"] = "?"
dictFeature["Dist-1"] = "?"
dictFeature["Dist-N"] = "?"
Sj_Positive = re.compile('^Sj\+')
Sj_Negative = re.compile('^Sj-')
order.append(0)
order = sorted(order)
sequence = []
for residue in re.finditer(_RESIDUE, self._sequence):
sequence_list = residue.group()
sequence.append(sequence_list)
for index, aa in enumerate(sequence):
for k in dictFeature:
val = 0
if Sj_Negative.match(k):
val = int(k[3:]) * -1
if Sj_Positive.match(k):
val = int(k[3:])
if (index + val) < 0 or (index + val) >= len(sequence):
dictFeature[k] = one_hot_encoding["-"]
else:
dictFeature[k] = one_hot_encoding[sequence[index+val]]
# dictFeature["Sj"]
dictFeature["Sj"] = one_hot_encoding[sequence[index]]
# dictFeature["S1"]
dictFeature["S1"] = one_hot_encoding[sequence[0]]
# dictFeature["SN"]
dictFeature["SN"] = one_hot_encoding[sequence[len(sequence)-1]]
# dictFeature["length"]
dictFeature["length"] = len(sequence)
# dictFeature["Dist-1"]="?"
dictFeature["Dist-1"] = index
# dictFeature["Dist-N"]="?"
dictFeature["Dist-N"] = len(sequence) - index - 1
col = []
for i in order:
if (i < 0):
col += list(dictFeature["Sj" + str(i)])
if (i == 0):
col += list(dictFeature["Sj"])
if (i > 0):
col += list(dictFeature["Sj+" + str(i)])
col += list(dictFeature["S1"]) + list(dictFeature["SN"]) + [dictFeature["length"]] + [dictFeature["Dist-1"] + 1] + [dictFeature["Dist-N"]]
res = [int(i) for i in col]
self._matrix.append(res)
def nomalizeIones(self):
self._yIonsNorm = [float(i) / max(self._yIons + self._bIons) for i in self._yIons]
self._bIonsNorm = [float(i) / max(self._yIons + self._bIons) for i in self._bIons]
def GenerateDataset(self, removeZeros):
self.nomalizeIones()
data = {'featureMatrix': [], 'target_Y': [], 'target_B': []}
for index, line in enumerate(self._matrix):
if removeZeros == True:
if (self._yIonsNorm[index] != 0):
data['featureMatrix'].append(line)
data['target_Y'].append(np.log2(1 + (10000 * (self._yIonsNorm[index]))))
if (self._bIonsNorm[index] != 0):
data['target_B'].append(np.log2(1+(10000*(self._bIonsNorm[index]))))
else:
data['featureMatrix'].append(line)
data['target_Y'].append(np.log2(1 + (10000 * (self._yIonsNorm[index]))))
# data['featureMatrix_B'].append(line)
data['target_B'].append(np.log2(1+(10000*(self._bIonsNorm[index]))))
data['target_Y'] = list(map(float, data['target_Y']))
data['target_B'] = list(map(float, data['target_B']))
# Correction Bug
data['featureMatrix'] = data['featureMatrix'][:-1]
data['target_Y'] = data['target_Y'][:-1]
data['target_B'] = data['target_B'][:-1]
return data
def pearson_correlation(y_true, y_pred):
return 1
def batch_size_shape(y_true, y_pred):
return K.shape(y_true)[1]
def TestNN(model_name, feature_matrix):
model_1 = load_model(model_name, custom_objects={'pearson_correlation': pearson_correlation,
'batch_size_shape': batch_size_shape}) # load the saved model
y_score = model_1.predict(feature_matrix, batch_size=32)
return (y_score)
def mean(listofNumpyArrays):
return np.array(listofNumpyArrays).mean(axis=0)
def Predict(model_file, lstsequences,fragmentation_method, charge, outputfile):
# General variables, depending on the fragmentation method the length of the sequences and the SVR models are differents.
suffix = ".h5"
if (fragmentation_method == "CID" and charge <= "2"):
max_length = 50
max_D1 = 50
max_DN = 50
Models = [
os.path.join(model_file,"model_cid2"+suffix)
]
elif (fragmentation_method == "CID" and charge >= "3"):
max_length = 50
max_D1 = 50
max_DN = 50
Models = [
os.path.join(model_file,"model_cid3"+suffix)
]
elif (fragmentation_method == "HCD" and charge <= "2"):
max_length = 50
max_D1 = 50
max_DN = 50
Models = [
os.path.join(model_file,"model_hcd2"+suffix)
]
elif (fragmentation_method == "HCD" and charge >= "3"):
max_length = 50
max_D1 = 50
max_DN = 50
Models = [
os.path.join(model_file, "model_hcd3" + suffix)
]
feature_matrix = []
result_matrix = []
target_matrix = []
for seq in lstsequences:
myTrainingMatrix = createWindowData(seq, "b1","1") # (seq,"b1","1") -> in order to create a window.. it is needed to provide at least One Ion, does not matter is not real (We don't need intensities)
myTrainingMatrix.GenerateMatrix(24)
d = myTrainingMatrix.GenerateDataset(removeZeros=False)
dm = d['featureMatrix']
dmy = d['target_Y']
dmb = d['target_B']
zipped = zip(dmy, dmb)
list_c = list(zipped)
for ix, ra in enumerate(list_c):
target_matrix.append(ra)
for index, r in enumerate(dm):
res = [seq, fragmentation_method, charge, "b+" + str(r[-2]), "y+" + str(r[-1])]
r[-3] = r[-3] * 1.0/max_length
r[-2] = r[-2] * 1.0/max_D1
r[-1] = r[-1] * 1.0/max_DN
feature_matrix.append(r)
result_matrix.append(res)
m_yb = []
for m in Models:
Col_BYIons = TestNN(m, np.array(feature_matrix)).tolist()
m_yb.append(Col_BYIons)
for idx, r in enumerate(result_matrix):
r.append(Col_BYIons[idx][1])
r.append(Col_BYIons[idx][0])
dictOutput = dict()
for row in result_matrix:
seq1 = []
k = str(row[0] + "-" + row[1] + "-" + row[2])
bindex = int(row[3].split("+")[1]) - 1
yindex = int(row[4].split("+")[1]) - 1
bintensity = row[5]
yintensity = row[6]
for residue in re.finditer(_RESIDUE, row[0]):
sequence_list = residue.group()
seq1.append(sequence_list)
if k not in dictOutput.keys():
dictOutput[k] = ([0] * (len(seq1) - 1), [0] * (len(seq1) - 1),[0] * (len(seq1) - 1),[0] * (len(seq1) - 1))
dictOutput[k][0][bindex] = bintensity
dictOutput[k][1][yindex] = yintensity
else:
dictOutput[k][0][bindex] = bintensity
dictOutput[k][1][yindex] = yintensity
with open(outputfile, 'a') as file:
for k in dictOutput:
listb = ["bXXX_charge1-noloss"] * len(dictOutput[k][0])
listy = ["yXXX_charge1-noloss"] * len(dictOutput[k][0])
for idx, val in enumerate(listb):
listb[idx] = val.replace("XXX", str(idx + 1))
for idx, val in enumerate(listy):
listy[idx] = val.replace("XXX", str(idx + 1))
IntensitiesTypes = ';'.join(map(str, (dictOutput[k][0]) + dictOutput[k][1]))
aux = "\t".join(k.split("-")) + "\t" + IntensitiesTypes + "\t" + ';'.join(listb + listy)
file.write(aux)
file.write('\n')
def main(argv):
inputfile = ''
outputfile = ''
modelpath = ''
sequenceCol = ''
try:
opts, args = getopt.getopt(argv, "hi:o:d:s:", ["ifile=", "ofile=","dfile","sequence_col"])
except getopt.GetoptError:
print('wiNNerprediction.exe -i <inputfile> -o <outputfile> -d <modelpath> -s <sequenceCol>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print("wiNNerprediction.exe -i <inputfile> -o <outputfile> -d <modelpath> -s <sequenceCol>")
sys.exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-o", "--ofile"):
outputfile = arg
elif opt in ("-d", "--dfile"):
modelpath = arg
elif opt in ("-s","--sequence_col"):
sequenceCol = arg
print('Input file is ', inputfile)
print('Output file is ', outputfile)
print('model path is ', modelpath)
print('sequence column is ', sequenceCol)
with open(outputfile, "w") as file:
columns = "Sequence\tFragmentation\tCharge\tFragmentIntensities\tFragmentIons"
file.write(columns)
file.write('\n')
print("\t===================================================================")
print("\t===== wiNNer Peptide Intensities Prediction - Window 24 =========")
print("\t===================================================================")
print("\n\n>>Predictions starting... It might takes several minutes, please wait")
read = pd.read_csv(inputfile, delimiter='\t')
dictSeq = {}
for index, row in read.iterrows():
if sequenceCol == "ModifiedSequence":
k = str(row["ModifiedSequence"])
elif sequenceCol == "Sequence":
k = str(row["Sequence"])
if k not in dictSeq.keys():
dictSeq[k] = [[], []]
dictSeq[k][0].append(row["Fragmentation"])
dictSeq[k][1].append(row["Charge"])
else:
dictSeq[k][0].append(row["Fragmentation"])
dictSeq[k][1].append(row["Charge"])
print("Loading input...done.")
for k in dictSeq:
Fragmentation = "".join(str(x) for x in dictSeq[k][0])
Charge = "".join(str(x) for x in dictSeq[k][1])
ListofSequences = [k]
print("Starting Prediction->", Fragmentation, Charge)
Predict(modelpath, ListofSequences, Fragmentation, Charge, outputfile)
print("Done:->", Fragmentation, Charge)
if __name__ == "__main__":
main(sys.argv[1:])