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w_4.py
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w_4.py
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import networkx as nx
import sys
import copy
import numpy as np
from collections import Counter
import time
AA = np.array(['G','A','S','P','V','T','C','I','L','N','D','Q','K','E','M','H','F','R','Y','W'])
AW = np.array([ 57,71,87,97,99,101,103,113,113,114,115,128,128,129,131,137,147,156,163,186])
aa = np.array(['X', 'Z'])
aw = np.array([4, 5])
def graph_spectrum_input(file):
masses = [0]
read = open(file)
for line in read:
l = line.split(' ')
l = list(map(int, l))
masses += l
return masses
def graph_spectrum(masses):
graph = []
d = {}
n = len(masses)
for i in range(0, n):
mass = masses[i]
for j in range(i+1, n):
find = masses[j] - mass
if find in AW:
index = np.where(AW==find)
acid = AA[index]
graph.append(str(mass) + '->' + str(masses[j]) + ':' + str(acid[0]))
if mass not in d:
d[mass] = []
d[mass].append(masses[j])
return graph, d
def ideal_spectrum(peptide):
peptide = list(peptide)
prefix_mass, suffix_mass = 0, 0
spectrum = [0]
for i in range(len(peptide)):
prefix = peptide[i]
suffix = peptide[-i - 1]
prefix_mass += AW[np.where(AA == prefix)][0]
suffix_mass += AW[np.where(AA == suffix)][0]
spectrum.append(prefix_mass)
spectrum.append(suffix_mass)
spectrum = list(set(spectrum))
spectrum.sort()
return spectrum
def all_paths(start, end, d, visited, nodes, path, paths):
#print(start, end)
visited[np.where(nodes == start)] = True
path.append(start)
if start == end:
#print(path)
paths.append(copy.deepcopy(path))
path.pop()
visited[np.where(nodes == start)] = False
return
else:
if start in d:
for i in d[start]:
if visited[np.where(nodes == i)] == False:
all_paths(i, end, d, visited, nodes, path, paths)
path.pop()
visited[np.where(nodes == start)] = False
def decoding_ideal_spectrum(spectrum):
graph, d = graph_spectrum(spectrum)
nodes = []
for key in d:
nodes.append(key)
for item in d[key]:
nodes.append(item)
nodes = np.array(list(set(nodes)))
visited = np.array([False for i in range(len(nodes))])
start = np.min(nodes)
end = np.max(nodes)
path = []
paths = []
all_paths(start, end, d, visited, nodes, path, paths)
for path in paths:
peptide = ''
x = len(path)
for i in range(1, x):
mass = path[i] - path[i-1]
peptide += str(AA[np.where(AW == mass)][0])
#print(ideal_spectrum(peptide), 'ideal')
#print(spectrum, 'find')
if Counter(ideal_spectrum(peptide)) == Counter(spectrum):
return peptide
def peptide_vector(peptide):
peptide = list(peptide)
prefix_mass = 0
spectrum = []
for i in range(len(peptide)):
prefix = peptide[i]
prefix_mass += AW[np.where(AA == prefix)][0]
spectrum.append(prefix_mass)
spectrum = list(set(spectrum))
spectrum.sort()
last = spectrum[len(spectrum) - 1]
vector = [0 for i in range(last)]
for i in spectrum:
vector[i-1] = 1
return vector
def vector_to_peptide(vector):
spectrum = []
for i in range(len(vector)):
if vector[i] == 1:
spectrum.append(i+1)
spectrum = [0] + spectrum
peptide = ''
for i in range(len(spectrum)-1):
mass = spectrum[i+1] - spectrum[i]
peptide += AA[np.where(AW == mass)][0]
return peptide
def spectrum_to_peptide(spectrum):
peptide = ''
for i in range(len(spectrum) - 1):
mass = spectrum[i + 1] - spectrum[i]
peptide += AA[np.where(AW == mass)][0]
return peptide
#score(peptide, spectrum_vector) = dot_product(peptide_vector, spectrum_vector)
def peptide_sequencing(spectral_vector):
#current = time.time()
#last = current + 260
spectral_vector = [0] + spectral_vector
m = len(spectral_vector)
nodes = np.array([i for i in range(m)])
spectral_vector = np.array(spectral_vector)
graph, edge_labels = graph_spectrum(nodes)
start = 0
end = m - 1
G = nx.DiGraph()
for key in edge_labels:
for node in edge_labels[key]:
G.add_edge(key, node, weight = -(spectral_vector[node] - spectral_vector[key]))
#paths = []
max_score = float('-Inf')
current_peptide = ''
#loops = 0
for path in nx.all_shortest_paths(G, source=start, target=end, weight = 'weight', method = 'bellman-ford'):
#want = path
#break
#print(path)
#path_ = path[::-1]
#peptide_of_path = spectrum_to_peptide(path)
#vector_peptide = np.array(peptide_vector(peptide_of_path))
#spec_ = np.array(spectrum)
score = sum([spectral_vector[i] for i in path])
if score < 100:
continue
if score > max_score:
max_score = score
current_peptide = spectrum_to_peptide(path)
print(max_score, current_peptide)
return current_peptide
'''
spectral_vector = '23 4 21 16 25 1 -15 -11 -3 4 -4 -2 4 5 -3 -1 -15 7 21 13 6 -18 7 20 4 -19 12 23 -19 -18 -13 2 5 29 6 22 1 2 -15 21 25 6 5 -12 22 -19 0 24 9 22 -12 0 20 9 25 10 -7 -14 -20 -18 -4 9 12 25 -16 5 19 9 26 29 13 2 8 22 18 -4 7 15 21 -3 -1 7 19 -17 16 -15 -17 20 -4 10 27 -16 -12 14 18 6 -20 -4 5 5 -9 7 -3 14 -5 17 17 11 -6 17 13 -8 -9 -7 -6 -15 23 -15 19 22 17 0 -4 4 26 -8 17 19 1 28 22 30 -7 5 -16 8 -11 -19 23 -17 -13 20 2 1 9 -7 16 18 -7 -13 6 27 -11 29 13 20 22 18 16 -15 27 11 -19 23 12 27 6 -19 -11 2 -13 -3 -6 18 -8 -18 -9 -14 1 -16 12 -18 7 13 8 -4 3 13 4 25 -3 18 -9 -9 30 0 -3 6 -10 24 4 0 4 -6 -5 19 22 -5 28 18 24 -12 -14 1 -7 6 -5 -2 25 -18 22 5 -3 -2 -11 23 12 20 1 -11 -6 25 -4 20 10 27 22 -2 -18 -7 4 -6 9 16 2 -5 12 21 -13 -5 -12 3 -10 -13 14 16 2 19 13 29 -19 -19 28 25 -10 20 -20 -8 -6 -14 10 -7 9 27 17 20 -13 16 -11 14 -8 15 17 17 -19 4 -9 -20 -2 29 23 -10 25 14 29 -13 17 26 13 22 -14 -16 10 -13 0 -17 15 -4 28 -19 2 4 1 9 10 29 -1 27 3 30 15 10 -17 -5 -17 27 10 15 0 23 -6 -5 -18 6 12 27 3 20 -16 -12 5 5 14 17 16 -14 -6 -12 24 0 5 -17 -15 27 24 24 20 -15 28 9 10 -14 6 11 5 -17 3 27 -3 10 -13 1 21 -13 20 10 17 29 1 -7 2 25 7 13 11 -10 -15 -19 -18 -9 7 -11 24 9 -10 -17 -7 9 21 -2 21 17 2 17 -4 -1 -17 19 -4 -17 2 -1 24 -7 21 6 14 18 24 23 2 -7 -20 -18 11 -16 22 -2 1 6 -7 -17 20 17 2 16 -5 13 8 -18 30 -11 -5 28 1 -2 24 15 21 29 -12 10 -17 24 10 17 -9 -13 -10 29 7 -19 -6 -11 -16 22 -20 2 -18 -19 21 22 14 -14 25 -4 -8 13 12 15 11 19 -5 -16 -14 7 18 17 12 10 -13 8 -5 24 26 -17 30 2 -2 24 24 23 5 -7 -3 -4 -16 25 20 26 -3 27 30 18 7 -3 13 1 28 27 0 -2 -17 16 6 22 13 29 29 -10 -10 9 14 -3 12 23 18 6 -7 5 20 4 -5 15 20 22 -15 18 -7 0 30 9 15 23 -18 9 -4 0 -14 22 -10 21 4 -2 0 21 -18 27 -1 -6 -3 0 -15 12 8 7 -20 1 8 7 30 29 19 -7 19 24 14 14 -2 -10 -14 17 25 20 -10 0 21 20 3 -10 0 9 5 13 1 2 28 -4 20 8 -8 2 -18 19 -7 28 -14 16 -3 -11 8 12 26 14 17 -10 -5 -18 4 -11 11 8 23 -8 7 26 6 15 5 -10 21 23 12 26 8 -12 9 -14 4 -7 28 4 -7 -4 10 -16 2 18 26 8 0 -16 -10 26 1 3 7 12 -6 1 15 29 5 29 19 13 -18 18 4 -4 -6 0 16 -1 3 1 11 1 21 -17 20 18 26 5 26 14 9 22 -6 16 -20 -10 13 18 -1 25 3 -9 -1 0 -5 15 17 25 -13 14 -18 -11 21 -14 -7 -12 24 0 11 -16 -5 -3'
spectral_vector = spectral_vector.split(' ')
spectral_vector = list(map(int, spectral_vector))
print(peptide_sequencing(spectral_vector))
'''
'''
vector = '0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1'
vector = vector.split(' ')
vector = list(map(int, vector))
print(vector_to_peptide(vector))
'''
'''
peptide = 'EQAVMRFWGGLQSFPTTADHA'
v = peptide_vector(peptide)
for i in v:
print(i, end = ' ')
print('\n')
'''
'''
file = 'decoding.txt'
spectrum = graph_spectrum_input(file)
print(decoding_ideal_spectrum(spectrum))
'''