-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathalignement_deterministe.py
157 lines (142 loc) · 6.11 KB
/
alignement_deterministe.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
# -*-coding:utf-8-*-
"""
Les algorithmes implémentés apparaissent dans un article de Linux magazine
"""
import enum
import numpy as np
__author__ = "besnier"
alphabet_adn = list('acgt')
alphabet_humain = list('azertyuiopqsfghjklmwxcvbn')
class Etats(enum.IntEnum):
CORRESPONDANCE = 0
INSERTION = 1
SUPPRESSION = 2
def aligner_needleman_wunsch(chi, psi, matrice_remplacement, penalite_is, alphabet):
# initialisation
n, m = len(chi), len(psi)
mat_poids = np.zeros((n+1, m+1))
mat_origine = np.zeros((n+1, m+1))
mat_poids[:, 0] = np.array([-i*penalite_is for i in range(n+1)])
mat_origine[1:, 0] = 1
mat_poids[0, :] = np.array([-j*penalite_is for j in range(m+1)])
mat_origine[0, 1:] = 2
# étape prograde
for i in range(1, n+1):
for j in range(1, m+1):
propositions = np.array([mat_poids[i-1, j-1] +
matrice_remplacement[alphabet.index(chi[i-1]), alphabet.index(psi[j-1])],
mat_poids[i-1, j] - penalite_is,
mat_poids[i, j-1] - penalite_is])
mat_poids[i, j] = np.max(propositions)
mat_origine[i, j] = np.argmax(propositions)
# étape rétrograde
chi_alignement = []
psi_alignement = []
alignement = []
i, j = n, m
while i > 0 or j > 0:
ori = mat_origine[i, j]
# print(i, j, ori)
if ori == Etats.CORRESPONDANCE and i > 0 and j > 0:
i, j = i-1, j-1
chi_alignement.append(chi[i])
psi_alignement.append(psi[j])
alignement.append("C")
elif ori == Etats.INSERTION and i > 0:
i -= 1
chi_alignement.append(chi[i])
psi_alignement.append("-")
alignement.append("I")
elif ori == Etats.SUPPRESSION and j > 0:
j -= 1
alignement.append("S")
chi_alignement.append("-")
psi_alignement.append(psi[j])
chi_alignement.reverse()
psi_alignement.reverse()
alignement.reverse()
# print(mat_poids)
# print(mat_origine)
return chi_alignement, psi_alignement, alignement
def aligner_smith_waterman(chi, psi, matrice_remplacement, penalite_is, alphabet):
# initialisation
n, m = len(chi), len(psi)
mat_poids = np.zeros((n+1, m+1))
mat_origine = np.zeros((n+1, m+1))
mat_poids[:, 0] = np.array([-i*penalite_is for i in range(n+1)])
mat_origine[1:, 0] = 1
mat_poids[0, :] = np.array([-j*penalite_is for j in range(m+1)])
mat_origine[0, 1:] = 2
# étape prograde
for i in range(1, n+1):
for j in range(1, m+1):
propositions = np.array([mat_poids[i-1, j-1] +
matrice_remplacement[alphabet.index(chi[i-1]), alphabet.index(psi[j-1])],
mat_poids[i-1, j] - penalite_is,
mat_poids[i, j-1] - penalite_is,
0])
mat_poids[i, j] = np.max(propositions)
mat_origine[i, j] = np.argmax(propositions)
# étape rétrograde
chi_alignement = []
psi_alignement = []
alignement = []
# print(mat_poids)
a = np.unravel_index(mat_poids.argmax(), mat_poids.shape)
# print("a", a)
i, j = a
while mat_poids[i, j] > 0:
ori = mat_origine[i, j]
if ori == Etats.CORRESPONDANCE and i > 0 and j > 0:
i, j = i-1, j-1
chi_alignement.append(chi[i])
psi_alignement.append(psi[j])
alignement.append("C")
elif ori == Etats.INSERTION and i > 0:
i, j = i-1, j
chi_alignement.append(chi[i])
psi_alignement.append("-")
alignement.append("I")
elif ori == Etats.SUPPRESSION and j > 0:
i, j = i, j-1
alignement.append("S")
chi_alignement.append("-")
psi_alignement.append(psi[j])
chi_alignement.reverse()
psi_alignement.reverse()
alignement.reverse()
return chi_alignement, psi_alignement, alignement
def distance_levenshtein(chi, psi, matrice_remplacement, penalite_is, alphabet):
# initialisation
n, m = len(chi), len(psi)
mat_poids = np.zeros((n+1, m+1))
mat_poids[:, 0] = np.array([i for i in range(n+1)])
mat_poids[0, :] = np.array([j for j in range(m+1)])
# étape prograde
for i in range(1, n+1):
for j in range(1, m+1):
propositions = np.array([mat_poids[i-1, j-1] +
matrice_remplacement[alphabet.index(chi[i-1]), alphabet.index(psi[j-1])],
mat_poids[i-1, j] + penalite_is,
mat_poids[i, j-1] + penalite_is])
mat_poids[i, j] = np.min(propositions)
# print(mat_poids)
return mat_poids[n, m]
if __name__ == "__main__":
sequence_1, sequence_2 = "aagtagccactag", "ggaagtaagct"
matrice_log_odds = np.array([[5, -1, -1, -1], [-1, 5, -1, -1], [-1, -1, 5, -1], [-1, -1, -1, 5]])
d = 0.5
ali1, ali2, ali = aligner_needleman_wunsch(sequence_1, sequence_2, matrice_log_odds, d, alphabet_adn)
print("", "".join(ali1), "\n", "".join(ali2), "\n", "".join(ali))
# aligner_smith_waterman(sequence_1, sequence_2, matrice_log_odds, d)
sequence_3 = "aaaaaaaaaatgtcattaaaaaaaa"
sequence_4 = "ttttgtactggggggggggg"
ali1, ali2, ali = aligner_smith_waterman(sequence_3, sequence_4, matrice_log_odds, d, alphabet_adn)
print("", "".join(ali1), "\n", "".join(ali2), "\n", "".join(ali))
matrice_levenshtein = np.ones((len(alphabet_humain), len(alphabet_humain))) + -1*np.eye(len(alphabet_humain))
print(distance_levenshtein(sequence_1, sequence_2, matrice_levenshtein, 1, alphabet_humain))
print(distance_levenshtein(sequence_3, sequence_4, matrice_levenshtein, 1, alphabet_humain))
print(distance_levenshtein("niche", "chiens", matrice_levenshtein, 1, alphabet_humain))
mat_humain = -5*np.ones((len(alphabet_humain), len(alphabet_humain))) + \
6*np.eye(len(alphabet_humain))
print(aligner_needleman_wunsch("chiens", "niche", mat_humain, d, alphabet_humain))