def global_align(seq1, seq2, Parameters=Parameters()):
M = create_matrix(len(seq1), len(seq2))
Ix = create_matrix(len(seq1), len(seq2))
Iy = create_matrix(len(seq1), len(seq2))
#initializes matrices
for i in range(0, len(seq1) + 1):
for j in range(0, len(seq2) + 1):
if i == 0 and j == 0:
M[i][j] = 0
elif i == 0:
Iy[i][j] = 0
Ix[i][j] = -INF
M[i][j] = -INF
elif j == 0:
Ix[i][j] = 0
Iy[i][j] = -INF
M[i][j] = -INF
#Affine Score
for i in range(1, len(seq1) + 1):
for j in range(1, len(seq2) + 1):
score = Parameters.score(seq1[i - 1], seq2[j - 1])
diag = M[i - 1][j - 1] + score
esq = Ix[i - 1][j - 1] + score
cim = Iy[i - 1][j - 1] + score
M[i][j] = max(esq, diag, cim)
Ix[i][j] = max(M[i - 1][j] + Parameters.gapopen + Parameters.gap,
Ix[i - 1][j] + Parameters.gap)
Iy[i][j] = max(M[i][j - 1] + Parameters.gapopen + Parameters.gap,
Iy[i][j - 1] + Parameters.gap)
if (DEBUG):
print("\nIx")
print_matrix("_" + seq1, "_" + seq2, Ix)
print("\nIy")
print_matrix("_" + seq1, "_" + seq2, Iy)
print("\nM")
print_matrix("_" + seq1, "_" + seq2, M)
return M, Ix, Iy
def local_align(seq1, seq2, Parameters=Parameters()):
M = create_matrix(len(seq1), len(seq2))
score = 0
li = 0
lj = 0
# fill in A in the right order
for i in range(1, len(seq1) + 1):
for j in range(1, len(seq2) + 1):
# the local alignment recurrance rule:
M[i][j] = max(
M[i][j - 1] + Parameters.gap, M[i - 1][j] + Parameters.gap,
M[i - 1][j - 1] + Parameters.score(seq1[i - 1], seq2[j - 1]),
0)
# track the cell with the largest score
if M[i][j] >= score:
score = M[i][j]
li = i
lj = j
return M, li, lj
def traceback_left(M, Ix, Iy, seq1, seq2, Parameters):
# As duas sequencias
alignedseq1 = ""
alignedseq2 = ""
# Os ultimos indices
i = len(seq1)
j = len(seq2)
#Matriz
if (M[i][j] >= Ix[i][j] and M[i][j] >= Iy[i][j]):
matrix = "M"
elif (Ix[i][j] > Iy[i][j]):
matrix = "Ix"
else:
matrix = "Iy"
while ((i is not 0) or (j is not 0)):
if matrix == "M":
alignedseq2 = alignedseq2 + seq2[j - 1]
alignedseq1 = alignedseq1 + seq1[i - 1]
score = Parameters.score(seq1[i - 1], seq2[j - 1])
diag = M[i - 1][j - 1] + score
esq = Ix[i - 1][j - 1] + score
cim = Iy[i - 1][j - 1] + score
if M[i][j] == esq:
matrix = "Ix"
elif M[i][j] == diag:
matrix = "M"
elif M[i][j] == cim:
matrix = "Iy"
i = i - 1
j = j - 1
elif matrix == "Ix":
alignedseq2 = alignedseq2 + "-"
alignedseq1 = alignedseq1 + seq1[i - 1]
if Ix[i][j] == M[i - 1][j] + Parameters.gapopen + Parameters.gap:
matrix = "M"
elif Ix[i][j] == Ix[i - 1][j] + Parameters.gap:
matrix = "Ix"
i = i - 1
elif matrix == "Iy":
alignedseq1 = alignedseq1 + '-'
alignedseq2 = alignedseq2 + seq2[j - 1]
if Iy[i][j] == M[i][j - 1] + Parameters.gapopen + Parameters.gap:
matrix = "M"
elif Iy[i][j] == Iy[i][j - 1] + Parameters.gap:
matrix = "Iy"
j = j - 1
#Revertendo a String
alignedseq1 = alignedseq1[::-1]
alignedseq2 = alignedseq2[::-1]
return alignedseq1, alignedseq2
elif Iy[i][j] == M[i][j - 1] + Parameters.gapopen + Parameters.gap:
matrix = "M"
j = j - 1
#Revertendo a String
alignedseq1 = alignedseq1[::-1]
alignedseq2 = alignedseq2[::-1]
return alignedseq1, alignedseq2
if __name__ == '__main__':
#Definicoes dos parametros
par = Parameters(gapopen=-10, gap=-0.5, matrix='BLOSUM62', stype='protein')
#Sequencias
seq1 = io.read_fasta(io.read_file("../inputs/default1.fasta"))
seq2 = io.read_fasta(io.read_file("../inputs/default2.fasta"))
#Matriz de apontadores
M, Ix, Iy = global_align(seq1, seq2, par)
#Sequencias alinhadas
leftalignedseq1, leftalignedseq2 = traceback_left(M, Ix, Iy, seq1, seq2,
par)
upalignedseq1, upalignedseq2 = traceback_up(M, Ix, Iy, seq1, seq2, par)
result = Alignment(leftalignedseq1, leftalignedseq2, "LEFT")
result.calculate_mat_mis_gaps()
def traceback_left(M, Ix, Iy, seq1, seq2, Parameters, maxmatrix, li, lj):
# As duas sequencias
alignedseq1 = ""
alignedseq2 = ""
# Os indices do max score
i = li
j = lj
#Matriz
matrix = maxmatrix
while True:
print(matrix, i, j)
if matrix == "M":
alignedseq2 = alignedseq2 + seq2[j - 1]
alignedseq1 = alignedseq1 + seq1[i - 1]
if (M[i][j] == 0):
break
score = Parameters.score(seq1[i - 1], seq2[j - 1])
diag = M[i - 1][j - 1] + score
esq = Ix[i - 1][j - 1] + score
cim = Iy[i - 1][j - 1] + score
if M[i][j] == esq:
matrix = "Ix"
elif M[i][j] == diag:
matrix = "M"
elif M[i][j] == cim:
matrix = "Iy"
i = i - 1
j = j - 1
elif matrix == "Ix":
alignedseq2 = alignedseq2 + "-"
alignedseq1 = alignedseq1 + seq1[i - 1]
if (Ix[i][j] == 0):
break
if Ix[i][j] == Ix[i - 1][j] + Parameters.gap:
matrix = "Ix"
elif Ix[i][j] == M[i - 1][j] + Parameters.gapopen + Parameters.gap:
matrix = "M"
i = i - 1
elif matrix == "Iy":
alignedseq1 = alignedseq1 + '-'
alignedseq2 = alignedseq2 + seq2[j - 1]
if (Iy[i][j] == 0):
break
if Iy[i][j] == M[i][j - 1] + Parameters.gapopen + Parameters.gap:
matrix = "M"
elif Iy[i][j] == Iy[i][j - 1] + Parameters.gap:
matrix = "Iy"
j = j - 1
#Revertendo a String
alignedseq1 = alignedseq1[::-1]
alignedseq2 = alignedseq2[::-1]
return alignedseq1, alignedseq2
def local_align(seq1, seq2, Parameters=Parameters()):
M = create_matrix(len(seq1), len(seq2))
Ix = create_matrix(len(seq1), len(seq2))
Iy = create_matrix(len(seq1), len(seq2))
#initializes matrices
for i in range(0, len(seq1) + 1):
for j in range(0, len(seq2) + 1):
if i == 0 and j == 0:
Iy[i][j] = Parameters.gapopen + (Parameters.gap * j)
Ix[i][j] = Parameters.gapopen + (Parameters.gap * i)
M[i][j] = 0
elif i == 0:
Iy[i][j] = Parameters.gapopen + (Parameters.gap * j)
Ix[i][j] = -INF
M[i][j] = -INF
elif j == 0:
Ix[i][j] = Parameters.gapopen + (Parameters.gap * i)
Iy[i][j] = -INF
M[i][j] = -INF
#Max score
maxmatrix = "M"
max_score = 0
li = 0
lj = 0
#Affine Score
for i in range(1, len(seq1) + 1):
for j in range(1, len(seq2) + 1):
score = Parameters.score(seq1[i - 1], seq2[j - 1])
diag = M[i - 1][j - 1] + score
esq = Ix[i - 1][j - 1] + score
cim = Iy[i - 1][j - 1] + score
M[i][j] = max(esq, diag, cim, 0)
if M[i][j] >= max_score:
max_score = M[i][j]
li = i
lj = j
maxmatrix = "M"
Ix[i][j] = max(M[i - 1][j] + Parameters.gapopen + Parameters.gap,
Ix[i - 1][j] + Parameters.gap)
if Ix[i][j] >= max_score:
max_score = Ix[i][j]
li = i
lj = j
maxmatrix = "Ix"
Iy[i][j] = max(M[i][j - 1] + Parameters.gapopen + Parameters.gap,
Iy[i][j - 1] + Parameters.gap)
if Iy[i][j] >= max_score:
max_score = Iy[i][j]
li = i
lj = j
maxmatrix = "Iy"
if (DEBUG):
print("\nIx")
print_matrix("_" + seq1, "_" + seq2, Ix)
print("\nIy")
print_matrix("_" + seq1, "_" + seq2, Iy)
print("\nM")
print_matrix("_" + seq1, "_" + seq2, M)
return M, Ix, Iy, maxmatrix, li, lj
def traceback(M, seq1, seq2, i, j, Parameters):
"""
Esta funcao encontra o melhor aliamente seguindo o algoritmo a seguir:
0- verificar se a celula corrente esta na borda
1- obter os n vizinhos
2- obter o valor da celula corrente
3- Usar a formula
a) M[i][j-1] + Parameters.gap == atual ?
b) M[i-1][j-1] + Parameters.score(seq1[i-1], seq2[j-1]) == atual ?
c) M[i-1][j] + Parameters.gap == atual ?
4 - Criar strings alignedseq1 e alignedseq2
5 - caso a) (esquerda)
alignedseq2 + "_"
alignedseq1 + seq1[j-1] #"_"+seq1
j = j-1
caso b) (diagonal)
alignedseq1 + seq1[j-1]
alignedseq2 + seq2[i-1]
j = j-1
i = i-1
caso c) (cima)
alignedseq1 + "_"
alignedseq2 + seq2[i-1]
i = i-1
6 - reverter a string
OBS: J É LINHA e I É COLUNA
"""
# As duas sequencias
alignedseq1 = ""
alignedseq2 = ""
while M[i][j] != 0:
#Primeira linha ou esquerda
if j == 0 or (M[i][j] == M[i - 1][j] + Parameters.gap):
#if M[i-1][j] is not 0:
alignedseq2 = alignedseq2 + "-"
alignedseq1 = alignedseq1 + seq1[i - 1]
i = i - 1
continue
#Primeira coluna
if i == 0:
#if M[i][j-1] is not 0:
alignedseq1 = alignedseq1 + "-"
alignedseq2 = alignedseq2 + seq2[j - 1]
j = j - 1
continue
#Diagonal
if M[i][j] == M[i - 1][j - 1] + Parameters.score(
seq1[i - 1], seq2[j - 1]):
#if M[i-1][j-1] is not 0:
alignedseq2 = alignedseq2 + seq2[j - 1]
alignedseq1 = alignedseq1 + seq1[i - 1]
j = j - 1
i = i - 1
continue
#Cima
if M[i][j] == M[i][j - 1] + Parameters.gap:
#if M[i][j-1] is not 0:
alignedseq1 = alignedseq1 + '-'
alignedseq2 = alignedseq2 + seq2[j - 1]
j = j - 1
continue
#Revertendo a String
alignedseq1 = alignedseq1[::-1]
alignedseq2 = alignedseq2[::-1]
return alignedseq1, alignedseq2
#Cima
if M[i][j] == M[i][j - 1] + Parameters.gap:
#if M[i][j-1] is not 0:
alignedseq1 = alignedseq1 + '-'
alignedseq2 = alignedseq2 + seq2[j - 1]
j = j - 1
continue
#Revertendo a String
alignedseq1 = alignedseq1[::-1]
alignedseq2 = alignedseq2[::-1]
return alignedseq1, alignedseq2
if __name__ == '__main__':
#Definicoes dos parametros
par = Parameters(gap=-10, matrix='BLOSUM62', stype='dna')
seq1 = io.read_fasta(io.read_file("../inputs/default1.fasta"))
seq2 = io.read_fasta(io.read_file("../inputs/default2.fasta"))
matrix, i, j = local_align(seq1, seq2, par)
alignedseq1, alignedseq2 = traceback(matrix, seq1, seq2, i, j, par)
result = Alignment(alignedseq1, alignedseq2, "DEFAULT")
result.calculate_mat_mis_gaps()
io.write_file("../outputs/locally_local_linear_output.txt", str(result))