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semiglobal_alignment.py
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import numpy as np
import copy
PAM250 = {
'A': {'A': 2, 'C': -2, 'D': 0, 'E': 0, 'F': -3, 'G': 1, 'H': -1, 'I': -1, 'K': -1, 'L': -2, 'M': -1, 'N': 0, 'P': 1, 'Q': 0, 'R': -2, 'S': 1, 'T': 1, 'V': 0, 'W': -6, 'Y': -3},
'C': {'A': -2, 'C': 12, 'D': -5, 'E':-5, 'F': -4, 'G': -3, 'H': -3, 'I': -2, 'K': -5, 'L': -6, 'M': -5, 'N': -4, 'P': -3, 'Q': -5, 'R': -4, 'S': 0, 'T': -2, 'V': -2, 'W': -8, 'Y': 0},
'D': {'A': 0, 'C': -5, 'D': 4, 'E': 3, 'F': -6, 'G': 1, 'H': 1, 'I': -2, 'K': 0, 'L': -4, 'M': -3, 'N': 2, 'P': -1, 'Q': 2, 'R': -1, 'S': 0, 'T': 0, 'V': -2, 'W': -7, 'Y': -4},
'E': {'A': 0, 'C': -5, 'D': 3, 'E': 4, 'F': -5, 'G': 0, 'H': 1, 'I': -2, 'K': 0, 'L': -3, 'M': -2, 'N': 1, 'P': -1, 'Q': 2, 'R': -1, 'S': 0, 'T': 0, 'V': -2, 'W': -7, 'Y': -4},
'F': {'A': -3, 'C': -4, 'D': -6, 'E':-5, 'F': 9, 'G': -5, 'H': -2, 'I': 1, 'K': -5, 'L': 2, 'M': 0, 'N': -3, 'P': -5, 'Q': -5, 'R': -4, 'S': -3, 'T': -3, 'V': -1, 'W': 0, 'Y': 7},
'G': {'A': 1, 'C': -3, 'D': 1, 'E': 0, 'F': -5, 'G': 5, 'H': -2, 'I': -3, 'K': -2, 'L': -4, 'M': -3, 'N': 0, 'P': 0, 'Q': -1, 'R': -3, 'S': 1, 'T': 0, 'V': -1, 'W': -7, 'Y': -5},
'H': {'A': -1, 'C': -3, 'D': 1, 'E': 1, 'F': -2, 'G': -2, 'H': 6, 'I': -2, 'K': 0, 'L': -2, 'M': -2, 'N': 2, 'P': 0, 'Q': 3, 'R': 2, 'S': -1, 'T': -1, 'V': -2, 'W': -3, 'Y': 0},
'I': {'A': -1, 'C': -2, 'D': -2, 'E':-2, 'F': 1, 'G': -3, 'H': -2, 'I': 5, 'K': -2, 'L': 2, 'M': 2, 'N': -2, 'P': -2, 'Q': -2, 'R': -2, 'S': -1, 'T': 0, 'V': 4, 'W': -5, 'Y': -1},
'K': {'A': -1, 'C': -5, 'D': 0, 'E': 0, 'F': -5, 'G': -2, 'H': 0, 'I': -2, 'K': 5, 'L': -3, 'M': 0, 'N': 1, 'P': -1, 'Q': 1, 'R': 3, 'S': 0, 'T': 0, 'V': -2, 'W': -3, 'Y': -4},
'L': {'A': -2, 'C': -6, 'D': -4, 'E':-3, 'F': 2, 'G': -4, 'H': -2, 'I': 2, 'K': -3, 'L': 6, 'M': 4, 'N': -3, 'P': -3, 'Q': -2, 'R': -3, 'S': -3, 'T': -2, 'V': 2, 'W': -2, 'Y': -1},
'M': {'A': -1, 'C': -5, 'D': -3, 'E':-2, 'F': 0, 'G': -3, 'H': -2, 'I': 2, 'K': 0, 'L': 4, 'M': 6, 'N': -2, 'P': -2, 'Q': -1, 'R': 0, 'S': -2, 'T': -1, 'V': 2, 'W': -4, 'Y': -2},
'N': {'A': 0, 'C': -4, 'D': 2, 'E': 1, 'F': -3, 'G': 0, 'H': 2, 'I': -2, 'K': 1, 'L': -3, 'M': -2, 'N': 2, 'P': 0, 'Q': 1, 'R': 0, 'S': 1, 'T': 0, 'V': -2, 'W': -4, 'Y': -2},
'P': {'A': 1, 'C': -3, 'D': -1, 'E':-1, 'F': -5, 'G': 0, 'H': 0, 'I': -2, 'K': -1, 'L': -3, 'M': -2, 'N': 0, 'P': 6, 'Q': 0, 'R': 0, 'S': 1, 'T': 0, 'V': -1, 'W': -6, 'Y': -5},
'Q': {'A': 0, 'C': -5, 'D': 2, 'E': 2, 'F': -5, 'G': -1, 'H': 3, 'I': -2, 'K': 1, 'L': -2, 'M': -1, 'N': 1, 'P': 0, 'Q': 4, 'R': 1, 'S': -1, 'T': -1, 'V': -2, 'W': -5, 'Y': -4},
'R': {'A': -2, 'C': -4, 'D': -1, 'E':-1, 'F': -4, 'G': -3, 'H': 2, 'I': -2, 'K': 3, 'L': -3, 'M': 0, 'N': 0, 'P': 0, 'Q': 1, 'R': 6, 'S': 0, 'T': -1, 'V': -2, 'W': 2, 'Y': -4},
'S': {'A': 1, 'C': 0, 'D': 0, 'E': 0, 'F': -3, 'G': 1, 'H': -1, 'I': -1, 'K': 0, 'L': -3, 'M': -2, 'N': 1, 'P': 1, 'Q': -1, 'R': 0, 'S': 2, 'T': 1, 'V': -1, 'W': -2, 'Y': -3},
'T': {'A': 1, 'C': -2, 'D': 0, 'E': 0, 'F': -3, 'G': 0, 'H': -1, 'I': 0, 'K': 0, 'L': -2, 'M': -1, 'N': 0, 'P': 0, 'Q': -1, 'R': -1, 'S': 1, 'T': 3, 'V': 0, 'W': -5, 'Y': -3},
'V': {'A': 0, 'C': -2, 'D': -2, 'E':-2, 'F': -1, 'G': -1, 'H': -2, 'I': 4, 'K': -2, 'L': 2, 'M': 2, 'N': -2, 'P': -1, 'Q': -2, 'R': -2, 'S': -1, 'T': 0, 'V': 4, 'W': -6, 'Y': -2},
'W': {'A': -6, 'C': -8, 'D': -7, 'E':-7, 'F': 0, 'G': -7, 'H': -3, 'I': -5, 'K': -3, 'L': -2, 'M': -4, 'N': -4, 'P': -6, 'Q': -5, 'R': 2, 'S': -2, 'T': -5, 'V': -6, 'W': 17, 'Y': 0},
'Y': {'A': -3, 'C': 0, 'D': -4, 'E':-4, 'F': 7, 'G': -5, 'H': 0, 'I': -1, 'K': -4, 'L': -1, 'M': -2, 'N': -2, 'P': -5, 'Q': -4, 'R': -4, 'S': -3, 'T': -3, 'V': -2, 'W': 0, 'Y': 10}
}
def count_alphabets(s):
"""
Counts the number of alphabets in a string.
"""
count = 0
for i in s:
if i.isalpha():
count += 1
return count
def create_matrix(x, y, gap_penalty):
"""
Creates a n x n matrix of zeros.
"""
matrix = np.zeros((x+1, y+1))
return matrix
def traceback_matrix(x, y):
"""
Creates a traceback matrix.
"""
T_matrix = {}
for i in range(x+1):
for j in range(y+1):
t = (i, j)
if i == 0 and j == 0:
T_matrix[t] = ['Done']
elif i == 0 and j != 0:
T_matrix[t] = ['Left']
elif i != 0 and j == 0:
T_matrix[t] = ['Up']
else:
T_matrix[t] = []
return T_matrix
def get_max_index(m, x, y):
"""
Returns the index of the maximum value in a matrix.
"""
max_col = matrix[:, -1].max()
max_row = matrix[-1, :].max()
max_value = max(max_col, max_row)
print(int(max_value))
# max_index = np.where(m == max_value)
maxe_index = []
for i in range(x):
for j in range(y):
if m[i][j] == max_value:
if j == y-1 or i == x-1:
maxe_index.append((i, j))
#print(i, j)
return maxe_index
def fill_matrix(s1, s2, matrix, T_matrix, gap_penalty):
"""
Fills the matrix with the values of the scoring matrix.
"""
x = len(s1)
y = len(s2)
for i in range(1, x+1):
for j in range(1, y+1):
t = (i, j)
horizantal = matrix[i][j-1] + gap_penalty
vertical = matrix[i-1][j] + gap_penalty
diagonal = matrix[i-1][j-1] + PAM250[s1[i-1]][s2[j-1]]
matrix[i][j] = max(horizantal, vertical, diagonal)
#print(matrix[i][j])
if matrix[i][j] == horizantal:
T_matrix[t].append('Left')
if matrix[i][j] == vertical:
T_matrix[t].append('Up')
if matrix[i][j] == diagonal:
T_matrix[t].append('Diag') # diagonal
return matrix, T_matrix
def traceback(s1, s2, T_matrix):
"""
Tracesback the matrix to find the optimal alignment.
"""
x = len(s1)
y = len(s2)
i = x
j = y
alignment1 = ''
alignment2 = ''
while i > 0 or j > 0:
print(i, j)
if (i, j) in T_matrix:
temp = copy.deepcopy(T_matrix[(i, j)])
direction = temp.pop()
print(direction)
if direction == 'Diag':
alignment1 += s1[i-1]
alignment2 += s2[j-1]
i -= 1
j -= 1
elif direction == 'Left':
alignment1 += s1[i-1]
alignment2 += '-'
j -= 1
elif direction == 'Up':
alignment1 += '-'
alignment2 += s2[j-1]
i -= 1
else:
break
else:
break
return alignment1[::-1], alignment2[::-1]
Seq = []
def recursive_traceback(ix, jx, s1, s2, T_matrix, alignment1, alignment2):
"""
Recursive traceback.
"""
global Seq
if (ix, jx) in T_matrix:
temp = copy.deepcopy(T_matrix[(ix, jx)])
if ix == 0 and jx == 0:
Seq.append((alignment1[::-1], alignment2[::-1]))
while len(temp) > 0:
direction = temp.pop()
at = copy.deepcopy(alignment1)
bt = copy.deepcopy(alignment2)
if direction == 'Diag':
recursive_traceback(ix-1, jx-1, s1, s2, T_matrix, alignment1 + s1[ix-1], alignment2+s2[jx-1])
alignment1 = at
alignment2 = bt
elif direction == 'Left':
recursive_traceback(ix, jx-1, s1, s2, T_matrix, alignment1+'-', alignment2+ s2[jx-1])
alignment1 = at
alignment2 = bt
elif direction == 'Up':
recursive_traceback(ix-1, jx, s1, s2, T_matrix, alignment1 + s1[ix-1], alignment2+'-' )
alignment1 = at
alignment2 = bt
def get_all_alignments(max_index, s1, s2, matrix, T_matrix, gap_penalty):
"""
Returns all the alignments.
"""
for i in max_index:
ix = i[0]
jx = i[1]
recursive_traceback(ix, jx, s1, s2, T_matrix, '', '')
seq = []
for s in Seq:
for i in max_index:
i1 = len(s1)
j1 = len(s2)
m = max(i1, j1)
#print(m)
alignment1 = s[0]
alignment2 = s[1]
a2 = ""
a1 = ""
#print(i)
if i1-i[0]:
while i1 > i[0]:
a2 += '-'
a1 += s1[i1-1]
i1 -= 1
if j1-i[1]:
while j1 > i[1]:
a1 += '-'
a2 += s2[j1-1]
j1 -= 1
alignment1 += a1[::-1]
alignment2 += a2[::-1]
if count_alphabets(alignment1) == len(s1) and count_alphabets(alignment2) == len(s2):
seq.append((alignment2, alignment1))
return seq
if __name__ == "__main__":
y = input()
x = input()
gap_penalty = -9
matrix = create_matrix(len(x), len(y), gap_penalty)
T_matrix = traceback_matrix(len(x), len(y))
a, b = fill_matrix(x, y, matrix, T_matrix, gap_penalty)
g = get_max_index(a, len(x)+1, len(y)+1)
g.reverse()
ix = g[0][0]
jx = g[0][1]
seq = get_all_alignments(g, x, y, a, b, gap_penalty)
sortedSeq = [i[0]+i[1] for i in seq]
sortedSeq.sort()
for i in sortedSeq:
print(i[0:int(len(i)/2)])
print(i[int(len(i)/2):])