def optimal_alignment_gaff_yesimon(s, t, gap_init= -11, gap_ext= -1):
sl, tl = len(s), len(t)
if sl < tl:
return optimal_alignment_gaff_yesimon(t, s, gap_init, gap_ext)
m = {(0, 0): (0, None)}
f = {}
g = {}
h = {}
m.update({((i, 0), (gap_init + gap_ext * (i - 1), (i - 1, 0))) for i in range(1, sl + 1)})
m.update({((0, i), (gap_init + gap_ext * (i - 1), (0, i - 1))) for i in range(1, tl + 1)})
for i, j in product(range(1, sl + 1), range(1, tl + 1)):
cost = blosum62.get((s[i - 1], t[j - 1]))
if cost == None:
cost = blosum62.get((t[j - 1], s[i - 1]))
f[(i, j)] = m[(i - 1, j - 1)][0] + cost
gg = g.get((i - 1, j))
if gg != None:
gg += gap_ext
hh = h.get((i, j - 1))
if hh != None:
hh += gap_ext
g[(i, j)] = max(m[(i - 1, j)][0] + gap_init, gg)
h[(i, j)] = max(m[(i, j - 1)][0] + gap_init, hh)
v = max(f[(i, j)], g[(i, j)], h[(i, j)])
if v == f[(i, j)]: m[(i, j)] = (v, (i - 1, j - 1))
elif v == g[(i, j)]: m[(i, j)] = (v, (i - 1, j))
elif v == h[(i, j)]: m[(i, j)] = (v, (i, j - 1))
retval = m[(i, j)]
print sl, tl
for i in xrange(sl + 1):
for j in xrange(tl + 1):
print m[(i, j)] if (i, j) in m else '-',
print
print m
return retval
def maxAlignscorgMat(s, t):
string_s = len(s)
string_t = len(t)
AlnScore = {(0, 0): (0, None)}
AlnScore.update({((m, 0), (m * -5, (m - 1, 0)))
for m in range(1, string_s + 1)})
AlnScore.update({((0, m), (m * -5, (0, m - 1)))
for m in range(1, string_t + 1)})
for m, n in product(range(1, string_s + 1), range(1, string_t + 1)):
cost = blosum62.get((s[m - 1], t[n - 1]))
if cost == None:
cost = blosum62.get((t[n - 1], s[m - 1]))
a = AlnScore[(m - 1, n - 1)][0] + cost
b = AlnScore[(m - 1, n)][0] - 5
c = AlnScore[(m, n - 1)][0] - 5
max_align = max(a, b, c)
if a == max_align:
AlnScore[(m, n)] = (max_align, (m - 1, n - 1))
elif b == max_align:
AlnScore[(m, n)] = (max_align, (m - 1, n))
elif c == max_align:
AlnScore[(m, n)] = (max_align, (m, n - 1))
return AlnScore[(m, n)][0]
def globAlign(s, t):
protein_s = len(s)
protein_t = len(t)
maximumAlnScore = {(0, 0): (0, None)}
a, b, c = {}, {}, {}
maximumAlnScore.update({((m, 0), (-5, (m - 1, 0)))
for m in range(1, protein_s + 1)})
maximumAlnScore.update({((0, m), (-5, (0, m - 1)))
for m in range(1, protein_t + 1)})
for m, n in product(range(1, protein_s + 1), range(1, protein_t + 1)):
cost = blosum62.get((s[m - 1], t[n - 1]))
if cost == None:
cost = blosum62.get((t[n - 1], s[m - 1]))
a[(m, n)] = maximumAlnScore[(m - 1, n - 1)][0] + cost
b[(m, n)] = max(maximumAlnScore[(m - 1, n)][0] - 5, b.get((m - 1, n)))
c[(m, n)] = max(maximumAlnScore[(m, n - 1)][0] - 5, c.get((m, n - 1)))
score = max(a[(m, n)], b[(m, n)], c[(m, n)])
if score == a[(m, n)]:
maximumAlnScore[(m, n)] = (score, (m - 1, n - 1))
elif score == b[(m, n)]:
maximumAlnScore[(m, n)] = (score, (m - 1, n))
elif score == c[(m, n)]:
maximumAlnScore[(m, n)] = (score, (m, n - 1))
return maximumAlnScore[(m, n)][0]
def calcBLOSUMfeatures(SAV_coords):
feat_dtype = np.dtype([('BLOSUM', 'f')])
features = np.zeros(len(SAV_coords), dtype=feat_dtype)
for i, SAV in enumerate(SAV_coords):
aa1 = SAV['aa_wt']
aa2 = SAV['aa_mut']
features[i] = blosum62.get((aa1, aa2), blosum62.get((aa2, aa1)))
return features
def score(A, B):
scores = [
#get(A,B) dan juga get(B,A), karena berbentuk tabel blosum hanya berbentuk matriks segitiga
blosum62.get((A[i], B[i]), blosum62.get((B[i], A[i])))
for i in range(len(A))
]
return sum(scores)
def get_score(s, t):
sl, tl = len(s), len(t)
m = {(0, 0): (0, None)}
f, g, h = {}, {}, {}
m.update({((i, 0), (-5, (i - 1, 0))) for i in range(1, sl + 1)})
m.update({((0, i), (-5, (0, i - 1))) for i in range(1, tl + 1)})
for i, j in product(range(1, sl + 1), range(1, tl + 1)):
cost = blosum62.get((s[i - 1], t[j - 1]))
if cost == None:
cost = blosum62.get((t[j - 1], s[i - 1]))
f[(i, j)] = m[(i - 1, j - 1)][0] + cost
g[(i, j)] = max(m[(i - 1, j)][0] - 5, g.get((i - 1, j)))
h[(i, j)] = max(m[(i, j - 1)][0] - 5, h.get((i, j - 1)))
v = max(f[(i, j)], g[(i, j)], h[(i, j)])
if v == f[(i, j)]:
m[(i, j)] = (v, (i - 1, j - 1))
elif v == g[(i, j)]:
m[(i, j)] = (v, (i - 1, j))
elif v == h[(i, j)]:
m[(i, j)] = (v, (i, j - 1))
return m[(i, j)][0]
def get_max_alignment(s, t):
sl, tl = len(s), len(t)
m = {(0, 0): (0, None)}
m.update({((i, 0), (i * - 5, (i - 1, 0))) for i in range(1, sl + 1)})
m.update({((0, i), (i * - 5, (0, i - 1))) for i in range(1, tl + 1)})
for i, j in product(range(1, sl + 1), range(1, tl + 1)):
cost = blosum62.get((s[i - 1], t[j - 1]))
if cost == None:
cost = blosum62.get((t[j - 1], s[i - 1]))
d = m[(i - 1, j - 1)][0] + cost
l = m[(i - 1, j)][0] - 5
u = m[(i, j - 1)][0] - 5
b = max(d, l, u)
if d == b:
m[(i, j)] = (b, (i - 1, j - 1))
elif l == b:
m[(i, j)] = (b, (i - 1, j))
elif u == b:
m[(i, j)] = (b, (i, j - 1))
return m[(i, j)][0]
def get_score(s, t):
sl, tl = len(s), len(t)
m = {(0, 0): (0, None)}
f, g, h = {}, {}, {}
m.update({((i, 0), (-5, (i - 1, 0))) for i in range(1, sl + 1)})
m.update({((0, i), (-5, (0, i - 1))) for i in range(1, tl + 1)})
for i, j in product(range(1, sl + 1), range(1, tl + 1)):
cost = blosum62.get((s[i - 1], t[j - 1]))
if cost == None:
cost = blosum62.get((t[j - 1], s[i - 1]))
f[(i, j)] = m[(i - 1, j - 1)][0] + cost
g[(i, j)] = max(m[(i - 1, j)][0] - 5, g.get((i - 1, j)))
h[(i, j)] = max(m[(i, j - 1)][0] - 5, h.get((i, j - 1)))
v = max(f[(i, j)], g[(i, j)], h[(i, j)])
if v == f[(i, j)]:
m[(i, j)] = (v, (i-1, j - 1))
elif v == g[(i, j)]:
m[(i, j)] = (v, (i - 1, j))
elif v == h[(i, j)]:
m[(i, j)] = (v, (i, j - 1))
return m[(i,j)][0]
def get_max_alignment(s, t):
sl, tl = len(s), len(t)
m = {(0, 0): (0, None)}
m.update({((i, 0), (i * -5, (i - 1, 0))) for i in range(1, sl + 1)})
m.update({((0, i), (i * -5, (0, i - 1))) for i in range(1, tl + 1)})
for i, j in product(range(1, sl + 1), range(1, tl + 1)):
cost = blosum62.get((s[i - 1], t[j - 1]))
if cost == None:
cost = blosum62.get((t[j - 1], s[i - 1]))
d = m[(i - 1, j - 1)][0] + cost
l = m[(i - 1, j)][0] - 5
u = m[(i, j - 1)][0] - 5
b = max(d, l, u)
if d == b:
m[(i, j)] = (b, (i - 1, j - 1))
elif l == b:
m[(i, j)] = (b, (i - 1, j))
elif u == b:
m[(i, j)] = (b, (i, j - 1))
return (m[(i, j)][0])
def match_callback(c1, c2):
return blosum62.get((c1, c2), 1 if c1 == c2 else -4)
iy = [[0] * (len(s2) + 1) for i in xrange(len(s1) + 1)]
for i in xrange(1, len(s1) + 1):
m[i][0] = -100
ix[i][0] = -b - (i - 1) * a
iy[i][0] = -100
for j in xrange(1, len(s2) + 1):
m[0][j] = -100
ix[0][j] = -100
iy[0][j] = -b - (j - 1) * a
l1, l2 = len(s1), len(s2)
for i in xrange(1, l1 + 1):
for j in xrange(1, l2 + 1):
sc = blosum62.get((s1[i - 1], s2[j - 1]))
if sc == None:
sc = blosum62.get((s2[j - 1], s1[i - 1]))
m[i][j] = max(m[i - 1][j - 1] + sc, ix[i - 1][j - 1] + sc,
iy[i - 1][j - 1] + sc)
ix[i][j] = max(m[i - 1][j] - b, ix[i - 1][j] - a, iy[i - 1][j] - b)
iy[i][j] = max(m[i][j - 1] - b, ix[i - 1][j] - b, iy[i][j - 1] - a)
print max(m[l1][l2], ix[l1][l2], iy[l1][l2])
def traceback(dist, s1, s2):
t1, t2, i, j = '', '', len(s1), len(s2)
while not (i == 0 and j == 0):
l, d, t = dist[i][j - 1], dist[i - 1][j - 1], dist[i - 1][j]
if s1[i - 1] == s2[j - 1] or (d == max(l, d, t) and d > l and d > t):
def create_figures(feature_to_weighted_sums, weight_totals, min_total,
report_dir, filetype):
aa_blosum = set()
for aa1, aa2 in blosum62.keys():
aa_blosum.add(aa1)
aa_blosum.add(aa2)
include_mask = weight_totals >= min_total
p = re.compile(r'aa_to_([A-Z])$')
aa_to_features = {}
for feature_name, weighted_sums in feature_to_weighted_sums.items():
m = p.match(feature_name)
if m:
aa = m[1]
mean_by_heads = np.where(include_mask,
weighted_sums / weight_totals, -1)
feature_vector = mean_by_heads.flatten()
feature_vector = feature_vector[feature_vector != -1]
aa_to_features[aa] = feature_vector
aas = sorted(aa_to_features.keys())
aas_set = set(aas)
print('Excluding following AAs not in feature set', aa_blosum - aas_set)
print('Excluding following AAs not in blosum62', aas_set - aa_blosum)
aa_list = sorted(list(aas_set & aa_blosum))
n_aa = len(aa_list)
corr = np.zeros((n_aa, n_aa))
for i, aa1 in enumerate(aa_list):
vector1 = aa_to_features[aa1]
for j, aa2 in enumerate(aa_list):
if i == j:
corr[i, j] = None
else:
vector2 = aa_to_features[aa2]
corr[i, j], _ = pearsonr(vector1, vector2)
cmap = 'Blues'
ax = sns.heatmap(corr, cmap=cmap, vmin=-0.5)
ax.set_xticklabels(aa_list)
ax.set_yticklabels(aa_list)
plt.savefig(report_dir / f'aa_corr_to.pdf', format=filetype)
plt.close()
blosum = np.zeros((n_aa, n_aa))
for i, aa1 in enumerate(aa_list):
for j, aa2 in enumerate(aa_list):
if i == j:
blosum[i, j] = None
else:
if blosum62.get((aa1, aa2)) is not None:
blosum[i, j] = blosum62.get((aa1, aa2))
else:
blosum[i, j] = blosum62.get((aa2, aa1))
ax = sns.heatmap(blosum, cmap=cmap, vmin=-4, vmax=4)
ax.set_xticklabels(aa_list)
ax.set_yticklabels(aa_list)
plt.savefig(report_dir / f'blosum62.pdf', format=filetype)
plt.close()
corr_scores = []
blos_scores = []
for i in range(n_aa):
for j in range(i):
corr_scores.append(corr[i, j])
blos_scores.append(blosum[i, j])
print('Pearson Correlation between feature corr and blosum',
pearsonr(corr_scores, blos_scores)[0])
def match_callback(c1, c2):
return blosum62.get((c1, c2), 1 if c1 == c2 else -4)
def score_function(x, y):
return blosum62.get((x, y), blosum62.get((y, x)))
def score_function(x, y):
if x == '-' or y == '-':
return -5
else:
return blosum62.get((x, y), blosum62.get((y, x)))
import sys
from itertools import product
from Bio.SubsMat.MatrixInfo import blosum62
if __name__ == '__main__':
s, t = sys.stdin.read().strip().split('\n')
sl, tl = len(s), len(t)
m = {(0, 0): (0, None)}
f = {}
g = {}
h = {}
m.update({((i, 0), (-11 - i, (i-1, 0))) for i in range(1, sl+1)})
m.update({((0, i), (-11 - i, (0, i-1))) for i in range(1, tl+1)})
for i, j in product(range(1, sl+1), range(1, tl+1)):
cost = blosum62.get((s[i-1], t[j-1]))
if cost == None:
cost = blosum62.get((t[j-1], s[i-1]))
f[(i, j)] = m[(i-1, j-1)][0] + cost
gg = g.get((i-1, j))
if gg != None:
gg -= 1
hh = h.get((i, j-1))
if hh != None:
hh -= 1
g[(i, j)] = max(m[(i-1, j)][0] - 12, gg)
h[(i, j)] = max(m[(i, j-1)][0] - 12, hh)
v = max(f[(i, j)], g[(i, j)], h[(i, j)])
if v == f[(i, j)]:
m[(i, j)] = (v, (i-1, j-1))
elif v == g[(i, j)]:
def blosum62_score(x, y):
cost = blosum62.get((x, y))
if cost == None: cost = blosum62.get((y, x))
return cost
