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sw_local_alignment.py
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sw_local_alignment.py
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from blosum import blosum62
from proteins import proteins
import numpy as np
import time
# Tam Dang
# 10.10.17
# CSE 427 - Computational Biology
# Smith-Waterson Local Sequence Alignment
# For running SW on two specific proteins
# Note that this program will fail for invalid protein sequences
def main():
prompt1 = input("Calculate Smith-Waterman alignment between two proteins? (y/n): ")
if prompt1.startswith("y"):
n1 = input("Enter name of first protein: ")
p1 = input("Enter first protein's sequence of amino acids: ")
n2 = input("Enter name of second protein: ")
p2 = input("Enter second protein's sequence of amino acids: ")
print("Calculating alignment between", n1, "and", n2, ":\n")
sw = SmithWatermanAlignment(p1, p2)
sw.align_sequences()
sw.print_alignment(n1, n2)
print("Score:", sw.max_score, "\n")
show_table = input("Show table? (y/n): ")
if show_table.startswith("y"):
print("Smith-Waterman Alignment Table for deadly & ddgearlyk:")
print("(Horizontal (T) -", n1, " Vertical (S) -", n2, "\n")
print(sw.alignment_table)
# Uncomment to reproduce all the results text files.
#
# Takes approximately 2 hrs to finish.
# generate_results()
# Generates results to answer the HW2 questions.
def generate_results():
print("Hello")
f1 = open('short_example.txt', 'w')
f2 = open('protein_alignments.txt', 'w')
f3 = open('protein_alignment_scores.txt', 'w')
f4 = open('myod1_lin32_syfm_pvalues.txt', 'w')
# Short example
sw = SmithWatermanAlignment("deadly", "ddgearlyk")
sw.align_sequences()
print("Calculating deadly & ddgearlyk p-value: \n")
f1.write("p-value for deadly & ddgearlyk: ")
f1.write(str(permute_and_score("deadly", "ddgearlyk", 100000)))
f1.write("\n")
f1.write("Smith-Waterman Alignment Table for deadly & ddgearlyk:\n")
f1.write("(Horizontal (T) - ddgearlyk, vertical (S) - deadly) \n\n")
f1.write(str(sw.alignment_table))
f1.write("\n\n")
f1.write("Alignment:\n")
sw.print_alignment_to_file("DEADLY", "DDGEARLYK", f1)
f1.write("Score: ")
f1.write(str(sw.max_score))
end = time.clock()
print("TIME:", end)
## Protein alignments
done = []
alignment_scores = []
print("Calculating alignments between proteins:")
count = 0
for p1, v1 in proteins.items():
s_chars = list(v1)
for p2, v2 in proteins.items():
if (p1 != p2 and p2 not in done):
t_chars = list(v2)
sw_p1_p2 = SmithWatermanAlignment(v1, v2)
sw_p1_p2.align_sequences()
sw_p1_p2.print_alignment_to_file(p1, p2, f2)
alignment_scores.append((p1, p2, sw_p1_p2.max_score))
print("Alignment - ", count)
count += 1
done.append(p1)
print(done)
print(len(alignment_scores), "total comparisons made!")
# Printing output of scores to file for 10x10 matrix
score_format = '{0:6} & {1:6}: {2}\n'
for score in alignment_scores:
entries = [score[0], score[1], score[2]]
f3.write(score_format.format(*entries))
## p-values between P15172 and Q10574
print("Calculating P15172 & Q10574 p-value:")
f4.write("p-value for P15172 & Q10574: ")
f4.write(str(permute_and_score(proteins.get("P15172"), proteins.get("Q10574"), 1000)))
f4.write("\n\n")
print("Calculating P15172 & O95363 p-value:")
f4.write("p-value for P15172 & O95363: ")
f4.write(str(permute_and_score(proteins.get("P15172"), proteins.get("O95363"), 1000)))
f4.write("\n\n")
# Calculates the p-value given two sequences, such that
# the S sequence is permuted and checked against the score
# between the two inputs.
#
# Returns a value of the form (k + 1) / (N + 1), where
# k is the number of times a permuted string and S outscored
# S and T
def permute_and_score(s, t, N):
sw = SmithWatermanAlignment(s, t)
sw.align_sequences()
score_s_t = sw.max_score
k = 0
for i in range(0, N):
permuted_s = permute(s)
sw_on_permuted = SmithWatermanAlignment(s, permuted_s)
sw_on_permuted.align_sequences()
if (sw_on_permuted.max_score >= score_s_t):
k += 1
return (k + 1) / (N + 1)
# # Returns a random permutation of s,
# # uniformly distributed
def permute(s):
n = len(s)
permuted_s = list(s)
for i in range(n - 1, 0, -1):
j = np.random.randint(i)
# Swap
s1 = permuted_s[i]
s2 = permuted_s[j]
permuted_s[i] = s2
permuted_s[j]= s1
return ''.join(permuted_s)
class SmithWatermanAlignment:
# Constructs and returns the alignment table using
# scores from BLOSUM62 for two protein sequences
#
# Returns the table as well as the coordinates of
# the largest score to begin the backtrace
def __init__(self, S, T):
self.s_align = None
self.t_align = None
self.full_backtrace = None
# Insert empty string to serve as empty prefix
self.s_chars = list(S.upper())
self.t_chars = list(T.upper())
self.s_chars.insert(0, '')
self.t_chars.insert(0, '')
# In this alignment table, S is along the left hand edge
self.alignment_table = np.zeros(shape=(len(self.s_chars), len(self.t_chars)))
x, y = None, None
max_score = None
for i, s_i in enumerate(self.s_chars):
# row for ith character of s
row = []
for j, t_j in enumerate(self.t_chars):
next_score = self.calculate_opt_score(s_i, t_j, i, j)
self.alignment_table[i][j] = next_score
if max_score is None or next_score > max_score:
max_score = next_score
x = i
y = j
self.max_score = max_score
self.max_score_x = x
self.max_score_y = y
# Calculates the smith-waterman local alignment score as well as
# the alignments.
def align_sequences(self):
# Backtrack from the maximum score
i, j = self.max_score_x, self.max_score_y
path = [(i, j)]
current_entry = self.alignment_table[i, j]
while current_entry != 0:
# Choose next direction and append to path
i, j = self.backtrace(self.s_chars[i], self.t_chars[j], i, j)
current_entry = self.alignment_table[i, j]
if current_entry != 0:
path.append((i, j))
full_backtrace = list(reversed(path))
# Constructs the alignment using the path found in backtrace
curr_place = None
alignment_length = len(full_backtrace)
self.s_align = np.chararray((alignment_length,))
self.t_align = np.chararray((alignment_length,))
for idx, (i, j) in enumerate(full_backtrace):
# initial alignment
if curr_place is None:
self.s_align[idx] = self.s_chars[i]
self.t_align[idx] = self.t_chars[j]
else:
if i == curr_place[0] + 1 and j == curr_place[1] + 1:
self.s_align[idx] = self.s_chars[i]
self.t_align[idx] = self.t_chars[j]
elif i == curr_place[0] and j == curr_place[1] + 1:
self.s_align[idx] = '-'
self.t_align[idx] = self.t_chars[j]
else:
self.s_align[idx] = self.s_chars[i]
self.t_align[idx] = '-'
curr_place = (i, j)
self.full_backtrace = full_backtrace
# Scores the left, diagonal, and above neighbor according to score + neighbor
#
# Returns the three as a list
def score_neighbors(self, u, v, i, j):
# Initialize values of neighbors
#
# Taking the left and above neighbors means incurring a penalty for
# including a gap
#
# diagonal includes the blosom62 score
left = self.alignment_table[i][j - 1] - 4 if j > 0 else None
diagonal = self.alignment_table[i - 1][j - 1] + blosum62[u][v] if i > 0 and j > 0 else None
above = self.alignment_table[i - 1][j] - 4 if i > 0 else None
return [left, diagonal, above, 0]
# Calculates the score for an unfilled entry in the
# alignment table.
#
# u : current char from S, v : current char from T,
# (i, j) : coordinates of the entry to be filled, where the letters
# that meet at (i, j) of the alignment_table are S[i] and T[j]
#
# i and j are assumed to be nonnegative
#
# returns the max score + neighbor combination and where
# it came from
def calculate_opt_score(self, u, v, i, j):
# First entry in table
if i == 0 or j == 0:
return 0
scores = self.score_neighbors(u, v, i, j)
max_score = None
for i, score in enumerate(scores):
if max_score is None or score is not None and max_score < score:
max_score = score
# Specific to local alignment
return max(max_score, 0)
# Calculates the next neighbor to jump to based on the
# maximum score + maximum neighbor
def backtrace(self, u, v, i, j):
# First entry in table
if i == 0 and j == 0:
return (0, 0)
scores = self.score_neighbors(u, v, i, j)
# In edge cases, there might not be a left or above
max_neighbor = None
max_neighbor_idx = None
for idx, score in enumerate(scores):
if (max_neighbor is None) or (score is not None and score > max_neighbor):
max_neighbor = score
max_neighbor_idx = idx
# Slight tweak here: in the case of a three-way tie, favor the diagonal
max_neighbor_coordinates = None
if max_neighbor_idx == 1:
max_neighbor_coordinates = (i - 1, j - 1)
elif max_neighbor_idx == 0:
max_neighbor_coordinates = (i, j - 1)
else:
max_neighbor_coordinates = (i - 1, j)
return max_neighbor_coordinates
# Prints protein alignments to a file
def print_alignment(self, p1, p2):
s_align_str = b''.join(self.s_align).decode('utf-8')
t_align_str = b''.join(self.t_align).decode('utf-8')
s_align_fragments = []
t_align_fragments = []
space_out = '{0:6}: {1:3} {2:20}'
if len(s_align_str) < 60:
r1 = [p1, self.full_backtrace[0][0], s_align_str]
r2 = [p2, self.full_backtrace[0][1], t_align_str]
print(space_out.format(*r1))
print(space_out.format(*r2))
else:
while (len(s_align_str) > 60):
s_align_fragments.append(s_align_str[0:60])
t_align_fragments.append(t_align_str[0:60])
s_align_str = s_align_str[60:len(s_align_str)]
for i in range(len(s_align_fragments)):
r1 = [p1, self.full_backtrace[0][0]+ i * 60, s_align_fragments[i]]
r2 = [p2, self.full_backtrace[0][1] + i * 60, t_align_fragments[i]]
print(space_out.format(*r1))
print(space_out.format(*r2))
# Prints protein alignments to a file
def print_alignment_to_file(self, p1, p2, f):
s_align_str = b''.join(self.s_align).decode('utf-8')
t_align_str = b''.join(self.t_align).decode('utf-8')
s_align_fragments = []
t_align_fragments = []
space_out = '{0:6}: {1:3} {2:20}'
if len(s_align_str) < 60:
r1 = [p1, self.full_backtrace[0][0], s_align_str]
r2 = [p2, self.full_backtrace[0][1], t_align_str]
f.write(space_out.format(*r1))
f.write("\n")
f.write(space_out.format(*r2))
f.write("\n\n")
print(space_out.format(*r1))
print(space_out.format(*r2))
else:
while (len(s_align_str) > 60):
s_align_fragments.append(s_align_str[0:60])
t_align_fragments.append(t_align_str[0:60])
s_align_str = s_align_str[60:len(s_align_str)]
for i in range(len(s_align_fragments)):
r1 = [p1, self.full_backtrace[0][0]+ i * 60, s_align_fragments[i]]
r2 = [p2, self.full_backtrace[0][1] + i * 60, t_align_fragments[i]]
f.write(space_out.format(*r1))
f.write("\n")
f.write(space_out.format(*r2))
f.write("\n\n")
print(space_out.format(*r1))
print(space_out.format(*r2))
print(p1, "&", p2, "alignment completed \n")
if __name__ == "__main__":
main()