def question_two():
"""
Compute comparison of two human and fruitfly local alignment sequences and return percentage of matches between both.
"""
scoring_matrix = read_scoring_matrix(PAM50_URL)
local_results = question_one()
pax_seq = read_protein(CONSENSUS_PAX_URL)
dash, new_human, new_fly = "-", "", ""
percentages = []
#remove dashes from human and fruit fly sequences
for char in local_results[1]:
if char != dash:
new_human += char
print("Old human seq: " + local_results[1])
print("New human seq: " + new_human)
for char in local_results[2]:
if char != dash:
new_fly += char
print("Old fly seq: " + local_results[2])
print("New fly seq: " + new_fly)
#compute alignment matrices and calculate global alignments between human, fruit and pax
print("Computing alignment matrices and global alignments...")
align_matrix = prj4.compute_alignment_matrix(new_human, pax_seq,
scoring_matrix, True)
result_human_comp = prj4.compute_global_alignment(new_human, pax_seq,
scoring_matrix,
align_matrix)
#print result_human_comp
align_matrix = prj4.compute_alignment_matrix(new_fly, pax_seq,
scoring_matrix, True)
result_fly_comp = prj4.compute_global_alignment(new_fly, pax_seq,
scoring_matrix,
align_matrix)
#print result_fly_comp
#calculate percantage of matches between human, fruit, and pax
matches = 0
for index in range(len(result_human_comp[2])):
if result_human_comp[1][index] == result_human_comp[2][index]:
matches += 1
percentages.append(matches / float(len(result_human_comp[2])))
matches = 0
for index in range(len(result_fly_comp[2])):
if result_fly_comp[1][index] == result_fly_comp[2][index]:
matches += 1
percentages.append(matches / float(len(result_fly_comp[2])))
#return the two percentages in a list
return percentages
def check_spelling(checked_word, dist, word_list):
'''
Iterates through word_list and returns the set of all words that are within
edit distance dist of the string checked_word.
'''
# Set constants
ALPHABET = set(list(string.ascii_lowercase))
DIAG_SCORE = 2
OFF_DIAG_SCORE = 1
DASH_SCORE = 0
# contruct scoring matrix over all lower case letters
scoring_matrix = student.build_scoring_matrix(ALPHABET, DIAG_SCORE, OFF_DIAG_SCORE, DASH_SCORE)
# Init list to store words
close_words = []
# Loop over word in word_list
for word in word_list:
# compute alignment matrix
alignment_matrix = student.compute_alignment_matrix(checked_word, word, scoring_matrix, True)
# compute score of global alignments
score, align_x, align_y = student.compute_global_alignment(checked_word, word, scoring_matrix, alignment_matrix)
# calculate edit distance
edit_distance = len(checked_word) + len(word) - score
# Compare edit_distance and dist
if edit_distance <= dist:
# save word
close_words.append(word)
return close_words
def question_two():
"""
Compute comparison of two human and fruitfly local alignment sequences and return percentage of matches between both.
"""
scoring_matrix = read_scoring_matrix(PAM50_URL)
local_results = question_one()
pax_seq = read_protein(CONSENSUS_PAX_URL)
dash, new_human, new_fly = "-", "", ""
percentages = []
#remove dashes from human and fruit fly sequences
for char in local_results[1]:
if char != dash:
new_human += char
print "Old human seq: " + local_results[1]
print "New human seq: " + new_human
for char in local_results[2]:
if char != dash:
new_fly += char
print "Old fly seq: " + local_results[2]
print "New fly seq: " + new_fly
#compute alignment matrices and calculate global alignments between human, fruit and pax
print "Computing alignment matrices and global alignments..."
align_matrix = prj4.compute_alignment_matrix(new_human, pax_seq, scoring_matrix, True)
result_human_comp = prj4.compute_global_alignment(new_human, pax_seq, scoring_matrix, align_matrix)
#print result_human_comp
align_matrix = prj4.compute_alignment_matrix(new_fly, pax_seq, scoring_matrix, True)
result_fly_comp = prj4.compute_global_alignment(new_fly, pax_seq, scoring_matrix, align_matrix)
#print result_fly_comp
#calculate percantage of matches between human, fruit, and pax
matches = 0
for index in xrange(len(result_human_comp[2])):
if result_human_comp[1][index] == result_human_comp[2][index]:
matches += 1
percentages.append(matches / float(len(result_human_comp[2])))
matches = 0
for index in xrange(len(result_fly_comp[2])):
if result_fly_comp[1][index] == result_fly_comp[2][index]:
matches += 1
percentages.append(matches / float(len(result_fly_comp[2])))
#return the two percentages in a list
return percentages
def answer_Q7():
alphabet = set(['A', 'C', 'T', 'G'])
diag_score = 2
off_diag_score = 1
dash_score = 0
seq_x = 'AA'
seq_y = 'TAAT'
scoring_matrix = student.build_scoring_matrix(alphabet, diag_score, off_diag_score, dash_score)
alignment_matrix = student.compute_alignment_matrix(seq_x, seq_y, scoring_matrix, True)
score, align_x, align_y = student.compute_global_alignment(seq_x, seq_y, scoring_matrix, alignment_matrix)
edit_distance = len(seq_x) + len(seq_y) - score
return (diag_score, off_diag_score, dash_score)
def percent_match(local_alignment):
'''
Computes the percent similarilty between a local alignment to the
global alignment of the PAX sequence.
'''
# remove the '-' from the local alignment
local_alignment = local_alignment.replace('-', '')
# load the PAM50 scoring matrix
pam50_scoring_matrix = provided.read_scoring_matrix(PAM50_URL)
# load the consensus sequence
consensus_sequence = provided.read_protein(CONSENSUS_PAX_URL)
# compute the global alignment
alignment_matrix = student.compute_alignment_matrix(local_alignment, consensus_sequence, pam50_scoring_matrix, True)
# compute the global alignment
score, global_alignment, consensus_alignment = student.compute_global_alignment(local_alignment, consensus_sequence, pam50_scoring_matrix, alignment_matrix)
# Init the variable to store matches
match = 0
# loop over each character
for char in range(len(global_alignment)):
# compare characters between the two alignments
if global_alignment[char] == consensus_alignment[char]:
# increase the match score by 1
match += 1
return round(match/float(len(global_alignment))*100, 2)
print (Project_4.compute_alignment_matrix('', '', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, True))
#expected [[0]] but received []
print (Project_4.compute_alignment_matrix('A', 'A', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, True))
#expected [[0, -4], [-4, 6]]
print (Project_4.compute_alignment_matrix('ATG', 'ACG', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, True))
#expected [[0, -4, -8, -12], [-4, 6, 2, -2], [-8, 2, 8, 4], [-12, -2, 4, 14]]
if TEST3:
print (Project_4.compute_global_alignment('', '', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, [[0]]))
#expected tuple of length 3
print (Project_4.compute_global_alignment('A', 'A', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, [[0, -4], [-4, 6]]))
#expected 6, 'A', 'A'
print (Project_4.compute_global_alignment('ACTACT', 'AGCTA', {'A': {'A': 2, 'C': 1, '-': 0, 'T': 1, 'G': 1},
'C': {'A': 1, 'C': 2, '-': 0, 'T': 1, 'G': 1}, '-': {'A': 0, 'C': 0, '-': 0, 'T': 0, 'G': 0},
'T': {'A': 1, 'C': 1, '-': 0, 'T': 2, 'G': 1}, 'G': {'A': 1, 'C': 1, '-': 0, 'T': 1, 'G': 2}},
[[0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2], [0, 2, 3, 4, 4, 4], [0, 2, 3, 4, 6, 6], [0, 2, 3, 4, 6, 8],
[0, 2, 3, 5, 6, 8], [0, 2, 3, 5, 7, 8]]))
#expected 8, 'ACTACT', 'AGCTA',
print (Project_4.compute_global_alignment('abddcdeffgh', 'aabcddefghij',
{'-': {'-': -1, 'a': -1, 'c': -1, 'b': -1, 'e': -1, 'd': -1, 'g': -1, 'f': -1, 'i': -1, 'h': -1, 'k': -1, 'j': -1,
'm': -1, 'l': -1, 'o': -1, 'n': -1, 'q': -1, 'p': -1, 's': -1, 'r': -1, 'u': -1, 't': -1, 'w': -1, 'v': -1, 'y': -1, 'x': -1,
Question 2
"""
ali_human = result[1]
ali_fly = result[2]
seq_con = provided.read_protein(provided.CONSENSUS_PAX_URL)
ali_human = ali_human.replace('-', '')
ali_fly = ali_fly.replace('-', '')
global_alignment_mx_human = Project_4.compute_alignment_matrix(
ali_human, seq_con, scoring_matrix, True)
global_alignment_mx_fly = Project_4.compute_alignment_matrix(
ali_fly, seq_con, scoring_matrix, True)
result2_human = Project_4.compute_global_alignment(ali_human, seq_con,
scoring_matrix,
global_alignment_mx_human)
result2_fly = Project_4.compute_global_alignment(ali_fly, seq_con,
scoring_matrix,
global_alignment_mx_fly)
print 'Score: ' + str(result2_human[0])
print 'Local Human: ' + result2_human[1]
print 'Consensus: ' + result2_human[2]
print
print 'Score: ' + str(result2_fly[0])
print 'Local Fly: ' + result2_fly[1]
print 'Consensus: ' + result2_fly[2]
len_human = len(result2_human[1])
len_fly = len(result2_fly[1])
print Project_4.compute_alignment_matrix('', '', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, True)
#expected [[0]] but received []
print Project_4.compute_alignment_matrix('A', 'A', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, True)
#expected [[0, -4], [-4, 6]]
print Project_4.compute_alignment_matrix('ATG', 'ACG', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, True)
#expected [[0, -4, -8, -12], [-4, 6, 2, -2], [-8, 2, 8, 4], [-12, -2, 4, 14]]
if TEST3:
print Project_4.compute_global_alignment('', '', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, [[0]])
#expected tuple of length 3
print Project_4.compute_global_alignment('A', 'A', {'A': {'A': 6, 'C': 2, '-': -4, 'T': 2, 'G': 2},
'C': {'A': 2, 'C': 6, '-': -4, 'T': 2, 'G': 2}, '-': {'A': -4, 'C': -4, '-': -4, 'T': -4, 'G': -4},
'T': {'A': 2, 'C': 2, '-': -4, 'T': 6, 'G': 2}, 'G': {'A': 2, 'C': 2, '-': -4, 'T': 2, 'G': 6}}, [[0, -4], [-4, 6]])
#expected 6, 'A', 'A'
print Project_4.compute_global_alignment('ACTACT', 'AGCTA', {'A': {'A': 2, 'C': 1, '-': 0, 'T': 1, 'G': 1},
'C': {'A': 1, 'C': 2, '-': 0, 'T': 1, 'G': 1}, '-': {'A': 0, 'C': 0, '-': 0, 'T': 0, 'G': 0},
'T': {'A': 1, 'C': 1, '-': 0, 'T': 2, 'G': 1}, 'G': {'A': 1, 'C': 1, '-': 0, 'T': 1, 'G': 2}},
[[0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2], [0, 2, 3, 4, 4, 4], [0, 2, 3, 4, 6, 6], [0, 2, 3, 4, 6, 8],
[0, 2, 3, 5, 6, 8], [0, 2, 3, 5, 7, 8]])
#expected 8, 'ACTACT', 'AGCTA',
print Project_4.compute_global_alignment('abddcdeffgh', 'aabcddefghij',
{'-': {'-': -1, 'a': -1, 'c': -1, 'b': -1, 'e': -1, 'd': -1, 'g': -1, 'f': -1, 'i': -1, 'h': -1, 'k': -1, 'j': -1,
'm': -1, 'l': -1, 'o': -1, 'n': -1, 'q': -1, 'p': -1, 's': -1, 'r': -1, 'u': -1, 't': -1, 'w': -1, 'v': -1, 'y': -1, 'x': -1,
"""
Question 2
"""
ali_human = result[1]
ali_fly = result[2]
seq_con = provided.read_protein(provided.CONSENSUS_PAX_URL)
ali_human = ali_human.replace('-', '')
ali_fly = ali_fly.replace('-', '')
global_alignment_mx_human = Project_4.compute_alignment_matrix(ali_human, seq_con, scoring_matrix, True)
global_alignment_mx_fly = Project_4.compute_alignment_matrix(ali_fly, seq_con, scoring_matrix, True)
result2_human = Project_4.compute_global_alignment(ali_human, seq_con, scoring_matrix, global_alignment_mx_human)
result2_fly = Project_4.compute_global_alignment(ali_fly, seq_con, scoring_matrix, global_alignment_mx_fly)
print 'Score: ' + str(result2_human[0])
print 'Local Human: ' + result2_human[1]
print 'Consensus: ' + result2_human[2]
print
print 'Score: ' + str(result2_fly[0])
print 'Local Fly: ' + result2_fly[1]
print 'Consensus: ' + result2_fly[2]
len_human = len(result2_human[1])
len_fly = len(result2_fly[1])
