def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
""" null distribution generator """
scoring_distribution = {}
for dummy_trial in range(num_trials):
y_index = range(len(seq_y))
# shuffle the y sequence
random.shuffle(y_index)
rand_y = ""
for index in y_index:
rand_y += seq_y[index]
# compute local alignment matrix
align_matrix = student.compute_alignment_matrix(seq_x, rand_y, scoring_matrix, False)
# compute local alignment score
score, x_align, y_align = student.compute_local_alignment(seq_x, rand_y, scoring_matrix, align_matrix)
if scoring_distribution.has_key(score):
scoring_distribution[score] += 1
else:
scoring_distribution[score] = 1
return scoring_distribution
def question2():
scoring_matrix =read_scoring_matrix(PAM50_URL)
human = read_protein(HUMAN_EYELESS_URL)
fly = read_protein(FRUITFLY_EYELESS_URL)
# for question 3
# acids = 'ACBEDGFIHKMLNQPSRTWVYXZ'
# hlen = len(human)
# flen = len(fly)
# human_random, fly_random = '', ''
# for dummy_i in xrange(hlen):
# human_random = human_random + human[random.randint(1,23)]
# for dummy_i in xrange(flen):
# fly_random = fly_random + fly[random.randint(1,23)]
# human = human_random
# fly = fly_random
consensusPAX = read_protein(CONSENSUS_PAX_URL)
alignment_matrix = student.compute_alignment_matrix(human, fly, scoring_matrix, False)
local_result = student.compute_local_alignment(human, fly, scoring_matrix, alignment_matrix)
local_human = ''.join(local_result[1].split('-'))
local_fly = ''.join(local_result[2].split('-'))
human_P = student.compute_alignment_matrix(local_human,consensusPAX,scoring_matrix, True)
human_result = student.compute_global_alignment(local_human,consensusPAX, scoring_matrix, human_P)
fly_P = student.compute_alignment_matrix(local_fly,consensusPAX, scoring_matrix, True)
fly_result = student.compute_global_alignment(local_fly,consensusPAX, scoring_matrix, fly_P)
total = len(consensusPAX)
human_count, fly_count =0, 0
for dummy_i in xrange(total):
if human_result[1][dummy_i] == human_result[2][dummy_i]:
human_count += 1
if fly_result[1][dummy_i] ==fly_result[2][dummy_i]:
fly_count += 1
print human_count * 1.0 / total
print fly_count * 1.0 / total
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
"""
input : two sequences, scoring matrix and number of trial
output : dictionary of scoring_distribution
"""
dict = {}
test = 1
seq_list = list(seq_y)
while test < num_trials:
# shuffle seq y
random.shuffle(seq_list)
rand_y = ''.join(seq_list)
# local alignment of seq_x and rand_y
alignment_matrix = student.compute_alignment_matrix(
seq_x, rand_y, scoring_matrix, False)
local_alignment = student.compute_local_alignment(
seq_x, rand_y, scoring_matrix, alignment_matrix)
# add score to dict
dict[test] = local_alignment[0]
# update test time
test += 1
return dict
def run_q1():
seq_x = read_protein(HUMAN_EYELESS_URL)
seq_y = read_protein(FRUITFLY_EYELESS_URL)
scoring_matrix = read_scoring_matrix(PAM50_URL)
alignment_matrix = student.compute_alignment_matrix(
seq_x, seq_y, scoring_matrix, False)
return student.compute_local_alignment(seq_x, seq_y, scoring_matrix,
alignment_matrix)
def check_spelling(checked_word, dist, word_list):
answer = []
for word in word_list:
alignment_matrix = student.compute_alignment_matrix(
checked_word, word, scoring_matrix, True)
result = student.compute_local_alignment(checked_word, word,
scoring_matrix,
alignment_matrix)
if (len(checked_word) + len(word) - result[0]) <= dist:
answer.append(word)
return answer
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
scoring_distribution = {}
rand_y = list(seq_y[:])
for trial in range(num_trials):
random.shuffle(rand_y)
alignment_matrix = student.compute_alignment_matrix(seq_x, rand_y, scoring_matrix, False)
score = student.compute_local_alignment(seq_x, rand_y, scoring_matrix, alignment_matrix)[0]
if score in scoring_distribution:
scoring_distribution[score] += 1
else:
scoring_distribution[score] = 1
return scoring_distribution
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
scoring_distribution = dict()
for num in xrange(num_trials):
rand_y = ''.join(random.sample(seq_y, len(seq_y)))
align = student.compute_alignment_matrix(seq_x, rand_y, scoring_matrix, False)
result = student.compute_local_alignment(seq_x, rand_y, scoring_matrix, align)
score = result[0]
if score not in scoring_distribution:
scoring_distribution[score] = 1
else:
scoring_distribution[score] += 1
return scoring_distribution
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
from collections import defaultdict
scoring_distribution = defaultdict(int)
for trial in range(num_trials):
chars = list(seq_y)
random.shuffle(chars)
rand_y = ''.join(chars)
local_alignment_matrix = student.compute_alignment_matrix(
seq_x, rand_y, scoring_matrix, False)
score = student.compute_local_alignment(seq_x, rand_y, scoring_matrix,
local_alignment_matrix)[0]
scoring_distribution[score] += 1
return scoring_distribution
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
scoring_distribution = {}
rand_y = list(seq_y[:])
for trial in range(num_trials):
random.shuffle(rand_y)
alignment_matrix = student.compute_alignment_matrix(
seq_x, rand_y, scoring_matrix, False)
score = student.compute_local_alignment(seq_x, rand_y, scoring_matrix,
alignment_matrix)[0]
if score in scoring_distribution:
scoring_distribution[score] += 1
else:
scoring_distribution[score] = 1
return scoring_distribution
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
scoring_distribution = {}
list_y = list(seq_y)
for trial in range(num_trials):
temp_y = list_y
random.shuffle(temp_y)
rand_y = ''.join(temp_y)
alignment_matrix = student.compute_alignment_matrix(
seq_x, rand_y, scoring_matrix, False)
score, _, _ = student.compute_local_alignment(seq_x, rand_y,
scoring_matrix,
alignment_matrix)
scoring_distribution[score] = scoring_distribution.get(score, 0) + 1
return scoring_distribution
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials) :
score_distribution = {}
seq_y = list(seq_y)
for count in range(num_trials) :
print count
random.shuffle(seq_y)
alignment_matrix = student.compute_alignment_matrix(seq_x, seq_y, scoring_matrix, False)
score, _, _ = student.compute_local_alignment(seq_x, seq_y, scoring_matrix, alignment_matrix)
if score not in score_distribution :
score_distribution[score] = 0
score_distribution[score] += 1
return score_distribution
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trial):
scoring_distribution = dict()
for dummy_idx in range(num_trial):
rand_y = list(seq_y)
random.shuffle(rand_y)
rand_y = ''.join(rand_y)
alignment_matrix = student.compute_alignment_matrix(
seq_x, rand_y, scoring_matrix, False)
result = student.compute_local_alignment(seq_x, rand_y, scoring_matrix,
alignment_matrix)
score = result[0]
if scoring_distribution.has_key(score):
scoring_distribution[score] += 1
else:
scoring_distribution[score] = 1
return scoring_distribution
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
"""
return a dictionary scoring_distribution that represents an un-normalized distribution
"""
distribution = {}
for trial in range(num_trials):
start = time.time()
rand_y = list(seq_y)
random.shuffle(rand_y)
rand_y = ''.join(rand_y)
alignment_matrix = alg_project4_solution.compute_alignment_matrix(seq_x, rand_y, scoring_matrix, False)
alignment = alg_project4_solution.compute_local_alignment(seq_x, rand_y, scoring_matrix, alignment_matrix)
score = alignment[0]
if score in distribution:
distribution[score] += 1
else:
distribution[score] = 1
return distribution
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
"""
return a dictionary scoring_distribution that represents an un-normalized distribution
"""
distribution = {}
for trial in range(num_trials):
start = time.time()
rand_y = list(seq_y)
random.shuffle(rand_y)
rand_y = ''.join(rand_y)
alignment_matrix = alg_project4_solution.compute_alignment_matrix(
seq_x, rand_y, scoring_matrix, False)
alignment = alg_project4_solution.compute_local_alignment(
seq_x, rand_y, scoring_matrix, alignment_matrix)
score = alignment[0]
if score in distribution:
distribution[score] += 1
else:
distribution[score] = 1
return distribution
def question1():
""" determine local alignment of human and fruitfly eyeless protein """
# load sequences and scoring matrix
score_matrix = read_scoring_matrix(PAM50_URL)
human_eyeless = read_protein(HUMAN_EYELESS_URL)
fruitfly_eyeless = read_protein(FRUITFLY_EYELESS_URL)
# compute local alignment matrix
align_matrix = student.compute_alignment_matrix(human_eyeless, fruitfly_eyeless,
score_matrix, False)
# compute local alignment score and sequences
score, human_align, fruitfly_align = student.compute_local_alignment(human_eyeless, fruitfly_eyeless,
score_matrix, align_matrix)
print "Score: " + str(score)
print "Human: " + human_align
print "FrFly: " + fruitfly_align
return
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
""" str, str, dict of dict, int -> dict
Takes two sequences, a scoring matrix, and a number of trials, and returns
a dictionary of unnormalized
"""
scoring_distribution = {}
for trial in range(num_trials):
list_y = list(seq_y)
random.shuffle(list_y)
rand_y = ''.join(list_y)
alignment_matrix = student.compute_alignment_matrix(
human, rand_y, scoring_matrix, False)
alignment = student.compute_local_alignment(human, rand_y,
scoring_matrix,
alignment_matrix)
score = alignment[0]
if score in scoring_distribution:
scoring_distribution[score] += 1
else:
scoring_distribution[score] = 1
return scoring_distribution
def q3() :
len_gen = len(seq_x)
seq_x = []
seq_y = []
for _ in range(len_gen) :
seq_x.append(random.choice("ACBEDGFIHKMLNQPSRTWVYXZ"))
seq_y.append(random.choice("ACBEDGFIHKMLNQPSRTWVYXZ"))
alignment_matrix = student.compute_alignment_matrix(seq_x, seq_y, scoring_matrix, False)
score, human_aligen, fruit_aligen = student.compute_local_alignment(seq_x, seq_y, scoring_matrix, alignment_matrix)
print score
print human_aligen.replace('-', '')
print fruit_aligen
human_aligen = human_aligen.replace('-', '')
fruit_aligen = fruit_aligen.replace('-', '')
pax = read_protein(CONSENSUS_PAX_URL)
alignment_matrix = student.compute_alignment_matrix(human_aligen, pax, scoring_matrix, False)
score, h1, h2 = student.compute_global_alignment(human_aligen, pax, scoring_matrix, alignment_matrix)
print len(h1), len(h2)
same = 0
for i in range(len(h1)) :
if h1[i] == h2[i] :
same += 1
print same * 1.0 / len(h1)
alignment_matrix = student.compute_alignment_matrix(fruit_aligen, pax, scoring_matrix, False)
score, f1, f2 = student.compute_global_alignment(fruit_aligen, pax, scoring_matrix, alignment_matrix)
print len(f1), len(f2)
same = 0
for i in range(len(f1)) :
if f1[i] == f2[i] :
same += 1
print same * 1.0 / len(f1)
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
"""
input:
two sequences, scoring matrix, number of trials.
A trial is defined as:
1. Generate a random permutation rand_y of the sequence seq_y using random.shuffle().
2. Compute the maximum value score for the local alignment of seq_x and rand_y using the score matrix scoring_matrix.
3. Increment the entry score in the dictionary scoring_distribution by one.
output:
a dictionary scoring_distribution that represents an un-normalized distribution
"""
scoring_distribution = {}
for i in range(num_trials):
rand_y = random.sample(seq_y, len(seq_y))
local_S = student.compute_alignment_matrix(seq_x, rand_y, scoring_matrix, False)
local_alignment = student.compute_local_alignment(seq_x, rand_y, scoring_matrix, local_S)
if local_alignment[0] in scoring_distribution:
scoring_distribution[local_alignment[0]] += 1
else:
scoring_distribution[local_alignment[0]] = 1
return scoring_distribution
print "Loaded a dictionary with", len(word_list), "words"
return word_list
# Q1
HumanEyelessProtein = read_protein(HUMAN_EYELESS_URL)
FruitflyEyelessProtein = read_protein(FRUITFLY_EYELESS_URL)
PAM50 = read_scoring_matrix(PAM50_URL)
alignment_matrix_Q1 = student.compute_alignment_matrix(HumanEyelessProtein,
FruitflyEyelessProtein,
PAM50, False)
result_Q1 = student.compute_local_alignment(HumanEyelessProtein,
FruitflyEyelessProtein, PAM50,
alignment_matrix_Q1)
# Q2
TempHumanSeq = result_Q1[1]
FruitflySeq = result_Q1[2]
HumanSeq = TempHumanSeq[:len(TempHumanSeq) -
3] + TempHumanSeq[len(TempHumanSeq) - 2:]
ConsensusPAXDomain = read_protein(CONSENSUS_PAX_URL)
alignment_matrix_Q2_Human = student.compute_alignment_matrix(
HumanSeq, ConsensusPAXDomain, PAM50, True)
alignment_matrix_Q2_Fruitfly = student.compute_alignment_matrix(
FruitflySeq, ConsensusPAXDomain, PAM50, True)
word_file = open(filename)
# read in files as string
words = word_file.read()
# template lines and solution lines list of line string
word_list = words.split('\n')
print "Loaded a dictionary with", len(word_list), "words"
return word_list
scoring_matrix = read_scoring_matrix(PAM50_URL)
seq_x = read_protein(HUMAN_EYELESS_URL)
seq_y = read_protein(FRUITFLY_EYELESS_URL)
alignment_matrix = student.compute_alignment_matrix(seq_x, seq_y, scoring_matrix, False)
score, human_aligen, fruit_aligen = student.compute_local_alignment(seq_x, seq_y, scoring_matrix, alignment_matrix)
print score
exit()
def q3() :
len_gen = len(seq_x)
seq_x = []
seq_y = []
for _ in range(len_gen) :
seq_x.append(random.choice("ACBEDGFIHKMLNQPSRTWVYXZ"))
seq_y.append(random.choice("ACBEDGFIHKMLNQPSRTWVYXZ"))
alignment_matrix = student.compute_alignment_matrix(seq_x, seq_y, scoring_matrix, False)
score, human_aligen, fruit_aligen = student.compute_local_alignment(seq_x, seq_y, scoring_matrix, alignment_matrix)
print score
from matplotlib import pyplot
PAM50_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_PAM50.txt"
HUMAN_EYELESS_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_HumanEyelessProtein.txt"
FRUITFLY_EYELESS_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_FruitflyEyelessProtein.txt"
CONSENSUS_PAX_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_ConsensusPAXDomain.txt"
WORD_LIST_URL = "http://storage.googleapis.com/codeskulptor-assets/assets_scrabble_words3.txt"
# question 1
hep = alg_alignment.read_protein(HUMAN_EYELESS_URL)
fep = alg_alignment.read_protein(FRUITFLY_EYELESS_URL)
scoring_matrix = alg_alignment.read_scoring_matrix(PAM50_URL)
hep_fep_local_alignment = alg_project4_solution.compute_local_alignment(hep, fep, scoring_matrix,
alg_project4_solution.compute_alignment_matrix(
hep, fep, scoring_matrix, False))
human_eyeless_fruitfly_local_alignment_score = hep_fep_local_alignment[0]
# question 1 answer
print "local alignment for human and fruitfly eyeless genome: " + str(hep_fep_local_alignment)
# question 2
cpd = alg_alignment.read_protein(CONSENSUS_PAX_URL)
hep_local_alignment = hep_fep_local_alignment[1]
fep_local_alignment = hep_fep_local_alignment[2]
hep_local_alignment_no_dashes = hep_local_alignment.replace('-', '')
hep_no_dashes_cpd_global_alignment = alg_project4_solution.compute_global_alignment(hep_local_alignment_no_dashes, cpd, scoring_matrix, alg_project4_solution.compute_alignment_matrix(
hep_local_alignment_no_dashes, cpd, scoring_matrix, True))
def run_q1():
seq_x = read_protein(HUMAN_EYELESS_URL)
seq_y = read_protein(FRUITFLY_EYELESS_URL)
scoring_matrix = read_scoring_matrix(PAM50_URL)
alignment_matrix = student.compute_alignment_matrix(seq_x, seq_y, scoring_matrix, False)
return student.compute_local_alignment(seq_x, seq_y, scoring_matrix, alignment_matrix)
def question1():
scoring_matrix =read_scoring_matrix(PAM50_URL)
human = read_protein(HUMAN_EYELESS_URL)
fly = read_protein(FRUITFLY_EYELESS_URL)
alignment_matrix = student.compute_alignment_matrix(human, fly, scoring_matrix, False)
print student.compute_local_alignment(human, fly, scoring_matrix, alignment_matrix)
import math
from matplotlib import pyplot
PAM50_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_PAM50.txt"
HUMAN_EYELESS_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_HumanEyelessProtein.txt"
FRUITFLY_EYELESS_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_FruitflyEyelessProtein.txt"
CONSENSUS_PAX_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_ConsensusPAXDomain.txt"
WORD_LIST_URL = "http://storage.googleapis.com/codeskulptor-assets/assets_scrabble_words3.txt"
# question 1
hep = alg_alignment.read_protein(HUMAN_EYELESS_URL)
fep = alg_alignment.read_protein(FRUITFLY_EYELESS_URL)
scoring_matrix = alg_alignment.read_scoring_matrix(PAM50_URL)
hep_fep_local_alignment = alg_project4_solution.compute_local_alignment(
hep, fep, scoring_matrix,
alg_project4_solution.compute_alignment_matrix(hep, fep, scoring_matrix,
False))
human_eyeless_fruitfly_local_alignment_score = hep_fep_local_alignment[0]
# question 1 answer
print "local alignment for human and fruitfly eyeless genome: " + str(
hep_fep_local_alignment)
# question 2
cpd = alg_alignment.read_protein(CONSENSUS_PAX_URL)
hep_local_alignment = hep_fep_local_alignment[1]
fep_local_alignment = hep_fep_local_alignment[2]
hep_local_alignment_no_dashes = hep_local_alignment.replace('-', '')
hep_no_dashes_cpd_global_alignment = alg_project4_solution.compute_global_alignment(
# template lines and solution lines list of line string
word_list = words.split('\n')
print "Loaded a dictionary with", len(word_list), "words"
return word_list
# Question 1 ##################################################################
HUMAN_EYELESS = read_protein(HUMAN_EYELESS_URL)
FRUITFLY_EYELESS = read_protein(FRUITFLY_EYELESS_URL)
SCORING_MATRIX = read_scoring_matrix(PAM50_URL)
ALIGNMENT_MATRIX = student.compute_alignment_matrix(HUMAN_EYELESS, \
FRUITFLY_EYELESS,\
SCORING_MATRIX, False)
student.compute_local_alignment(HUMAN_EYELESS, FRUITFLY_EYELESS,\
SCORING_MATRIX, ALIGNMENT_MATRIX)
# Question 2 ##################################################################
PAX = read_protein(CONSENSUS_PAX_URL)
loc_score, loc_human, loc_fly = student.compute_local_alignment(HUMAN_EYELESS,\
FRUITFLY_EYELESS,\
SCORING_MATRIX,\
ALIGNMENT_MATRIX)
for align in (loc_human, loc_fly):
align = align.replace('-', '')
alignment_matrix = student.compute_alignment_matrix(
align, PAX, SCORING_MATRIX, True)
score, alignment, cons = student.compute_global_alignment(
align, PAX, SCORING_MATRIX, alignment_matrix)
def find_local_align():
score_matrix = read_scoring_matrix(PAM50_URL)
seq_human = read_protein(HUMAN_EYELESS_URL)
seq_fly = read_protein(FRUITFLY_EYELESS_URL)
local_alignment_matrix = student.compute_alignment_matrix(
seq_human, seq_fly, score_matrix, False)
score, seq_loc_human, seq_loc_fly = student.compute_local_alignment(
seq_human, seq_fly, score_matrix, local_alignment_matrix)
length = len(seq_loc_fly)
agree = 0
for idx in range(length):
if seq_loc_fly[idx] == seq_loc_human[idx]:
agree += 1
print 'Question 1:\n'
print 'score:', score, '\nhuman:', seq_loc_human, '\nfly: ', seq_loc_fly
print 'Agree percentage: %.2f' % (100 * float(agree) / length)
"""
Question 1:
local alignment score: 875
human: HSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLLSEGVCTNDNIPSVSSINRVLRNLASEK-QQ
fly: HSGVNQLGGVFVGGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATAEVVSKISQYKRECPSIFAWEIRDRLLQENVCTNDNIPSVSSINRVLRNLAAQKEQQ
Agree percentage: 93.98%
"""
### Question 2 ###
print '\nQuestion 2:\n'
seq_loc_human = seq_loc_human.replace('-', '')
seq_loc_fly = seq_loc_fly.replace('-', '')
seq_pax = read_protein(CONSENSUS_PAX_URL) #Q2
# seq_pax = 'ACBEDGFIHKMLNQPSRTWVYXZ' #Q3
for idx in range(2):
if idx == 0:
seq = seq_loc_human
type = 'human'
else:
seq = seq_loc_fly
type = 'fly'
global_alignment_matrix = student.compute_alignment_matrix(
seq, seq_pax, score_matrix, True)
score, x_glbl, pax_glbl = student.compute_global_alignment(
seq, seq_pax, score_matrix, global_alignment_matrix)
length = len(x_glbl)
agree = 0
for idx in range(length):
if x_glbl[idx] == pax_glbl[idx]:
agree += 1
print 'score:', score, '\n' + type, x_glbl, '\nPAX: ', pax_glbl
print type + ' agree percentage: %.2f' % (100 * float(agree) / length)
"""
Question 2:
human score: 613
human: -HSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLLSEGVCTNDNIPSVSSINRVLRNLASEKQQ
PAX: GHGGVNQLGGVFVNGRPLPDVVRQRIVELAHQGVRPCDISRQLRVSHGCVSKILGRYYETGSIKPGVIGGSKPKVATPKVVEKIAEYKRQNPTMFAWEIRDRLLAERVCDNDTVPSVSSINRIIR--------
human agree percentage: 72.93
flyscore: 586
fly: -HSGVNQLGGVFVGGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATAEVVSKISQYKRECPSIFAWEIRDRLLQENVCTNDNIPSVSSINRVLRNLAAQKEQQ
PAX: GHGGVNQLGGVFVNGRPLPDVVRQRIVELAHQGVRPCDISRQLRVSHGCVSKILGRYYETGSIKPGVIGGSKPKVATPKVVEKIAEYKRQNPTMFAWEIRDRLLAERVCDNDTVPSVSSINRIIR---------
fly agree percentage: 70.15
"""
"""
def build_scoring_matrix(alphabet) :
"""
Make a matrix with diag_score, off_diag_score, dash_score
"""
matrix = dict()
matrix['-'] = dict()
matrix['-']['-'] = 0
for rows in alphabet :
matrix[rows] = dict()
matrix[rows]['-'] = -6
matrix['-'][rows] = -6
for cols in alphabet :
if rows == cols :
matrix[rows][cols] = 10
else :
matrix[rows][cols] = 4
return matrix
m = build_scoring_matrix(chset)
s = student.compute_alignment_matrix("AA", "TAAT", m, False)
print s[0][2]
print s[2][0]
print s[2][2]
print s
ali = student.compute_local_alignment("AA", "TAAT", m, s)
print ali
