def question1():
human = read_protein(HUMAN_EYELESS_URL)
fly = read_protein(FRUITFLY_EYELESS_URL)
print(len(human), len(fly))
scoring = read_scoring_matrix(PAM50_URL)
local_align = compute_alignment_matrix(human, fly, scoring, False)
score, xs, ys = compute_local_alignment(human, fly, scoring, local_align)
print('Question 1')
print(score)
print(xs)
print(ys)
print()
print('Question 2')
consensus = read_protein(CONSENSUS_PAX_URL)
human_nodash = ''.join([x for x in xs if x != '-'])
fly_nodash = ''.join([x for x in ys if x != '-'])
hc_global_align = compute_alignment_matrix(human_nodash, consensus, scoring, True)
fc_global_align = compute_alignment_matrix(fly_nodash, consensus, scoring, True)
hc_agree = agreement(human_nodash, consensus, scoring, hc_global_align)
fc_agree = agreement(fly_nodash, consensus, scoring, fc_global_align)
print('Human vs Consensus agree = %s%%' % hc_agree)
print('Fly vs Consensus agree = %s%%' % fc_agree)
def question4(filename):
human = read_protein(HUMAN_EYELESS_URL)
fly = read_protein(FRUITFLY_EYELESS_URL)
scoring = read_scoring_matrix(PAM50_URL)
distr, raw = generate_null_distribution(human, fly, scoring, 1000)
from pprint import pprint as pp
distr = str_keys(distr)
pp(distr)
distr = norm(distr)
pairs = list(distr.iteritems())
pairs = sorted(pairs, key=itemgetter(0))
print(pairs)
index = np.arange(len(pairs))
plt.bar(index, map(itemgetter(1), pairs))
plt.xticks(index + 0.4, map(itemgetter(0), pairs), fontsize=8)
plt.xlabel('Scores')
plt.ylabel('Fraction of total trials')
plt.title('Distribution of scores')
plt.tight_layout()
plt.savefig(filename)
s_score = 875
n = 1000
mean = sum(raw) / n
std = np.sqrt(sum((x - mean) ** 2 for x in raw) / n)
z_score = (s_score - mean) / std
print('mean = %f' % mean)
print('std = %f' % std)
print('z_score = %f' % z_score)
def question_1():
'''
First, load the files HumanEyelessProtein and FruitflyEyelessProtein using
the provided code. These files contain the amino acid sequences that form
the eyeless proteins in the human and fruit fly genomes, respectively. Then
load the scoring matrix PAM50 for sequences of amino acids. This scoring
matrix is defined over the alphabet {A,R,N,D,C,Q,E,G,H,I,L,K,M,F,P,S,T,W,Y,
V,B,Z,X,-} which represents all possible amino acids and gaps (the "dashes"
in the alignment).
Next, compute the local alignments of the sequences of HumanEyelessProtein
and FruitflyEyelessProtein using the PAM50 scoring matrix and enter the
score and local alignments for these two sequences below. Be sure to
clearly distinguish which alignment is which and include any dashes ('-')
that might appear in the local alignment.
'''
human_protein = provided.read_protein(provided.HUMAN_EYELESS_URL)
fruitfly_protein = provided.read_protein(provided.FRUITFLY_EYELESS_URL)
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
alignment_matrix = project4.compute_alignment_matrix(
human_protein, fruitfly_protein, scoring_matrix, False)
local_alignment = project4.compute_local_alignment(human_protein,
fruitfly_protein,
scoring_matrix,
alignment_matrix)
return local_alignment
def question4(filename):
human = read_protein(HUMAN_EYELESS_URL)
fly = read_protein(FRUITFLY_EYELESS_URL)
scoring = read_scoring_matrix(PAM50_URL)
distr, raw = generate_null_distribution(human, fly, scoring, 1000)
from pprint import pprint as pp
distr = str_keys(distr)
pp(distr)
distr = norm(distr)
pairs = list(distr.iteritems())
pairs = sorted(pairs, key=itemgetter(0))
print(pairs)
index = np.arange(len(pairs))
plt.bar(index, map(itemgetter(1), pairs))
plt.xticks(index + 0.4, map(itemgetter(0), pairs), fontsize=8)
plt.xlabel('Scores')
plt.ylabel('Fraction of total trials')
plt.title('Distribution of scores')
plt.tight_layout()
plt.savefig(filename)
s_score = 875
n = 1000
mean = sum(raw) / n
std = np.sqrt(sum((x - mean)**2 for x in raw) / n)
z_score = (s_score - mean) / std
print('mean = %f' % mean)
print('std = %f' % std)
print('z_score = %f' % z_score)
def question1():
human = read_protein(HUMAN_EYELESS_URL)
fly = read_protein(FRUITFLY_EYELESS_URL)
print(len(human), len(fly))
scoring = read_scoring_matrix(PAM50_URL)
local_align = compute_alignment_matrix(human, fly, scoring, False)
score, xs, ys = compute_local_alignment(human, fly, scoring, local_align)
print('Question 1')
print(score)
print(xs)
print(ys)
print()
print('Question 2')
consensus = read_protein(CONSENSUS_PAX_URL)
human_nodash = ''.join([x for x in xs if x != '-'])
fly_nodash = ''.join([x for x in ys if x != '-'])
hc_global_align = compute_alignment_matrix(human_nodash, consensus,
scoring, True)
fc_global_align = compute_alignment_matrix(fly_nodash, consensus, scoring,
True)
hc_agree = agreement(human_nodash, consensus, scoring, hc_global_align)
fc_agree = agreement(fly_nodash, consensus, scoring, fc_global_align)
print('Human vs Consensus agree = %s%%' % hc_agree)
print('Fly vs Consensus agree = %s%%' % fc_agree)
def question1():
"""
Code for quetion 1
"""
human = read_protein(HUMAN_EYELESS_URL)
fruitfly = read_protein(FRUITFLY_EYELESS_URL)
score_mat = read_scoring_matrix(PAM50_URL)
align_mat = compute_alignment_matrix(human, fruitfly, score_mat, False)
result = compute_local_alignment(human, fruitfly, score_mat, align_mat)
return result
def local_alignment_eyeless_protein():
"""
Question: 1
"""
human_eyeless_seq = provided.read_protein(provided.HUMAN_EYELESS_URL)
fruitfly_eyeless_seq = provided.read_protein(provided.FRUITFLY_EYELESS_URL)
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
alignment_matrix = student.compute_alignment_matrix(human_eyeless_seq, fruitfly_eyeless_seq, scoring_matrix, False)
local_alignment = student.compute_local_alignment(human_eyeless_seq, fruitfly_eyeless_seq, scoring_matrix, alignment_matrix)
return local_alignment
def answer_Q1():
'''
Answers Q1.
'''
# load the acid sequences that form the eyeless proteins for humans genomes
human_sequence = provided.read_protein(HUMAN_EYELESS_URL)
# load the acid sequences that form the eyeless proteins for fruit flies genomes
fly_sequence = provided.read_protein(FRUITFLY_EYELESS_URL)
# load the PAM50 scoring matrix
pam50_scoring_matrix = provided.read_scoring_matrix(PAM50_URL)
# compute the alignment method using method Q12
alignment_matrix = student.compute_alignment_matrix(human_sequence, fly_sequence, pam50_scoring_matrix, False)
return student.compute_local_alignment(human_sequence, fly_sequence, pam50_scoring_matrix, alignment_matrix)
def local_alignment_eyeless_protein():
"""
Question: 1
"""
human_eyeless_seq = provided.read_protein(provided.HUMAN_EYELESS_URL)
fruitfly_eyeless_seq = provided.read_protein(provided.FRUITFLY_EYELESS_URL)
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
alignment_matrix = student.compute_alignment_matrix(
human_eyeless_seq, fruitfly_eyeless_seq, scoring_matrix, False)
local_alignment = student.compute_local_alignment(human_eyeless_seq,
fruitfly_eyeless_seq,
scoring_matrix,
alignment_matrix)
return local_alignment
def run_ques_4():
"""
Question: 4 & 5
"""
seq_x = provided.read_protein(provided.HUMAN_EYELESS_URL)
seq_y = provided.read_protein(provided.FRUITFLY_EYELESS_URL)
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
num_trials = 1000
scoring_distribution = generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials)
mean, std_dev = compute_stats(scoring_distribution, num_trials)
print mean, ",", std_dev
z_score = float(local_alignment_eyeless_protein()[0] - mean) / std_dev
print z_score
bar_plot(scoring_distribution, num_trials)
def perform_human_fly_trials():
# load the acid sequences that form the eyeless proteins for humans genomes
human_sequence = provided.read_protein(HUMAN_EYELESS_URL)
# load the acid sequences that form the eyeless proteins for fruit flies genomes
fly_sequence = provided.read_protein(FRUITFLY_EYELESS_URL)
# load the PAM50 scoring matrix
pam50_scoring_matrix = provided.read_scoring_matrix(PAM50_URL)
# perform 1000 trials
scoring_distribution = generate_null_distribution(human_sequence, fly_sequence, pam50_scoring_matrix, 1000)
# conver result to pd dataframe
scoring_dist_df = pd.DataFrame(scoring_distribution.values(), index=scoring_distribution.keys(), columns=['Frequency'])
# fix index name
scoring_dist_df.index.rename('Scores', inplace=True)
return scoring_dist_df
def run_app_q1():
"""
Question 1 of application.
"""
scoring_matrix = provided.read_scoring_matrix(PAM50_PATH)
human_eyeless_protein = provided.read_protein(HUMAN_EYELESS_PATH)
fruitfly_eyeless_protein = provided.read_protein(FRUITFLY_EYELESS_PATH)
alignment_matrix = compute_alignment_matrix(human_eyeless_protein,
fruitfly_eyeless_protein,
scoring_matrix, False)
(score, local_human, local_fruitfly) = compute_local_alignment(
human_eyeless_protein, fruitfly_eyeless_protein, scoring_matrix,
alignment_matrix)
return (score, local_human, local_fruitfly)
def question2():
"""
Code for question 2
"""
q1_result = question1()
score_mat = read_scoring_matrix(PAM50_URL)
human, fruitfly = q1_result[1], q1_result[2]
human = human.replace('-', '')
fruitfly = fruitfly.replace('-', '')
consensus = read_protein(CONSENSUS_PAX_URL)
align_m_h = compute_alignment_matrix(human, consensus, score_mat, True)
align_m_f = compute_alignment_matrix(fruitfly, consensus, score_mat, True)
global_align_hc = compute_global_alignment(human, consensus,
score_mat, align_m_h)
global_h, global_ch = global_align_hc[1], global_align_hc[2]
per1, per2 = 0, 0
for idx in range(len(global_h)):
if global_h[idx] == global_ch[idx]:
per1 += 1
print float(per1) / len(global_h) * 100
global_align_fc = compute_global_alignment(fruitfly, consensus,
score_mat, align_m_f)
global_f, global_cf = global_align_fc[1], global_align_fc[2]
for idx in range(len(global_f)):
if global_f[idx] == global_cf[idx]:
per2 += 1
print float(per2) / len(global_f) * 100
def question5():
"""
Code for question 5
"""
human = read_protein(HUMAN_EYELESS_URL)
fruitfly = read_protein(FRUITFLY_EYELESS_URL)
score_mat = read_scoring_matrix(PAM50_URL)
dist = generate_null_distribution(human, fruitfly, score_mat, 1000)
scores = []
for score, count in dist.iteritems():
scores.extend([score] * count)
N = len(scores)
mean = float(sum(scores)) / N
std = math.sqrt(float(sum([(score - mean) ** 2 for score in scores])) / N)
z_score = (875 - mean) / std
print mean, std, z_score
def run_ques_4():
"""
Question: 4 & 5
"""
seq_x = provided.read_protein(provided.HUMAN_EYELESS_URL)
seq_y = provided.read_protein(provided.FRUITFLY_EYELESS_URL)
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
num_trials = 1000
scoring_distribution = generate_null_distribution(seq_x, seq_y,
scoring_matrix,
num_trials)
mean, std_dev = compute_stats(scoring_distribution, num_trials)
print mean, ",", std_dev
z_score = float(local_alignment_eyeless_protein()[0] - mean) / std_dev
print z_score
bar_plot(scoring_distribution, num_trials)
def question4_plot():
"""
Code for question 4
"""
human = read_protein(HUMAN_EYELESS_URL)
fruitfly = read_protein(FRUITFLY_EYELESS_URL)
score_mat = read_scoring_matrix(PAM50_URL)
dist = generate_null_distribution(human, fruitfly, score_mat, 1000)
y = []
for count in dist.itervalues():
y.append(count / 1000.0)
plt.bar(dist.keys(), y)
plt.title("Normalized score distribution")
plt.ylabel("Fractions of total trials")
plt.xlabel("Scores of local alignments")
plt.show()
print dist
def run_app_q4():
"""
Question 4 of application.
"""
scoring_matrix = provided.read_scoring_matrix(PAM50_PATH)
human_eyeless_protein = provided.read_protein(HUMAN_EYELESS_PATH)
fruitfly_eyeless_protein = provided.read_protein(FRUITFLY_EYELESS_PATH)
num_trials = 1000
scoring_distribution = generate_null_distribution(
human_eyeless_protein, fruitfly_eyeless_protein, scoring_matrix,
num_trials)
for score in scoring_distribution.keys():
scoring_distribution[score] /= (1.0 * num_trials)
plt.bar(scoring_distribution.keys(),
scoring_distribution.values(),
color='g')
plt.grid(True)
plt.xlabel('Scores')
plt.ylabel('Fraction of Total Trials')
plt.title('Normalized Scoring Distribution')
plt.show()
return scoring_distribution
def run_app_q3():
"""
Question 3 of application.
"""
scoring_matrix = provided.read_scoring_matrix(PAM50_PATH)
human_eyeless_protein = provided.read_protein(HUMAN_EYELESS_PATH)
fruitfly_eyeless_protein = provided.read_protein(FRUITFLY_EYELESS_PATH)
human_rand = gen_random_seqs(len(human_eyeless_protein))
fruitfly_rand = gen_random_seqs(len(fruitfly_eyeless_protein))
alignment_matrix = compute_alignment_matrix(human_rand, fruitfly_rand,
scoring_matrix, False)
(score, local_human_rand,
local_fruitfly_rand) = compute_local_alignment(human_rand, fruitfly_rand,
scoring_matrix,
alignment_matrix)
print score
print local_human_rand
print local_fruitfly_rand
consensus_pax_domain = provided.read_protein(CONSENSUS_PAX_PATH)
local_human_rand = remove_dash(local_human_rand)
alignment_matrix = compute_alignment_matrix(local_human_rand,
consensus_pax_domain,
scoring_matrix, True)
(score, global_human_rand, global_consensus) = compute_global_alignment(
local_human_rand, consensus_pax_domain, scoring_matrix,
alignment_matrix)
human_match = count_match_percentage(global_human_rand, global_consensus)
print human_match
local_fruitfly_rand = remove_dash(local_fruitfly_rand)
alignment_matrix = compute_alignment_matrix(local_fruitfly_rand,
consensus_pax_domain,
scoring_matrix, True)
(score, global_fruitfly_rand, global_consensus) = compute_global_alignment(
local_fruitfly_rand, consensus_pax_domain, scoring_matrix,
alignment_matrix)
fruitfly_match = count_match_percentage(global_fruitfly_rand,
global_consensus)
print fruitfly_match
def question_4():
'''
We will take an approach known as statistical hypothesis testing to
determine whether the local alignments computed in Question 1 are
statistically significant (that is, that the probability that they could
have arisen by chance is extremely small).
'''
# Use the function generate_null_distribution to create a distribution with
# 1000 trials using the protein sequences HumanEyelessProtein and
# FruitflyEyelessProtein (using the PAM50 scoring matrix).
human_protein = provided.read_protein(provided.HUMAN_EYELESS_URL)
fruitfly_protein = provided.read_protein(provided.FRUITFLY_EYELESS_URL)
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
num_trials = 1000
distribution = generate_null_distribution(human_protein, fruitfly_protein,
scoring_matrix, num_trials)
# Next, create a bar plot of the normalized version of this distribution.
# The horizontal axis should be the scores and the vertical axis should be
# the fraction of total trials corresponding to each score. As usual,
# choose reasonable labels for the axes and title.
normalized_dist = {}
for score in distribution:
normalized_dist[score] = float(distribution[score]) / num_trials
plt.bar(normalized_dist.keys(), normalized_dist.values())
plt.title('Null Distribution for Hypothesis Testing using 1000 Trials')
plt.xlabel('Local Alignment Scores')
plt.ylabel('Fraction of Total Trials')
plt.show()
return distribution
def run_app_q2():
"""
Question 2 of application.
"""
scoring_matrix = provided.read_scoring_matrix(PAM50_PATH)
(score, local_human, local_fruitfly) = run_app_q1()
consensus_pax_domain = provided.read_protein(CONSENSUS_PAX_PATH)
# local_human = remove_dash(local_human)
local_fruitfly = remove_dash(local_fruitfly)
# alignment_matrix = compute_alignment_matrix(local_human, consensus_pax_domain, scoring_matrix, True)
# (score, global_human, global_consensus) = compute_global_alignment(local_human, consensus_pax_domain, scoring_matrix, alignment_matrix)
# human_match = count_match_percentage(global_human, global_consensus)
alignment_matrix = compute_alignment_matrix(local_fruitfly,
consensus_pax_domain,
scoring_matrix, True)
(score, global_fruitfly, global_consensus) = compute_global_alignment(
local_fruitfly, consensus_pax_domain, scoring_matrix, alignment_matrix)
fruitfly_match = count_match_percentage(global_fruitfly, global_consensus)
print fruitfly_match
def global_alignment_consensus():
"""
Question: 2
"""
ans_similar = []
local_alignments = local_alignment_eyeless_protein()
consensus_seq = provided.read_protein(provided.CONSENSUS_PAX_URL)
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
for idx in range(1, 3):
seq_x = local_alignments[idx]
seq_x = seq_x.replace("-", "")
alignment_matrix = student.compute_alignment_matrix(seq_x, consensus_seq, scoring_matrix, True)
global_alignment = student.compute_global_alignment(seq_x, consensus_seq, scoring_matrix, alignment_matrix)
similar_count = 0
for letter1, letter2 in zip(global_alignment[1], global_alignment[2]):
if letter1 == letter2:
similar_count += 1
ans_similar.append(float(similar_count * 100) / len(global_alignment[1]))
return ans_similar
def question_2():
'''
To continue our investigation, we next consider the similarity of the two
sequences in the local alignment computed in Question 1 to a third
sequence. The file ConsensusPAXDomain contains a "consensus" sequence of
the PAX domain; that is, the sequence of amino acids in the PAX domain in
any organism. In this problem, we will compare each of the two sequences of
the local alignment computed in Question 1 to this consensus sequence to
determine whether they correspond to the PAX domain.
'''
consensus = provided.read_protein(provided.CONSENSUS_PAX_URL)
score, human_alignment, fruitfly_alignment = question_1()
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
# Delete any dashes '-' present in the sequence
human = human_alignment.replace('-', '')
fruitfly = fruitfly_alignment.replace('-', '')
# Compute the global alignment of this dash-less sequence with the
# ConsensusPAXDomain sequence.
alignment_matrix_human = project4.compute_alignment_matrix(
human, consensus, scoring_matrix, True)
human_global = project4.compute_global_alignment(human, consensus,
scoring_matrix,
alignment_matrix_human)
alignment_matrix_fruitfly = project4.compute_alignment_matrix(
fruitfly, consensus, scoring_matrix, True)
fruitfly_global = project4.compute_global_alignment(
fruitfly, consensus, scoring_matrix, alignment_matrix_fruitfly)
# Compare corresponding elements of these two globally-aligned sequences
# (local vs. consensus) and compute the percentage of elements in these two
# sequences that agree.
human_similarity = compute_similarity(human_global[1], human_global[2])
fruitfly_similarity = compute_similarity(fruitfly_global[1],
fruitfly_global[2])
return 'Human:', human_similarity, 'Fruitfly:', fruitfly_similarity
def global_alignment_consensus():
"""
Question: 2
"""
ans_similar = []
local_alignments = local_alignment_eyeless_protein()
consensus_seq = provided.read_protein(provided.CONSENSUS_PAX_URL)
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
for idx in range(1, 3):
seq_x = local_alignments[idx]
seq_x = seq_x.replace("-", "")
alignment_matrix = student.compute_alignment_matrix(
seq_x, consensus_seq, scoring_matrix, True)
global_alignment = student.compute_global_alignment(
seq_x, consensus_seq, scoring_matrix, alignment_matrix)
similar_count = 0
for letter1, letter2 in zip(global_alignment[1], global_alignment[2]):
if letter1 == letter2:
similar_count += 1
ans_similar.append(
float(similar_count * 100) / len(global_alignment[1]))
return ans_similar
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)
""" Author: Ko-Shin Chen Algorithmic Thinking (Part 2) Application 4: Applications to Genomics and Beyond """ import Project_4 import alg_application4_provided as provided import math import matplotlib.pyplot as plt """ Question 1 """ seq_human = provided.read_protein(provided.HUMAN_EYELESS_URL) seq_fly = provided.read_protein(provided.FRUITFLY_EYELESS_URL) scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL) local_alignment_mx = Project_4.compute_alignment_matrix(seq_human, seq_fly, scoring_matrix, False) result = Project_4.compute_local_alignment(seq_human, seq_fly, scoring_matrix, local_alignment_mx) print 'Score:' + str(result[0]) print 'Human: ' + result[1] print 'Fly: ' + result[2] """ Question 2 """
"""
Algorithm thinking application 4-2
data: 2015/07/30
Author: You-Hao
"""
import alg_application4_provided as app4
import AT_project_4 as pj4
scoring_matrix = app4.read_scoring_matrix(app4.PAM50_URL)
seq_PAX = app4.read_protein(app4.CONSENSUS_PAX_URL)
seq_human = 'HSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLLSEGVCTNDNIPSVSSINRVLRNLASEK-QQ'
seq_fruitfly = 'HSGVNQLGGVFVGGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATAEVVSKISQYKRECPSIFAWEIRDRLLQENVCTNDNIPSVSSINRVLRNLAAQKEQQ'
seq_human_nodash = ''
seq_fruitfly_nodash = ''
for char in seq_human:
if char != '-':
seq_human_nodash = seq_human_nodash + char
for char in seq_fruitfly:
if char != '-':
seq_fruitfly_nodash = seq_fruitfly_nodash + char
print len(seq_human_nodash)
print len(seq_fruitfly_nodash)
# for human
"""
Application 4 scripts
"""
import Project4 as help
import alg_application4_provided as provided
import random
seq_x = provided.read_protein(provided.HUMAN_EYELESS_URL)
seq_y = provided.read_protein(provided.FRUITFLY_EYELESS_URL)
score_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
"""
# Question1
local_align_matrix = help.compute_alignment_matrix(seq_x, seq_y, score_matrix, False)
score, align_x, align_y = help.compute_local_alignment(seq_x, seq_y, score_matrix, local_align_matrix)
# Question2
new_seq_x = align_x.rstrip("-QQ")
new_seq_x = new_seq_x + "QQ"
new_seq_y = align_y
seq_consensus = provided.read_protein(provided.CONSENSUS_PAX_URL)
global_matrix_x = help.compute_alignment_matrix(new_seq_x, seq_consensus, score_matrix, True)
score_x_consensus, align_x1, align_y1 = help.compute_global_alignment(new_seq_x, seq_consensus, score_matrix, global_matrix_x)
global_matrix_y = help.compute_alignment_matrix(new_seq_y, seq_consensus, score_matrix, True)
score_y_consensus, align_x2, align_y2 = help.compute_global_alignment(new_seq_y, seq_consensus, score_matrix, global_matrix_y)
# Question3
alphabets = "ACBEDGFIHKMLNQPSRTWVYXZ"
seq1 = ""
seq2 = ""
for dummy_num in range(len(seq_x)):
for dummy_idx in range(num_trials):
tmp_y = list(seq_y)
random.shuffle(tmp_y)
rand_y = ''.join(tmp_y)
alignment_matrix = pj4.compute_alignment_matrix(seq_x, rand_y, scoring_matrix, False)
score = max([max(value) for value in alignment_matrix])
#score, align_x, align_y = pj4.compute_local_alignment(seq_x, rand_y, scoring_matrix, alignment_matrix)
if score not in scoring_distribution.keys():
scoring_distribution[score] = 1
else:
scoring_distribution[score] += 1
return scoring_distribution
protein_human = app4.read_protein(app4.HUMAN_EYELESS_URL)
protein_fruitfly = app4.read_protein(app4.FRUITFLY_EYELESS_URL)
scoring_matrix = app4.read_scoring_matrix(app4.PAM50_URL)
scoring_distribution = generate_null_distribution(protein_human, protein_fruitfly, scoring_matrix, 1000)
#scoring_distribution = {38: 1, 39: 1, 40: 8, 41: 9, 42: 28, 43: 35, 44: 50, 45: 46, 46: 49, 47: 57, 48: 63, 49: 62, 50: 72, 51: 56, 52: 56, 53: 61, 54: 62, 55: 32, 56: 25, 57: 33, 58: 29, 59: 22, 60: 25, 61: 15, 62: 13, 63: 10, 64: 13, 65: 20, 66: 2, 67: 4, 68: 14, 69: 5, 70: 3, 71: 2, 72: 3, 74: 2, 75: 2, 76: 1, 77: 1, 79: 2, 81: 2, 84: 1, 85: 1, 94: 1, 97: 1}
print scoring_distribution
x_value = scoring_distribution.keys()
print x_value
x_value.sort()
print x_value
y_value = []
for score in x_value:
y_value.append(scoring_distribution[score] / float(1000) * 100)
"""
Author: Tejaswini Dhupad
Algorithmic Thinking (Part 2)
Application 4: Applications to Genomics and Beyond
"""
import Project_4
import alg_application4_provided as provided
import math
import matplotlib.pyplot as plt
"""
Question 1
"""
seq_human = provided.read_protein(provided.HUMAN_EYELESS_URL)
seq_fly = provided.read_protein(provided.FRUITFLY_EYELESS_URL)
scoring_matrix = provided.read_scoring_matrix(provided.PAM50_URL)
local_alignment_mx = Project_4.compute_alignment_matrix(
seq_human, seq_fly, scoring_matrix, False)
result = Project_4.compute_local_alignment(seq_human, seq_fly, scoring_matrix,
local_alignment_mx)
print 'Score:' + str(result[0])
print 'Human: ' + result[1]
print 'Fly: ' + result[2]
"""
Question 2
"""
ali_human = result[1]
ali_fly = result[2]
seq_con = provided.read_protein(provided.CONSENSUS_PAX_URL)