def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
"""
return a dictionary scoring_distribution that represents an un-normalized distribution generated by performing the
following process num_trials times
- Generate a random permutation rand_y of the sequence seq_y using random.shuffle().
- Compute the maximum value score for the local alignment of seq_x and rand_y using the score matrix scoring_matrix.
- Increment the entry score in the dictionary scoring_distribution by one.
:rtype : dict
:param seq_x: first input sequence
:param seq_y: second input sequence
:param scoring_matrix: scoring matrix
:param num_trials: number of trials
:return: dictionary scoring_distribution
"""
scoring_distribution = {}
for idx in range(num_trials):
rand_y = list(seq_y)
random.shuffle(rand_y)
rand_y = "".join(rand_y)
a_matrix = soln.compute_alignment_matrix(seq_x, rand_y, scoring_matrix, False)
score, dummy1, dummy2 = soln.compute_local_alignment(seq_x, rand_y, scoring_matrix, a_matrix)
if score in scoring_distribution:
scoring_distribution[score] += 1
else:
scoring_distribution[score] = 1
if (idx + 1) % (num_trials / 20) == 0:
print "done: ", idx
return scoring_distribution
def q2():
def q2_helper(str1, str2):
if len(str1) != len(str2):
return 0
else:
ctr = 0
for char in range(len(str1)):
if str1[char] == str2[char]:
ctr += 1
return (ctr * 1.0 / len(str1)) * 100
human = data.read_protein(data.HUMAN_EYELESS_URL)
fly = data.read_protein(data.FRUITFLY_EYELESS_URL)
scores = data.read_scoring_matrix(data.PAM50_URL)
c_pax = data.read_protein(data.CONSENSUS_PAX_URL)
# get local alignment of human and fly
a_matrix = soln.compute_alignment_matrix(human, fly, scores, False)
l_score, l_h, l_ff = soln.compute_local_alignment(human, fly, scores, a_matrix)
# removing the dashes
l_h = l_h.replace("-", "")
l_ff = l_ff.replace("-", "")
# get global alignment matrix for each local string and pax
pax_a_h_matrix = soln.compute_alignment_matrix(l_h, c_pax, scores, True)
pax_a_ff_matrix = soln.compute_alignment_matrix(l_ff, c_pax, scores, True)
# compute global alignment
h_ga = soln.compute_global_alignment(l_h, c_pax, scores, pax_a_h_matrix)
ff_ga = soln.compute_global_alignment(l_ff, c_pax, scores, pax_a_ff_matrix)
print "human:\t\t", q2_helper(h_ga[1], h_ga[2])
print "fruit fly:\t", q2_helper(ff_ga[1], ff_ga[2])
def analyze_data():
global human_protein, fruitfly_protein, alignment, scoring_matrix
human_protein = read_protein(HUMAN_EYELESS_URL)
fruitfly_protein = read_protein(FRUITFLY_EYELESS_URL)
scoring_matrix = read_scoring_matrix(PAM50_URL)
alignment_matrix = p.compute_alignment_matrix(human_protein,
fruitfly_protein,
scoring_matrix, False)
alignment = p.compute_local_alignment(human_protein, fruitfly_protein,
scoring_matrix, alignment_matrix)
def q1():
human = data.read_protein(data.HUMAN_EYELESS_URL)
fly = data.read_protein(data.FRUITFLY_EYELESS_URL)
scores = data.read_scoring_matrix(data.PAM50_URL)
a_matrix = soln.compute_alignment_matrix(human, fly, scores, False)
print soln.compute_local_alignment(human, fly, scores, a_matrix)
a_matrix = soln.compute_alignment_matrix(human, fly, scores, True)
print soln.compute_global_alignment(human, fly, scores, a_matrix)
# local answer
b = (875,
'HSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLLSEGVCTNDNIPSVSSINRVLRNLASEK-QQ',
'HSGVNQLGGVFVGGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATAEVVSKISQYKRECPSIFAWEIRDRLLQENVCTNDNIPSVSSINRVLRNLAAQKEQQ')
# global answer
a = (4,
'MQN--------------------------------------S--------------HSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLLSEGVCTNDNIPSVSSINRVLRNLASEK-QQ--------------------------------------------M------------GA----DG-----MYDKLRMLN-------G--Q----T---G-S---WGTR---P----G------------W----YPG----T--------------SV---------------P---------G-Q---P--T-------Q-DGCQQ-QE-G-G-GENTNSISSN-GEDSDEAQMRLQLKRKLQRNRTSFTQEQIEALEKEFERTHYPDVFARERLAAKIDLPEARIQVWFSNRRAKWRREEKLRNQRR--Q-----A-----S---N-T--P------SH-I------P----I---SS-S-FSTSVYQP-----I--PQ-PT-TP-V-SSFTSGSMLGR-T-D-----T--AL-T----NT-Y--S-------AL-P---P-M---P-SF-TM-AN--N--LPM-Q------P-P------V-----PS----Q---T-SS-YSC-M-L---PTSPS----V--N-GR--------------------S-YD--T-YT--PPHM------Q-------------T--H-M--NS-Q-P-MGTS--GTT-STGL----ISPGV-S---V----P--VQ-V-P----G-S---EPDMSQ------YWPRLQ',
'MRNLPCLGTAGGSGLGGIAGKPSPTMEAVEASTASHPHSTSSYFATTYYHLTDDECHSGVNQLGGVFVGGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATAEVVSKISQYKRECPSIFAWEIRDRLLQENVCTNDNIPSVSSINRVLRNLAAQKEQQSTGSGSSSTSAGNSISAKVSVSIGGNVSNVASGSRGTLSSSTDLMQTATPLNSSESGGASNSGEGSEQEAIYEKLRLLNTQHAAGPGPLEPARAAPLVGQSPNHLGTRSSHPQLVHGNHQALQQHQQQSWPPRHYSGSWYPTSLSEIPISSAPNIASVTAYASGPSLAHSLSPPNDIESLASIGHQRNCPVATEDIHLKKELDG-HQSDETGSGEGENSNGGASNIG-NTEDDQARLILKRKLQRNRTSFTNDQIDSLEKEFERTHYPDVFARERLAGKIGLPEARIQVWFSNRRAKWRREEKLRNQRRTPNSTGASATSSSTSATASLTDSPNSLSACSSLLSGSAGGPSVSTINGLSSPSTLSTNVNAPTLGAGIDSSESPTPIPHIRPSCTSDNDNGRQSEDCRRVCSPCPLGVGGHQNTHHIQSNGHAQGHALVPAISPRLNFNSGSFGAMYSNMHHTALSMSDSYGAVTPIPSFNHSAVGPLAPPSPIPQQGDLTPSSLYPCHMTLRPPPMAPAHHHIVPGDGGRPAGVGLGSGQSANLGASCSGSGYEVLSAYALPPPPMASSSAADSSFSAASSASANVTPHHTIAQESCPSPCSSASHFGVAHSSGFSSDPISPAVSSYAHMSYNYASSANTMTPSSASGTSAHVAPGKQQFFASCFYSPWV-')
def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
"""
Takes as input two sequences seq_x and
seq_y, a scoring matrix scoring_matrix, and a number of trials num_trials. This function should return a dictionary
scoring_distribution that represents an un-normalized distribution
generated by performing the following process num_trials times:
Generate a random permutation rand_y of the sequence seq_y using random.shuffle().
Compute the maximum value score for the local alignment of seq_x and rand_y using the score matrix scoring_matrix.
Increment the entry score in the dictionary scoring_distribution by one.
"""
scoring_distribution = {}
for dummy in range(num_trials):
list_y = list(seq_y)
random.shuffle(list_y)
rand_y = ''.join(list_y)
alignment_matrix = p.compute_alignment_matrix(seq_x, rand_y,
scoring_matrix, False)
score = p.compute_local_alignment(seq_x, rand_y, scoring_matrix,
alignment_matrix)[0]
scoring_distribution[score] = scoring_distribution.get(score, 0) + 1
return scoring_distribution
def q5():
# calculate mean/standard deviation
dist2 = {38: 3, 40: 7, 41: 13, 42: 23, 43: 27, 44: 29, 45: 45, 46: 74, 47: 69, 48: 72, 49: 71, 50: 65, 51: 52,
52: 64, 53: 57, 54: 49, 55: 43, 56: 28, 57: 40, 58: 31, 59: 17, 60: 18, 61: 14, 62: 12, 63: 9, 64: 9,
65: 4, 66: 10, 67: 8, 68: 5, 69: 6, 70: 2, 71: 3, 72: 4, 73: 1, 75: 3, 77: 4, 79: 2, 80: 1, 81: 1, 82: 2,
84: 1, 85: 1, 93: 1}
avg = numpy.mean(dist2.keys())
std_d = numpy.std(dist2.keys())
print "mean:", avg
print "standard deviation: ", std_d
# get local scores
human = data.read_protein(data.HUMAN_EYELESS_URL)
fly = data.read_protein(data.FRUITFLY_EYELESS_URL)
scores = data.read_scoring_matrix(data.PAM50_URL)
# get local alignment of human and fly
a_matrix = soln.compute_alignment_matrix(human, fly, scores, False)
l_score, l_h, l_ff = soln.compute_local_alignment(human, fly, scores, a_matrix)
print "local score: ", l_score
print "z score: ", (l_score - avg) / std_d
