def test_dataset_06(self):
pattern = 'CCA'
dna = 'CCACCT'
d = 0
matching_positions = approximate_pattern_matching(pattern, dna, d)
expected = '0'
self.assertEqual(expected, ToSingleLineOfString(matching_positions))
def test_dataset_04(self):
pattern = 'CCGTCATCC'
dna = 'CCGTCATCCGTCATCCTCGCCACGTTGGCATGCATTCCGTCATCCCGTCAGGCATACTTCTGCATATAAGTACAAACATCCGTCATGTCAAAGGGAGCCCGCAGCGGTAAAACCGAGAACCATGATGAATGCACGGCGATTGC'
d = 3
matching_positions = approximate_pattern_matching(pattern, dna, d)
expected = '0 7 36 44 48 72 79 112'
self.assertEqual(expected, ToSingleLineOfString(matching_positions))
def test_dataset_02(self):
pattern = 'GAGCGCTGG'
dna = 'GAGCGCTGGGTTAACTCGCTACTTCCCGACGAGCGCTGTGGCGCAAATTGGCGATGAAACTGCAGAGAGAACTGGTCATCCAACTGAATTCTCCCCGCTATCGCATTTTGATGCGCGCCGCGTCGATT'
d = 2
matching_positions = approximate_pattern_matching(pattern, dna, d)
expected = '0 30 66'
self.assertEqual(expected, ToSingleLineOfString(matching_positions))
def test_dataset_03(self):
pattern = 'AATCCTTTCA'
dna = 'CCAAATCCCCTCATGGCATGCATTCCCGCAGTATTTAATCCTTTCATTCTGCATATAAGTAGTGAAGGTATAGAAACCCGTTCAAGCCCGCAGCGGTAAAACCGAGAACCATGATGAATGCACGGCGATTGCGCCATAATCCAAACA'
d = 3
matching_positions = approximate_pattern_matching(pattern, dna, d)
expected = '3 36 74 137'
self.assertEqual(expected, ToSingleLineOfString(matching_positions))
def test_sample(self):
pattern = 'ATTCTGGA'
dna = 'CGCCCGAATCCAGAACGCATTCCCATATTTCGGGACCACTGGCCTCCACGGTACGGACGTCAATCAAAT'
d = 3
matching_positions = approximate_pattern_matching(pattern, dna, d)
expected = '6 7 26 27'
self.assertEqual(expected, ToSingleLineOfString(matching_positions))
def test_dataset_01(self):
pattern = 'AAA'
dna = 'TTTTTTAAATTTTAAATTTTTT'
d = 2
matching_positions = approximate_pattern_matching(pattern, dna, d)
expected = '4 5 6 7 8 11 12 13 14 15'
self.assertEqual(expected, ToSingleLineOfString(matching_positions))
def test_dataset_05(self):
pattern = 'TTT'
dna = 'AAAAAA'
d = 3
matching_positions = approximate_pattern_matching(pattern, dna, d)
expected = '0 1 2 3'
self.assertEqual(expected, ToSingleLineOfString(matching_positions))
def test_dataset_02(self):
"""
This dataset checks to see if your code is missing the last symbol of Genome.
"""
dna = 'ACCC'
min_skew_indices = get_minimum_skews(dna)
expected = '4'
self.assertEqual(expected, ToSingleLineOfString(min_skew_indices))
def approximate_pattern_matching_problem():
with open('Datasets/ApproximatePatternMatchingProblem_data02.txt',
'r') as datafile:
pattern = datafile.readline().strip()
dna = datafile.readline().strip()
d = int(datafile.readline().strip())
matching_positions = approximate_pattern_matching(pattern, dna, d)
print(ToSingleLineOfString(matching_positions))
def test_dataset_03(self):
"""
This dataset makes sure you are not accidentally finding the maximum skew instead of the minimum skew.
"""
dna = 'CCGGGT'
min_skew_indices = get_minimum_skews(dna)
expected = '2'
self.assertEqual(expected, ToSingleLineOfString(min_skew_indices))
def test_dataset_04(self):
"""
First, this dataset checks if you are only finding 1 index (and not multiple indices).
Then, it checks if you are using a delimiter to separate your indices (ideally a space character).
"""
dna = 'CCGGCCGG'
min_skew_indices = get_minimum_skews(dna)
expected = '2 6'
self.assertEqual(expected, ToSingleLineOfString(min_skew_indices))
def frequent_words_with_mismatches_problem():
with open('Datasets/FrequentWordsWithMismatchesProblem_data02.txt',
'r') as datafile:
dna = datafile.readline().strip()
params = datafile.readline().strip().split(' ')
k = int(params[0])
d = int(params[1])
words = frequent_words_with_mismatches(dna, k, d)
print(ToSingleLineOfString(words))
def test_sample(self):
dna = 'ACGTTGCATGTCGCATGATGCATGAGAGCT'
k = 4
d = 1
words = frequent_words_with_mismatches(dna, k, d)
expected = 'GATG ATGC ATGT'
passed = ResultEqual(expected, words)
if not passed:
self.assertEqual(expected, ToSingleLineOfString(words))
def test_dataset_05(self):
dna = 'AAT'
k = 3
d = 0
words = frequent_words_with_mismatches(dna, k, d)
expected = 'AAT'
passed = ResultEqual(expected, words)
if not passed:
self.assertEqual(expected, ToSingleLineOfString(words))
def test_dataset_06(self):
dna = 'TAGCG'
k = 2
d = 1
words = frequent_words_with_mismatches(dna, k, d)
expected = 'GG TG'
passed = ResultEqual(expected, words)
if not passed:
self.assertEqual(expected, ToSingleLineOfString(words))
def test_dataset_04(self):
dna = 'ATA'
k = 3
d = 1
words = frequent_words_with_mismatches(dna, k, d)
expected = 'GTA ACA AAA ATC ATA AGA ATT CTA TTA ATG'
passed = ResultEqual(expected, words)
if not passed:
self.assertEqual(expected, ToSingleLineOfString(words))
def test_dataset_01(self):
"""
This dataset checks if your code's indexing is off.
Specifically, it verifies that your code is not returning an index 1 too high (i.e. 4) or 1 too low (i.e. 2).
"""
dna = 'ACCG'
min_skew_indices = get_minimum_skews(dna)
expected = '3'
self.assertEqual(expected, ToSingleLineOfString(min_skew_indices))
def test_dataset_02(self):
dna = 'AGTCAGTC'
k = 4
d = 2
words = frequent_words_with_mismatches(dna, k, d)
expected = 'TCTC CGGC AAGC TGTG GGCC AGGT ATCC ACTG ACAC AGAG ATTA TGAC AATT CGTT GTTC GGTA AGCA CATC'
passed = ResultEqual(expected, words)
if not passed:
self.assertEqual(expected, ToSingleLineOfString(words))
def test_dataset_04(self):
dna = 'ATA'
k = 3
d = 1
words = frequent_words_with_mismatches_and_reverse_complements(
dna, k, d)
expected = 'AAA AAT ACA AGA ATA ATC ATG ATT CAT CTA GAT GTA TAA TAC TAG TAT TCT TGT TTA TTT'
passed = ResultEqual(expected, words)
if not passed:
self.assertEqual(expected, ToSingleLineOfString(words))
def test_dataset_03(self):
dna = 'AATTAATTGGTAGGTAGGTA'
k = 4
d = 0
words = frequent_words_with_mismatches_and_reverse_complements(
dna, k, d)
expected = 'AATT'
passed = ResultEqual(expected, words)
if not passed:
self.assertEqual(expected, ToSingleLineOfString(words))
def test_extra_dataset(self):
with open('Datasets/ApproximatePatternMatchingProblem_data01.txt',
'r') as datafile:
datafile.readline()
pattern = datafile.readline().strip()
dna = datafile.readline().strip()
d = int(datafile.readline().strip())
datafile.readline()
expected = datafile.readline().strip()
matching_positions = approximate_pattern_matching(pattern, dna, d)
self.assertEqual(expected, ToSingleLineOfString(matching_positions))
def motif_enumeration_problem():
k = 5
d = 1
dnas = [
"CGTTACGGAAGTTAAGTATGGGTCG", "CGAAAAAGCCTCAGCTTAAACCCAA",
"GCGTTCTCACCTATACGAAAAGGAA", "CGGAACTTCGAAAGACTATAGGTGT",
"TTTCGTCATTCGTAAGGTGACCTCT", "CGCAATGGTGTTCATAAGCGCTTTT"
]
motifs = motif_enumeration(dnas, k, d)
print(ToSingleLineOfString(motifs))
def test_extra_dataset(self):
with open('Datasets/FrequentWordsWithMismatchesProblem_data01.txt',
'r') as datafile:
datafile.readline()
dna = datafile.readline().strip()
params = datafile.readline().strip().split(' ')
k = int(params[0])
d = int(params[1])
datafile.readline()
expected = datafile.readline().strip()
words = frequent_words_with_mismatches(dna, k, d)
passed = ResultEqual(expected, words)
if not passed:
self.assertEqual(expected, ToSingleLineOfString(words))
def minimum_skew_problem():
with open('Datasets/MinimumSkewProblem_data02.txt', 'r') as datafile:
dna = datafile.readline().strip()
min_skew_indices = get_minimum_skews(dna)
print(ToSingleLineOfString(min_skew_indices))
def q3():
print(
ToSingleLineOfString(
get_minimum_skews('CATTCCAGTACTTCGATGATGGCGTGAAGA')))
def test_sample(self):
dna = 'CATGGGCATCGGCCATACGCC'
skew_values = skew(dna)
expected = '0 -1 -1 -1 0 1 2 1 1 1 0 1 2 1 0 0 0 0 -1 0 -1 -2'
self.assertEqual(expected, ToSingleLineOfString(skew_values))
def quiz2_2():
print('Q3: %s' % ToSingleLineOfString(
get_minimum_skews('CATTCCAGTACTTCGATGATGGCGTGAAGA')))
def test_sample(self):
dna = 'TAAAGACTGCCGAGAGGCCAACACGAGTGCTAGAACGAGGGGCGTAAACGCGGGTCCGAT'
min_skew_indices = get_minimum_skews(dna)
expected = '11 24'
self.assertEqual(expected, ToSingleLineOfString(min_skew_indices))
def test_extra_dataset(self):
with open('Datasets/MinimumSkewProblem_data01.txt', 'r') as datafile:
dna = datafile.readline().strip()
min_skew_indices = get_minimum_skews(dna)
expected = '89969 89970 89971 90345 90346'
self.assertEqual(expected, ToSingleLineOfString(min_skew_indices))
