def test_should_raise_exception_if_msa_contains_only_one_sequence(self):
# setup
sequence = MSA(['AA'])
# check
with self.assertRaises(Exception):
Score(sequence)
def test_should_raise_exception_if_sequences_have_different_lengths(self):
# setup
sequences = MSA(['AA', 'A', 'AA'])
# check
with self.assertRaises(Exception):
Score(sequences)
def test_most_frequent_A_with_BLOSUM62(self):
# setup
sequences = MSA(['AA', 'AC', 'AC'])
# results
result = Star(sequences, Blosum62()).compute()
expected = 30
# check
self.assertEqual(expected, result)
def test_get_score_of_column_with_only_gaps(self):
# setup
column = MSA(['-', '-', '-'])
# results
result = SumOfPairs(column).get_column_score(0)
expected = 3
# check
self.assertEqual(expected, result)
def test_only_gaps_with_BLOSUM62(self):
# setup
sequences = MSA(['---', '---'])
# results
result = SumOfPairs(sequences, Blosum62()).compute()
expected = 3
# check
self.assertEqual(expected, result)
def test_basic_score_with_gaps_BLOSUM62(self):
# setup
sequences = MSA(['FA', 'A-'])
# results
result = SumOfPairs(sequences, Blosum62()).compute()
expected = -10
# check
self.assertEqual(expected, result)
def test_percentage_of_non_gaps_0(self):
# setup
sequences = MSA(["----", "----"])
# results
result = PercentageOfNonGaps(sequences).compute()
expected = 0.0
# check
self.assertEqual(result, expected)
def test_basic_score_of_12_with_PAM250(self):
# setup
sequences = MSA(['AA', 'AA', 'AA'])
# results
result = SumOfPairs(sequences, PAM250()).compute()
expected = 12
# check
self.assertEqual(expected, result)
def test_percentage_of_totally_conserved_columns_0(self):
# setup
sequences = MSA(["ABCD", "DCBA"])
# results
result = PercentageOfTotallyConservedColumns(sequences).compute()
expected = 0.0
# check
self.assertEqual(result, expected)
def test_compute_of_two_gapped_seqs(self):
# setup
sequences = MSA(["-----", "-----"])
# results
expected = 0
result = Entropy(sequences).compute()
# check
self.assertEqual(expected, result)
def test_compute_of_three_seqs_with_gaps(self):
# setup
sequences = MSA(["A-TGCAAT-G", "-CT-CCAT-A", "-TTAT-CTG-"])
# results
expected = -6.94
result = round(Entropy(sequences).compute(), 2)
# check
self.assertEqual(expected, result)
def test_compute_of_four_seqs_with_no_gaps(self):
# setup
sequences = MSA(["ACGT", "ACGT", "TGCA", "TGCA"])
# results
expected = -2.77
result = round(Entropy(sequences).compute(), 2)
# check
self.assertEqual(expected, result)
def test_most_frequent_gaps_with_PAM250(self):
# setup
sequences = MSA(['AA', 'A-', 'AC'])
# results
result = Star(sequences, PAM250()).compute()
expected = -2
# check
self.assertEqual(expected, result)
def test_get_score_of_an_alignment(self):
# setup
sequences = \
MSA(['---GKGDPKKPRGKMSSYAFFVQTSREEHKKKHPDASVNFSEFSKKCSERWKTMSAKEKGKFEDMAKADKARYEREMKTYI------PPKGE----',
'------MQDRVKRPMNAFIVWSRDQRRKMALENPRMR-NS-EISKQLGYQWKMLTEAEKWPFFQEAQKLQAMHREKYPNYKYRP---RRKAKMLPK',
'MKKLKKHPDFPKKPLTPYFRFFMEKRAKYAKLHPEMS-NL-DLTKILSKKYKELPEKKKMKYIQDFQREKQEFERNLARFREDH---PDLIQNAKK',
'--------MHIKKPLNAFMLYMKEMRANVVAES-TLK-ESAAINQILGRRWHALSREEQAKYYELARKERQLHMQLYPGWSARDNYGKKKKRKREK'])
# results
result = SumOfPairs(sequences, PAM250()).compute()
expected = 24
# check
self.assertEqual(expected, result)
def run_all_scores(sequences: list) -> None:
aligned_sequences = list(pair[1] for pair in sequences)
sequences_id = list(pair[0] for pair in sequences)
msa = MSA(aligned_sequences, sequences_id)
print_alignment(msa)
# Percentage of non-gaps and totally conserved columns
non_gaps = PercentageOfNonGaps(msa)
totally_conserved_columns = PercentageOfTotallyConservedColumns(msa)
percentage = non_gaps.compute()
print("Percentage of non-gaps: {0} %".format(percentage))
conserved = totally_conserved_columns.compute()
print("Percentage of totally conserved columns: {0}".format(conserved))
# Entropy
value = Entropy(msa).compute()
print("Entropy score: {0}".format(value))
# Sum of pairs
value = SumOfPairs(msa, Blosum62()).compute()
print("Sum of Pairs score (Blosum62): {0}".format(value))
value = SumOfPairs(msa, PAM250()).compute()
print("Sum of Pairs score (PAM250): {0}".format(value))
value = SumOfPairs(msa, FileMatrix('PAM380.txt')).compute()
print("Sum of Pairs score (PAM380): {0}".format(value))
# Star
value = Star(msa, Blosum62()).compute()
print("Star score (Blosum62): {0}".format(value))
value = Star(msa, PAM250()).compute()
print("Star score (PAM250): {0}".format(value))
