def test_run(self):
# perform pairwise sequence alignments
sequences = utils.parse_fasta_files(["../data/feng_test/feng1.fa"])
# case 1
print('####### WPGMA, PAM250, gap=8')
feng = FengDoolittle(gap_penalty=8,
substitution_matrix=MatrixInfo.pam250,
clustering_method=Clustering.WPGMA)
res = feng.run(sequences)
expected_order = [
"ILDMDVVEGSAARFDCKVEGYPDPEVMWFKDDNPVKESRHFQIDYDEEGN",
"ISDTEADIGSNLRWGCXAAAGKPRPMVRWLRNGEPLXASQNXRVEVXXLAX",
"RRLIPAARGGEISILCQPRAAXPKATILWSKXGTEILGNXSTRVTVXTXSD",
"RDPVKTHEGWGVMLPCNPPAHYPGLSYRWLLNEFPNFIPTDGRHFVSQXTT"
]
print(res.sequences)
print(res.score)
print('####### UPGMA, PAM250, gap=8')
feng = FengDoolittle(gap_penalty=8,
substitution_matrix=MatrixInfo.pam250,
clustering_method=Clustering.UPGMA)
res = feng.run(sequences)
expected_order = [
"ILDMDVVEGSAARFDCKVEGYPDPEVMWFKDDNPVKESRHFQIDYDEEGN",
"ISDTEADIGSNLRWGCXAAAGKPRPMVRWLRNGEPLXASQNXRVEVXXLAX",
"RRLIPAARGGEISILCQPRAAXPKATILWSKXGTEILGNXSTRVTVXTXSD",
"RDPVKTHEGWGVMLPCNPPAHYPGLSYRWLLNEFPNFIPTDGRHFVSQXTT"
]
print(res.sequences)
print(res.score)
def test_run_blossum(self):
pairs_to_result = {
('ILDMDVVEGSAARFDCKVEGYPDPEVMWFKDDNPVKESRHFQIDYDEEGN', 'RDPVKTHEGWGVMLPCNPPAHYPGLSYRWLLNEFPNFIPTDGRHFVSQTT'):
0,
('ILDMDVVEGSAARFDCKVEGYPDPEVMWFKDDNPVKESRHFQIDYDEEGN', 'ISDTEADIGSNLRWGCAAAGKPRPMVRWLRNGEPLASQNRVEVLA'):
41,
('ILDMDVVEGSAARFDCKVEGYPDPEVMWFKDDNPVKESRHFQIDYDEEGN', 'RRLIPAARGGEISILCQPRAAPKATILWSKGTEILGNSTRVTVTSD'):
5,
('RDPVKTHEGWGVMLPCNPPAHYPGLSYRWLLNEFPNFIPTDGRHFVSQTT', 'RRLIPAARGGEISILCQPRAAPKATILWSKGTEILGNSTRVTVTSD'):
-4,
('ISDTEADIGSNLRWGCAAAGKPRPMVRWLRNGEPLASQNRVEVLA', 'RRLIPAARGGEISILCQPRAAPKATILWSKGTEILGNSTRVTVTSD'):
18,
('RDPVKTHEGWGVMLPCNPPAHYPGLSYRWLLNEFPNFIPTDGRHFVSQTT', 'ISDTEADIGSNLRWGCAAAGKPRPMVRWLRNGEPLASQNRVEVLA'):
-5
}
sequence_file = '../data/guideline_tests/needlemanwunsch.fa'
sequences = parse_fasta_files([sequence_file])
gt = Gotoh(substitution_matrix=MatrixInfo.blosum62,
gap_penalty=11,
gap_extend=1,
similarity=True,
verbose=False,
complete_traceback=True)
results = gt.pairwise_alignments(sequences)
for result in results:
seqs = (str(result.seq1), str(result.seq2))
expected_score = pairs_to_result[seqs]
self.assertEqual(result.score, expected_score)
print(len(result.alignments))
def test_run_blossum(self):
pairs_to_result = {
('ILDMDVVEGSAARFDCKVEGYPDPEVMWFKDDNPVKESRHFQIDYDEEGN', 'RDPVKTHEGWGVMLPCNPPAHYPGLSYRWLLNEFPNFIPTDGRHFVSQTT'):
4,
('ILDMDVVEGSAARFDCKVEGYPDPEVMWFKDDNPVKESRHFQIDYDEEGN', 'ISDTEADIGSNLRWGCAAAGKPRPMVRWLRNGEPLASQNRVEVLA'):
37,
('ILDMDVVEGSAARFDCKVEGYPDPEVMWFKDDNPVKESRHFQIDYDEEGN', 'RRLIPAARGGEISILCQPRAAPKATILWSKGTEILGNSTRVTVTSD'):
-4,
('RDPVKTHEGWGVMLPCNPPAHYPGLSYRWLLNEFPNFIPTDGRHFVSQTT', 'ISDTEADIGSNLRWGCAAAGKPRPMVRWLRNGEPLASQNRVEVLA'):
3,
('RDPVKTHEGWGVMLPCNPPAHYPGLSYRWLLNEFPNFIPTDGRHFVSQTT', 'RRLIPAARGGEISILCQPRAAPKATILWSKGTEILGNSTRVTVTSD'):
9,
('ISDTEADIGSNLRWGCAAAGKPRPMVRWLRNGEPLASQNRVEVLA', 'RRLIPAARGGEISILCQPRAAPKATILWSKGTEILGNSTRVTVTSD'):
24
}
sequence_file = '../data/guideline_tests/needlemanwunsch.fa'
sequences = parse_fasta_files([sequence_file])
# init the needleman
settings = ScoringSettings(substitution_matrix=MatrixInfo.blosum62,
gap_penalty=6,
similarity=True)
nw = NeedlemanWunsch(settings, complete_traceback=False, verbose=False)
results = nw.pairwise_alignments(sequences)
for result in results:
seqs = (str(result.seq1.seq), str(result.seq2.seq))
expected_score = pairs_to_result[seqs]
self.assertEqual(result.score, expected_score)
def test_calculate_guide_tree(self):
nw = NeedlemanWunsch()
sequences = utils.parse_fasta_files(["../data/xpgma/xpgma1.fa"])
alignments = nw.pairwise_alignments(sequences)
xpgma = Xpgma()
xpgma.create_distance_matrix(alignments)
guidetree = xpgma.calculate_guide_tree()
expected = '((A:2.00,B:2.00):0.00,C:2.00)'
expected_nodes = "{'A': A:2.00, 'B': B:2.00, 'C': C:2.00, 'AB': (A:2.00,B:2.00):0.00, 'ABC': ABC:NONE}"
self.assertEqual(str(guidetree), expected)
self.assertEqual(str(guidetree.nodes), expected_nodes)
def test_convert_to_evolutionary_distances(self):
# perform pairwise sequence alignments
nw = NeedlemanWunsch()
sequences = utils.parse_fasta_files(
["../data/feng_test/conversion.fa"])
alignments = nw.pairwise_alignments(sequences)
feng = FengDoolittle()
# Convert the scores to approximate pairwise evolutionary distances.
alignment = alignments[0]
print(f'Alignment: {alignment} ')
alignment.score = feng.convert_to_evolutionary_distances(alignment)
print(f'Score: {alignment.score} ')
self.assertAlmostEqual(first=2.70805020110221, second=alignment.score)
def test_create_distance_matrix(self):
nw = NeedlemanWunsch()
sequences = utils.parse_fasta_files(["../data/xpgma/xpgma1.fa"])
alignments = nw.pairwise_alignments(sequences)
xpgma = Xpgma()
xpgma.create_distance_matrix(alignments)
self.assertDictEqual(
xpgma.distances, {
'A': {
'B': 4.0,
'C': 4.0
},
'B': {
'A': 4.0,
'C': 4.0
},
'C': {
'A': 4.0,
'B': 4.0
}
})