def setUp(self):
self.feature_by_id = {
"GENE_1": DummyCDS(0, 30000, locus_tag="GENE_1"),
"GENE_2": DummyCDS(30000, 50000, locus_tag="GENE_2"),
"GENE_3": DummyCDS(70000, 90000, locus_tag="GENE_3"),
"GENE_X": DummyCDS(95000, 100000, locus_tag="GENE_X"),
"GENE_4": DummyCDS(125000, 140000, locus_tag="GENE_4"),
"GENE_5": DummyCDS(145000, 150000, locus_tag="GENE_5")
}
self.features = list(self.feature_by_id.values())
self.features.sort(key=lambda x: x.location.start) # vital for py3 < 3.5
self.record = DummyRecord(self.features)
self.results_by_id = {
"GENE_1": [FakeHSPHit("a", "GENE_1", 0, 10, 50, 0),
FakeHSPHit("b", "GENE_1", 0, 10, 50, 0)],
"GENE_2": [FakeHSPHit("a", "GENE_1", 0, 10, 50, 0),
FakeHSPHit("c", "GENE_1", 0, 10, 50, 0)],
"GENE_3": [FakeHSPHit("b", "GENE_1", 0, 10, 50, 0),
FakeHSPHit("c", "GENE_1", 0, 10, 50, 0)],
"GENE_4": [FakeHSPHit("e", "GENE_1", 0, 10, 50, 0),
FakeHSPHit("f", "GENE_1", 0, 10, 50, 0)],
"GENE_5": [FakeHSPHit("f", "GENE_1", 0, 10, 50, 0),
FakeHSPHit("g", "GENE_1", 0, 10, 50, 0)]}
self.signature_names = set(["a", "b", "c", "d", "e", "f", "g", "modelA", "modelB"])
def setUp(self):
self.config = build_config([])
self.rules_file = path.get_full_path(__file__, "..", "cluster_rules", "strict.txt")
self.signature_file = path.get_full_path(__file__, "..", "data", "hmmdetails.txt")
self.signature_names = {sig.name for sig in core.get_signature_profiles()}
self.valid_categories = {cat.name for cat in core.get_rule_categories()}
self.filter_file = path.get_full_path(__file__, "..", "filterhmmdetails.txt")
self.results_by_id = {
"GENE_1": [
FakeHSPHit("modelA", "GENE_1", 0, 10, 50, 0),
FakeHSPHit("modelB", "GENE_1", 0, 10, 50, 0)
],
"GENE_2": [
FakeHSPHit("modelC", "GENE_2", 0, 10, 50, 0),
FakeHSPHit("modelB", "GENE_2", 0, 10, 50, 0)
],
"GENE_3": [
FakeHSPHit("modelC", "GENE_3", 0, 10, 50, 0),
FakeHSPHit("modelF", "GENE_3", 0, 10, 50, 0)
],
"GENE_4": [
FakeHSPHit("modelA", "GENE_4", 0, 10, 50, 0),
FakeHSPHit("modelE", "GENE_4", 0, 10, 50, 0)
],
"GENE_5": [
FakeHSPHit("modelA", "GENE_5", 0, 10, 50, 0),
FakeHSPHit("modelG", "GENE_5", 0, 10, 50, 0)
]
}
self.feature_by_id = {
"GENE_1": DummyCDS(0, 30000, locus_tag="GENE_1"),
"GENE_2": DummyCDS(30000, 50000, locus_tag="GENE_2"),
"GENE_3": DummyCDS(70000, 90000, locus_tag="GENE_3"),
"GENE_X": DummyCDS(95000, 100000, locus_tag="GENE_X"), # no hits
"GENE_4": DummyCDS(125000, 140000, locus_tag="GENE_4"),
"GENE_5": DummyCDS(130000, 150000, locus_tag="GENE_5")
}
self.test_names = {"modelA", "modelB", "modelC", "modelF", "modelG",
"a", "b", "c", "d"}
self.categories = {"Cat"}
self.rules = rule_parser.Parser("\n".join([
"RULE MetaboliteA CATEGORY Cat CUTOFF 10 NEIGHBOURHOOD 5 CONDITIONS modelA",
"RULE MetaboliteB CATEGORY Cat CUTOFF 10 NEIGHBOURHOOD 5 CONDITIONS cds(modelA and modelB)",
"RULE MetaboliteC CATEGORY Cat CUTOFF 10 NEIGHBOURHOOD 5 CONDITIONS (modelA and modelB)",
"RULE MetaboliteD CATEGORY Cat CUTOFF 20 NEIGHBOURHOOD 5 CONDITIONS minimum(2,[modelC,modelB]) and modelA",
"RULE Metabolite0 CATEGORY Cat CUTOFF 1 NEIGHBOURHOOD 3 CONDITIONS modelF",
"RULE Metabolite1 CATEGORY Cat CUTOFF 1 NEIGHBOURHOOD 3 CONDITIONS modelG"]),
self.test_names, self.categories).rules
self.features = []
for gene_id in self.feature_by_id:
self.features.append(self.feature_by_id[gene_id])
self.features.sort(key=lambda x: x.location.start) # vital for py3 < 3.5
self.record = Record()
self.record._record.seq = Seq("A"*150000)
for feature in self.features:
self.record.add_cds_feature(feature)
def test_filter(self):
# fake HSPs all in one CDS with overlap > 20 and query_ids from the same equivalence group
# not overlapping by > 20
first = FakeHSPHit("AMP-binding", "A", 50, 90, 0.1, None)
second = FakeHSPHit("A-OX", "A", 70, 100, 0.5, None)
new, by_id = hmm_detection.filter_results([first, second], {"A": [first, second]},
self.filter_file, self.signature_names)
assert new == [first, second]
assert by_id == {"A": [first, second]}
# overlapping, in same group
first.hit_end = 91
assert hmm_detection.hsp_overlap_size(first, second) == 21
new, by_id = hmm_detection.filter_results([first, second], {"A": [first, second]},
self.filter_file, self.signature_names)
assert new == [second]
assert by_id == {"A": [second]}
# overlapping, not in same group
second.query_id = "none"
new, by_id = hmm_detection.filter_results([first, second], {"A": [first, second]},
self.filter_file, self.signature_names)
assert new == [first, second]
assert by_id == {"A": [first, second]}
# not in the same CDS, but int he same group
second.hit_id = "B"
second.query_id = "A-OX"
new, by_id = hmm_detection.filter_results([first, second], {"A": [first], "B": [second]},
self.filter_file, self.signature_names)
assert new == [first, second]
assert by_id == {"A": [first], "B": [second]}
def test_chained_and_a(self):
# remove the c hit from GENE_2
self.results_by_id["GENE_2"] = [FakeHSPHit("a", "GENE_1", 0, 10, 50, 0)]
results = self.run_test("A", 25, 20, "a and b and not c")
# GENE_1 contains both
# GENE_2 contains a b, but reaches the c in GENE_3
self.expect(results, ["GENE_1"])
def test_hsp_overlap_size(self):
overlap_size = hmm_detection.hsp_overlap_size
first = FakeHSPHit("A", "A", 50, 60, 0., None)
second = FakeHSPHit("B", "B", 70, 100, 0., None)
# no overlap
assert overlap_size(first, second) == 0
first.hit_end = 70
# still no overlap, end isn't inclusive
assert overlap_size(first, second) == 0
# a mix of second starting inside first
for i in range(1, 30):
first.hit_end += 1
assert overlap_size(first, second) == i
# second wholly contained
first.hit_end = 110
assert overlap_size(first, second) == 30
# first inside second
first.hit_start = 75
assert overlap_size(first, second) == 25
# first inside second, but direction reversed
first.hit_end = 50
with self.assertRaises(AssertionError):
overlap_size(first, second)
def test_filter_multiple(self):
# all in one CDS no overlap and the same query_ids -> cull all but the best score
# not overlapping, not same query_id
first = FakeHSPHit("AMP-binding", "A", 50, 60, 0.1, None)
second = FakeHSPHit("A-OX", "A", 70, 100, 0.5, None)
both = [first, second]
by_id = {"A": [first, second]}
new, by_id = hmm_detection.filter_result_multiple(
list(both), dict(by_id))
assert new == [first, second]
assert by_id == {"A": [first, second]}
# not overlapping, same query_id
first.query_id = "A-OX"
new, by_id = hmm_detection.filter_result_multiple(
list(both), dict(by_id))
assert new == [second]
assert by_id == {"A": [second]}
# not in same CDS, same query_id
second.hit_id = "B"
by_id = {"A": [first], "B": [second]}
new, by_id = hmm_detection.filter_result_multiple(
list(both), dict(by_id))
assert new == [first, second]
assert by_id == {"A": [first], "B": [second]}
def test_single_gene(self):
self.results_by_id = {
"GENE_1": [
FakeHSPHit("modelA", "GENE_1", 0, 10, 50, 0),
FakeHSPHit("modelB", "GENE_1", 0, 10, 50, 0)
]
}
self.feature_by_id = {"GENE_1": DummyCDS(0, 30000, locus_tag="GENE_1")}
results = self.run_test("A", 10, 20, "minimum(2, [modelA,modelB])")
self.expect(results, ["GENE_1"])
results = self.run_test("A", 10, 20, "cds(modelA and modelB)")
self.expect(results, ["GENE_1"])
results = self.run_test("A", 10, 20, "cds(modelA or modelB)")
self.expect(results, ["GENE_1"])
results = self.run_test("A", 10, 20, "modelA and modelB")
self.expect(results, ["GENE_1"])
results = self.run_test("A", 10, 20, "modelA or modelB")
self.expect(results, ["GENE_1"])
