def test_has_pathology(self):
"""Test for checking edges that have a causal pathology."""
graph = BELGraph()
a, b, c = protein(n(), n()), pathology(n(), n()), pathology(n(), n())
key = graph.add_increases(a, b, n(), n())
self.assertFalse(has_pathology_causal(graph, a, b, key))
key = graph.add_increases(b, a, n(), n())
self.assertTrue(has_pathology_causal(graph, b, a, key))
key = graph.add_association(b, a, n(), n())
self.assertFalse(has_pathology_causal(graph, b, a, key))
key = graph.add_increases(a, c, n(), n())
self.assertFalse(has_pathology_causal(graph, a, c, key))
def test_count_pathologies(self):
"""Test counting pathologies in the graph."""
graph = BELGraph()
a, b, c, d = protein(n(), n()), protein(n(), n()), pathology(n(), n()), pathology(n(), n())
graph.add_association(a, c, n(), n())
graph.add_association(a, d, n(), n())
graph.add_association(b, d, n(), n())
pathology_counter = count_pathologies(graph)
self.assertIn(c, pathology_counter)
self.assertIn(d, pathology_counter)
self.assertEqual(1, pathology_counter[c])
self.assertEqual(2, pathology_counter[d])
top_pathology_counter = get_top_pathologies(graph, count=1)
self.assertEqual(1, len(top_pathology_counter))
node, count = top_pathology_counter[0]
self.assertEqual(d, node)
self.assertEqual(2, count)
def test_get_top_hubs(self):
"""Test counting pathologies in the graph."""
graph = BELGraph()
a, b, c = protein(n(), n()), protein(n(), n()), pathology(n(), n())
graph.add_association(a, b, citation=n(), evidence=n())
graph.add_association(a, c, citation=n(), evidence=n())
top_hubs = get_top_hubs(graph, n=1)
self.assertEqual(1, len(top_hubs))
node, degree = top_hubs[0]
self.assertEqual(a, node)
self.assertEqual(2, degree)
def bel_isolated_reconstituted(self, graph: BELGraph):
"""Run the isolated node test."""
self.assertIsNotNone(graph)
self.assertIsInstance(graph, BELGraph)
adgrb1 = protein(namespace='HGNC', name='ADGRB1')
adgrb2 = protein(namespace='HGNC', name='ADGRB2')
adgrb_complex = complex_abundance([adgrb1, adgrb2])
achlorhydria = pathology(namespace='MESHD', name='Achlorhydria')
for node in graph:
self.assertIsInstance(node, BaseEntity)
self.assertIn(adgrb1, graph)
self.assertIn(adgrb2, graph)
self.assertIn(adgrb_complex, graph)
self.assertIn(achlorhydria, graph)
assert_has_edge(self, adgrb_complex, adgrb1, graph)
assert_has_edge(self, adgrb_complex, adgrb2, graph)
def test_count_pathologies(self):
"""Test counting pathologies in the graph."""
graph = BELGraph()
a, b = (protein(namespace='HGNC', name=n()) for _ in range(2))
c, d = (pathology(namespace='DOID', name=n()) for _ in range(2))
graph.add_association(a, c, citation=n(), evidence=n())
graph.add_association(a, d, citation=n(), evidence=n())
graph.add_association(b, d, citation=n(), evidence=n())
pathology_counter = count_pathologies(graph)
self.assertIn(c, pathology_counter)
self.assertIn(d, pathology_counter)
self.assertEqual(1, pathology_counter[c])
self.assertEqual(2, pathology_counter[d])
top_pathology_counter = get_top_pathologies(graph, n=1)
self.assertEqual(1, len(top_pathology_counter))
node, count = top_pathology_counter[0]
self.assertEqual(d, node)
self.assertEqual(2, count)
def test_remove_pathologies(self):
"""Test removal of pathologies."""
g = BELGraph()
p1, p2, p3 = (Protein(namespace='HGNC', name=n()) for _ in range(3))
d1, d2 = (pathology(namespace='MESH', name=n()) for _ in range(2))
g.add_increases(p1, p2, citation=n(), evidence=n())
g.add_increases(p2, p3, citation=n(), evidence=n())
g.add_positive_correlation(p1, d1, citation=n(), evidence=n())
g.add_positive_correlation(p2, d1, citation=n(), evidence=n())
g.add_association(p2, d1, citation=n(), evidence=n())
g.add_positive_correlation(d1, d2, citation=n(), evidence=n())
g.add_positive_correlation(d1, d2, citation=n(), evidence=n())
self.assertEqual(5, g.number_of_nodes())
self.assertEqual(7, g.number_of_edges())
self.assertEqual(2, len(g[p2][d1]))
remove_associations(g)
relations = list(g[p2][d1].values())
self.assertEqual(1, len(relations))
self.assertEqual(POSITIVE_CORRELATION, relations[0][RELATION])
self.assertEqual(5, g.number_of_nodes())
self.assertEqual(6, g.number_of_edges())
self.assertEqual(5, g.number_of_nodes())
remove_pathologies(g)
self.assertTrue(p1, g)
self.assertTrue(p2, g)
self.assertTrue(p3, g)
self.assertEqual(3, g.number_of_nodes())
self.assertEqual(2, g.number_of_edges())
def test_remove_pathologies(self):
"""Test removal of pathologies."""
g = BELGraph()
p1, p2, p3 = (protein(namespace='HGNC', name=n()) for _ in range(3))
d1, d2 = (pathology(namespace='MESH', name=n()) for _ in range(2))
g.add_increases(p1, p2, n(), n())
g.add_increases(p2, p3, n(), n())
g.add_qualified_edge(p1, d1, POSITIVE_CORRELATION, n(), n())
g.add_qualified_edge(p2, d1, POSITIVE_CORRELATION, n(), n())
g.add_association(p2, d1, n(), n())
g.add_qualified_edge(d1, d2, POSITIVE_CORRELATION, n(), n())
g.add_qualified_edge(d1, d2, POSITIVE_CORRELATION, n(), n())
self.assertEqual(5, g.number_of_nodes())
self.assertEqual(7, g.number_of_edges())
self.assertEqual(2, len(g[p2.as_tuple()][d1.as_tuple()]))
remove_associations(g)
relations = list(g[p2.as_tuple()][d1.as_tuple()].values())
self.assertEqual(1, len(relations))
self.assertEqual(POSITIVE_CORRELATION, relations[0][RELATION])
self.assertEqual(5, g.number_of_nodes())
self.assertEqual(6, g.number_of_edges())
self.assertEqual(5, g.number_of_nodes())
remove_pathologies(g)
self.assertTrue(g.has_node_with_data(p1))
self.assertTrue(g.has_node_with_data(p2))
self.assertTrue(g.has_node_with_data(p3))
self.assertEqual(3, g.number_of_nodes())
self.assertEqual(2, g.number_of_edges())
g(HGNC:MTHFR,sub(A,1298,C)) =| p(HGNC:MTHFR)
g(HGNC:MTHFR,sub(C,677,T)) neg a(CHEBI:"folic acid")
g(HGNC:MTHFR,sub(C,677,T)) pos path(MESH:"Alzheimer Disease")
"""
c2 = '21119889'
e2 = "Two common MTHFR polymorphisms, namely 677C>T (Ala222Val) and 1298A>C (Glu429Ala), are known to reduce MTHFR activity. \
It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects.\
the MTHFR 677C>T polymorphism as a candidate AD risk factor"
e2 = str(hash(e2))
mthfr = protein('HGNC', 'MTHFR')
mthfr_c677t = protein('HGNC', 'MTHFR', variants=[protein_substitution('Ala', 222, 'Val')])
mthfr_a1298c = protein('HGNC', 'MTHFR', variants=[protein_substitution('Glu', 429, 'Ala')])
folic_acid = abundance('CHEBI', 'folic acid')
alzheimer_disease = pathology('MESH', 'Alzheimer Disease')
example_graph.add_decreases(mthfr_c677t, mthfr, citation=c2, evidence=e2, object_modifier=activity())
example_graph.add_decreases(mthfr_a1298c, mthfr, citation=c2, evidence=e2, object_modifier=activity())
example_graph.add_negative_correlation(mthfr_c677t, folic_acid, citation=c2, evidence=e2)
example_graph.add_positive_correlation(mthfr_c677t, alzheimer_disease, citation=c2, evidence=e2)
c3 = '17948130'
e3 = 'A polymorphism in the NDUFB6 promoter region that creates a possible DNA methylation site (rs629566, A/G) was ' \
'associated with a decline in muscle NDUFB6 expression with age. Although young subjects with the rs629566 G/G ' \
'genotype exhibited higher muscle NDUFB6 expression, this genotype was associated with reduced expression in' \
' elderly subjects. This was subsequently explained by the finding of increased DNA methylation in the promoter ' \
'of elderly, but not young, subjects carrying the rs629566 G/G genotype. Furthermore, the degree of DNA' \
' methylation correlated negatively with muscle NDUFB6 expression, which in turn was associated with insulin ' \
'sensitivity.'
e3 = str(hash(e3))
r1 = rna(HGNC, '1')
p1 = protein(HGNC, '1')
p1_phosphorylated = protein(HGNC, '1', variants=[pmod('Ph')])
g2 = gene(HGNC, '2')
r2 = rna(HGNC, '2')
p2 = protein(HGNC, '2')
g3 = gene(HGNC, '3')
r3 = rna(HGNC, '3')
p3 = protein(HGNC, '3')
g4 = gene(HGNC, '4')
m4 = mirna(HGNC, '4')
p5 = pathology(GO, '5')
class TestCollapse(unittest.TestCase):
"""Tests for collapse functions."""
def test_collapse_by_dict(self):
"""Test collapsing nodes by a dictionary."""
graph = BELGraph()
graph.add_node_from_data(p1)
graph.add_node_from_data(p2)
graph.add_node_from_data(p3)
graph.add_increases(p1, p3, citation=n(), evidence=n())
graph.add_qualified_edge(p2,
p3,
relation=DIRECTLY_INCREASES,
def test_pathology(self):
node = pathology(namespace='DO', name='Alzheimer disease')
self.assertEqual('path(DO:"Alzheimer disease")', str(node))
