def test_circular_sequence_optimize_with_report(tmpdir):
"""Test that the custom function of CircularDnaOptimizationProblems works.
"""
np.random.seed(123)
# Until the feature gets more battle-test, we're making sure it works
# across a range of sequences.
dna_sequence = (
"CTC"
+ dc.random_dna_sequence(100)
+ "CGTCTC"
+ dc.random_dna_sequence(100)
+ "CGT"
)
problem = dc.CircularDnaOptimizationProblem(
sequence=dna_sequence,
constraints=[
dc.AvoidPattern("BsmBI_site"),
dc.EnforceGCContent(
mini=0.4, maxi=0.6, location=(150, 250), window=50
),
dc.UniquifyAllKmers(k=9, location=(10, 100)),
],
logger=None,
)
target = os.path.join(str(tmpdir), "circular_with_solution")
os.mkdir(target)
assert os.listdir(target) == []
assert not problem.all_constraints_pass()
success, message, data = problem.optimize_with_report(target)
assert problem.all_constraints_pass()
record = problem.to_record()
assert str(record.seq) != dna_sequence
def test_circular_sequence_basic():
np.random.seed(123)
# Until the feature gets more battle-test, we're making sure it works
# across a range of sequences.
for i in range(4):
dna_sequence = (
"CTC"
+ dc.random_dna_sequence(100)
+ "CGTCTC"
+ dc.random_dna_sequence(100)
+ "CGT"
)
problem = dc.CircularDnaOptimizationProblem(
sequence=dna_sequence,
constraints=[
dc.AvoidPattern("BsmBI_site"),
dc.EnforceGCContent(
mini=0.4, maxi=0.6, location=(150, 250), window=50
),
dc.UniquifyAllKmers(k=9, location=(10, 100)),
],
logger=None,
)
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_whole_sequence_change_objective_100():
np.random.seed(123)
problem = dc.DnaOptimizationProblem(
sequence=dc.random_dna_sequence(50), objectives=[dc.EnforceChanges()]
)
problem.optimize()
assert problem.number_of_edits() == 50
def test_EnforceRegionsCompatibility():
# Two enzymes, BsmBI(CGTCTC) is GC-rich, EcoRI(GAATTC) is GC-poor, which
# enzyme will be chosen and inserted in the sequence depends on the other
# constraint on GC content
numpy.random.seed(123)
def compatibility_condition(location1, location2, problem):
seq1 = location1.extract_sequence(problem.sequence)
seq2 = location2.extract_sequence(problem.sequence)
return sequences_differences(seq1, seq2) >= 2
locations = [(0, 4), (50, 54), (100, 104), (150, 154)]
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(200, seed=123),
constraints=[
EnforceRegionsCompatibility(
locations=locations,
compatibility_condition=compatibility_condition,
condition_label="2bp difference",
),
EnforceGCContent(mini=0.4, maxi=0.6, window=40),
],
logger=None,
)
assert not any([e.passes for e in problem.constraints_evaluations()])
problem.resolve_constraints()
assert problem.all_constraints_pass()
seq = problem.sequence
assert [
sequences_differences(seq[s1:e1], seq[s2:e2]) >= 2
for (s1, e1), (s2, e2) in itertools.combinations(locations, 2)
]
def make_restriction_part(part_length, left_overhang, right_overhang,
enzyme, forbidden_enzymes, assembly_enzyme='BsmBI'):
l_left = len(left_overhang)
l_right = len(right_overhang)
left_overhang_location = (0, l_left)
right_overhang_location = (l_left + part_length,
l_left + part_length + l_right)
center_location = (l_left, l_left + part_length)
core_sequence = (left_overhang + dc.random_dna_sequence(part_length)
+ right_overhang)
enforce_enzyme = dc.EnforcePatternOccurence(
enzyme=enzyme, location=center_location)
problem = dc.DnaOptimizationProblem(
sequence=core_sequence,
constraints=[
dc.AvoidChanges(left_overhang_location),
dc.AvoidChanges(right_overhang_location),
] + [enforce_enzyme] + [
dc.AvoidPattern(enzyme=enzyme_name)
for enzyme_name in forbidden_enzymes + [assembly_enzyme]
]
)
problem.resolve_constraints()
core_sequence = dc.sequence_to_biopython_record(problem.sequence)
for loc in [left_overhang_location, right_overhang_location]:
dc.annotate_record(core_sequence, loc, 'overhang')
site_location = enforce_enzyme.evaluate(problem).data['matches'][0]
dc.annotate_record(core_sequence, site_location.to_tuple(), enzyme)
assembly_site = Restriction.__dict__[assembly_enzyme].site
flank = dc.sequence_to_biopython_record(assembly_site + 'A')
dc.annotate_record(flank, label='flank')
return flank + core_sequence + flank.reverse_complement()
def test_whole_sequence_change_constraint_100():
np.random.seed(123)
problem = dc.DnaOptimizationProblem(
sequence=dc.random_dna_sequence(50), constraints=[dc.EnforceChanges()]
)
assert problem.all_constraints_pass() # due to initial seq. constraining
assert problem.number_of_edits() == 50
def test_enforce_changes_with_indices_as_constraint():
np.random.seed(123)
indices = [10, 20] + list(range(30, 40)) + [44, 45, 46]
problem = dc.DnaOptimizationProblem(
sequence=dc.random_dna_sequence(50),
constraints=[dc.EnforceChanges(indices=indices)],
)
assert problem.number_of_edits() == 15
def test_feature_to_spec():
sequence = random_dna_sequence(100)
record = sequence_to_biopython_record(sequence)
label = "@gc(40-60%/20bp) & @no(BsaI_site) & @keep"
annotate_record(record, label=label)
feature = record.features[0]
specs = Specification.list_from_biopython_feature(feature)
assert len(specs) == 3
def test_avoid_pattern_basics():
numpy.random.seed(123)
problem = DnaOptimizationProblem(sequence=random_dna_sequence(10000,
seed=123),
constraints=[AvoidPattern(enzyme="BsaI")])
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_AvoidNonUniqueSegments_as_objective():
numpy.random.seed(123)
sequence = random_dna_sequence(1000, seed=123)
specification = AvoidNonUniqueSegments(8)
problem = DnaOptimizationProblem(sequence=sequence,
objectives=[specification])
problem.optimize()
assert problem.objectives[0].evaluate(problem).passes
def test_AvoidNonuniqueSegments_as_constraint():
numpy.random.seed(123)
sequence = random_dna_sequence(1000, seed=123)
problem = DnaOptimizationProblem(sequence=sequence,
constraints=[AvoidNonuniqueSegments(8)])
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_SequenceLengthBounds():
for length, expected in [(750, True), (400, False), (1200, False)]:
problem = dc.DnaOptimizationProblem(
sequence=dc.random_dna_sequence(length),
constraints=[dc.SequenceLengthBounds(500, 800)],
logger=None,
)
assert problem.all_constraints_pass() == expected
def test_UniquifyAllKmers_as_objective():
numpy.random.seed(123)
sequence = random_dna_sequence(1000, seed=123)
specification = UniquifyAllKmers(8)
problem = DnaOptimizationProblem(sequence=sequence,
objectives=[specification],
logger=None)
problem.optimize()
assert problem.objectives[0].evaluate(problem).passes
def test_UniquifyAllKmers_as_constraint():
numpy.random.seed(123)
sequence = random_dna_sequence(1000, seed=123)
problem = DnaOptimizationProblem(sequence=sequence,
constraints=[UniquifyAllKmers(8)],
logger=None)
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_avoid_hairpin_basics():
numpy.random.seed(123)
random_sequences = [random_dna_sequence(30) for i in range(10)]
full_sequence = "".join([
seq
for sequence in random_sequences
for seq in (random_dna_sequence(50),
sequence,
random_dna_sequence(50),
reverse_complement(sequence),
random_dna_sequence(50))
])
problem = DnaOptimizationProblem(full_sequence,
constraints=[AvoidHairpins()])
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_whole_sequence_change_constraint_4():
np.random.seed(123)
problem = dc.DnaOptimizationProblem(
sequence=dc.random_dna_sequence(50),
constraints=[dc.EnforceChanges(minimum=4)],
)
print(problem.number_of_edits())
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert 6 >= problem.number_of_edits() >= 4
def test_codon_optimize_with_custom_table():
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(1200, seed=123),
constraints=[EnforceTranslation()],
objectives=[CodonOptimize(
codon_usage_table=biotools.CODON_USAGE_TABLES['b_subtilis'])]
)
assert (problem.objective_scores_sum() < -10)
problem.optimize()
assert (problem.objective_scores_sum() == 0)
def test_record_with_multispec_feature():
sequence = random_dna_sequence(100)
record = sequence_to_biopython_record(sequence)
label = "@gc(40-60%/20bp) & @no(BsaI_site) & @keep"
annotate_record(record, label=label)
problem = DnaOptimizationProblem.from_record(record)
assert len(problem.constraints) == 3
c1, c2, c3 = problem.constraints
assert c1.mini == 0.4
assert c2.pattern.name == "BsaI"
def test_optimize_with_report(tmpdir):
problem = DnaOptimizationProblem(sequence=random_dna_sequence(10000,
seed=123),
constraints=[AvoidPattern('BsmBI_site')])
target = os.path.join(str(tmpdir), 'with_solution')
os.mkdir(target)
assert os.listdir(target) == []
success, message, data = problem.optimize_with_report(target)
assert success
assert os.listdir(target) != []
def test_codon_optimize_with_custom_table():
table = get_codons_table("b_subtilis")
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(1200, seed=123),
constraints=[EnforceTranslation()],
objectives=[CodonOptimize(codon_usage_table=table)],
logger=None,
)
assert problem.objective_scores_sum() < -10
problem.optimize()
assert problem.objective_scores_sum() == 0
def random_compatible_dna_sequence(sequence_length, constraints, probas=None,
seed=None, max_random_iters=5000,
logger='bar', **kwargs):
sequence = dc.random_dna_sequence(
sequence_length, probas=probas, seed=seed)
problem = dc.DnaOptimizationProblem(sequence, constraints=constraints,
logger=logger)
problem.max_random_iters = max_random_iters
problem.resolve_constraints(**kwargs)
return problem.sequence
def test_optimization_with_report_no_solution(tmpdir):
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(10000, seed=123),
constraints=[AvoidPattern(enzyme='BsmBI'), AvoidChanges()]
)
target = os.path.join(str(tmpdir), 'no_solution')
os.mkdir(target)
assert os.listdir(target) == []
success, message, data = optimization_with_report(target, problem)
assert not success
assert os.listdir(target) != []
def test_codon_optimize_as_hard_constraint():
numpy.random.seed(123)
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(2000, seed=123),
constraints=[
EnforceTranslation(location=Location(1000, 1300)),
CodonOptimize(location=Location(1000, 1300), species='e_coli')
]
)
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_basics():
numpy.random.seed(123)
probas = {'A': 0.2, 'T': 0.2, 'G': 0.3, 'C': 0.3}
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(10000, probas=probas, seed=123),
constraints=[
AvoidPattern(enzyme="BsaI"),
EnforceTerminalGCContent(mini=0.2, maxi=0.4, window_size=50)
])
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_optimize_with_report_no_solution(tmpdir):
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(10000, seed=123),
constraints=[AvoidPattern("BsmBI_site"), AvoidChanges()],
logger=None,
)
target = os.path.join(str(tmpdir), "no_solution")
os.mkdir(target)
assert os.listdir(target) == []
success, message, data = problem.optimize_with_report(target)
assert not success
assert os.listdir(target) != []
def test_avoid_matches_with_phage():
PHAGE_TAXID = "697289"
collection = GenomeCollection()
index = collection.get_taxid_bowtie_index_path(PHAGE_TAXID, version="1")
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(30, seed=123),
constraints=[AvoidMatches(bowtie_index=index, match_length=10)],
logger=None,
)
all_breaches = problem.constraints_evaluations().all_locations()
assert len(all_breaches) == 5
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_basics():
numpy.random.seed(123)
probas = {"A": 0.2, "T": 0.2, "G": 0.3, "C": 0.3}
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(10000, probas=probas, seed=123),
constraints=[
AvoidPattern("BsaI_site"),
EnforceTerminalGCContent(mini=0.2, maxi=0.4, window_size=50),
],
logger=None,
)
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_EnforceGCContents():
numpy.random.seed(123)
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(10000, seed=123),
constraints=[
AvoidPattern(enzyme="BsaI"),
EnforceGCContent(mini=0.3, maxi=0.7, window=50)
],
objectives=[EnforceGCContent(target=0.4)]
)
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
def test_avoid_changes_with_indices_as_constraint():
numpy.random.seed(123)
indices = [10, 20] + list(range(30, 40)) + [44, 45, 46]
sequence = random_dna_sequence(50)
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[AvoidChanges(indices=indices)],
objectives=[EnforceChanges()],
logger=None,
)
problem.optimize()
assert problem.number_of_edits() == 50 - 15
def create_new_primer(existing_primers):
"""Create a new primer based on the primers created so far"""
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(length=20),
constraints=[
AvoidHeterodimerization(existing_primers, tmax=3),
AvoidPattern("3x3mer"),
AvoidPattern("4xG"),
],
objectives=[EnforceGCContent(target=0.6)],
logger=None,
)
problem.resolve_constraints()
problem.optimize()
return problem.sequence
