def test_constraints_text_summary():
problem = DnaOptimizationProblem(sequence="ATTGCCATATGCGC",
constraints=[
EnforceGCContent(mini=0.4, maxi=0.6),
AvoidPattern('ATT')
])
text = problem.constraints_text_summary()
assert 'FAILURE: 1 constraints evaluations failed' in text
from dnachisel import (EnforceTranslation, DnaOptimizationProblem,
random_dna_sequence, Location, EnforcePattern)
# sequence = random_dna_sequence(5000, seed=123456)
# sequence = random_dna_sequence(5000, seed=2)
# sequence = random_dna_sequence(5000, seed=3)
# for seed in [2, 3, 123456]:
sequence = random_dna_sequence(5000, seed=123)
constraints = [
EnforceTranslation(Location(1000, 2500)),
EnforceTranslation(Location(3000, 4500)),
EnforcePattern("ANANANANTT", location=Location(1100, 2150)),
EnforcePattern("ATGATGCCTK", location=Location(2700, 2800))
]
problem = DnaOptimizationProblem(sequence=sequence, constraints=constraints)
print(problem.constraints_text_summary())
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
print(problem.constraints_text_summary())
all_9mers = [sequence[i:i + 9] for i in range(len(sequence) - 9)]
number_of_non_unique_9mers = sum([
count for ninemer, count in Counter(all_9mers).items() if count > 1
])
score = -(9.0 * number_of_non_unique_9mers) / len(sequence)
return SpecEvaluation(self,
problem,
score=score,
locations=[Location(0, len(sequence))],
message="Score: %.02f (%d non-unique ninemers)" %
(score, number_of_non_unique_9mers))
def __str__(self):
"""String representation."""
return "MinimizeNinemersScore"
sequence = reverse_translate(random_protein_sequence(300))
problem = DnaOptimizationProblem(sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[MinimizeNinemersScore()])
print("\n=== Status before optimization ===")
print(problem.objectives_text_summary())
problem.optimize()
print("\n=== Status after optimization ===")
print(problem.objectives_text_summary())
print(problem.constraints_text_summary(failed_only=True))
"""
from dnachisel import DnaOptimizationProblem, random_dna_sequence, AvoidMatches
from genome_collector import GenomeCollection
# THIS CREATES THE ECOLI BLAST DATABASE ON YOUR MACHINE IF NOT ALREADY HERE
collection = GenomeCollection()
ecoli_index = collection.get_taxid_bowtie_index_path(511145, version="1")
# DEFINE AND SOLVE THE PROBLEM
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(500, seed=123),
constraints=[
AvoidMatches(bowtie_index=ecoli_index, match_length=15, mismatches=1)
],
)
print(
"Constraints validity before optimization\n",
problem.constraints_text_summary(),
)
print("\nNow resolving the problems\n")
problem.resolve_constraints(final_check=True)
print(
"Constraints validity after optimization\n",
problem.constraints_text_summary(),
)
