from model_maker_utils import equal_distribution
from matrix_from_aln import matrix_from_exa
matrixAcceptor0 = numpy.array(matrix_from_exa('new_acceptor1.exa'))
acceptor0_data = classify(matrixAcceptor0, 2)
model = HiddenMarkovModel('intron_acceptor')
intron = State(DiscreteDistribution(
calculator.intron_calculator('cuts_intron.txt').p),
name='in')
acceptor0_states = sequence_state_factory(acceptor0_data, 'acceptor0')
post = State(DiscreteDistribution(equal_distribution), name='post')
model.add_state(intron)
add_sequence(model, acceptor0_states)
model.add_state(post)
model.add_transition(model.start, intron, 1)
model.add_transition(intron, intron, 0.9)
model.add_transition(intron, acceptor0_states[0], 0.1)
model.add_transition(acceptor0_states[-1], post, 1)
model.add_transition(post, post, 0.5)
model.add_transition(post, model.end, 0.5)
model.bake()
test_l = 'GTAACACTGAATACTCAGGAACAATTAATGGATGGTAACATATGAGGAATATCTAGGAGGCACACCCTCTCTGGCATCTATGATGGGCCAAAAACCCGCATTCGCTTGGCCACAGTATGTGAAATATAACCCAGCTTAGACACAGGGTGCGGCAGCTGTCATGTTTCTCTGTGTGTGCCGAGTGTCATGTCTGCACCGTACAGGGATAGCTGAGTCTTCATCCTCCTCAGCTCCTATCTGTCCAGTGCAATGAACAGCAGCTGCTCTCTTCCTCTCTGGTTCCCATGGCAGCCATGCTCTGTTGCAGAGAGAACAGGATTGCATGTTCCCTCTTAATGGGAACGTCCATTTTGCTTTCTGGGACCACTCTCTTAATGCCGCCTGTCAAAACCAGCTAGGACTCCCTGGGGTCCAATCCCTCTGTGTTTAATCTTCTGTCATCTCTGTCCCACCTGGCTCATCAGGGAGATGCAGAAGGCTGAAGAAAAGGAAGTCCCTGAGGACTCACTGGAGGAATGTGCCATCACTTGTTCAAATAGCCATGGCCCTTATGACTCCAACCATGACTCCAACC'
converted = converter_to(test_l.lower().replace(' ', '').replace('p', ''))
#logp, path = model.viterbi(converted)
#print(logp, [x[1].name + str(i) for i, x in enumerate(path)])
def train_and_test():
with open('../data extractors/exons_start_1.txt') as in_file:
total = []
for line in in_file:
no_p_line = line.replace('P', '').lower().replace('\n', '')
total.append(no_p_line)
converted_total = [converter_to(x, 2) for x in total]
matrixDonor0 = numpy.array(
matrix_from_exa('../data extractors/new_donor1.exa'))
c0, c1, c2 = calculator.calculate_proba2('../data extractors/new_cuts.txt')
print(c0.p, c1.p, c2.p)
coding_state0 = State(DiscreteDistribution(c0.p), 'coding state 0')
coding_state1 = State(DiscreteDistribution(c1.p), 'coding state 1')
coding_state2 = State(DiscreteDistribution(c2.p), 'coding state 2')
donor0_data = classify(matrixDonor0, 2)
donor0_states = sequence_state_factory(donor0_data, 'donor0')
post = State(DiscreteDistribution(equal_distribution), name='post')
model = HiddenMarkovModel('coding to donor')
model.add_state(coding_state0)
model.add_state(coding_state1)
model.add_state(coding_state2)
add_sequence(model, donor0_states)
model.add_state(post)
model.add_transition(model.start, coding_state0, 1)
model.add_transition(coding_state0, coding_state1, 0.6)
model.add_transition(coding_state0, donor0_states[0], 0.4)
model.add_transition(coding_state1, coding_state2, 0.6)
model.add_transition(coding_state1, donor0_states[0], 0.4)
model.add_transition(coding_state2, coding_state0, 0.6)
model.add_transition(coding_state2, donor0_states[0], 0.4)
model.add_transition(donor0_states[-1], post, 1)
model.add_transition(post, post, 0.9)
model.add_transition(post, model.end, 0.1)
model.bake()
test_model(model)
model.fit(converted_total,
transition_pseudocount=1,
emission_pseudocount=1,
verbose=True)
test_model(model)
with open('partial_model_coding_to_donor_model0.json', 'w') as out:
out.write(model.to_json())
coding_state0 = State(DiscreteDistribution(c0.p), 'coding state 0') coding_state1 = State(DiscreteDistribution(c1.p), 'coding state 1') coding_state2 = State(DiscreteDistribution(c2.p), 'coding state 2') coding_model.add_state(back) coding_model.add_state(fake_back) coding_model.add_state(coding_state0) coding_model.add_state(coding_state1) coding_model.add_state(coding_state2) coding_model.add_state(in0) coding_model.add_state(in1) coding_model.add_state(in2) coding_model.add_state(exon3_state) add_sequence(coding_model, poly_a_states) add_sequence(coding_model, post_poly_spacer) add_sequence(coding_model, in0_spacers) add_sequence(coding_model, in1_spacers) add_sequence(coding_model, in2_spacers) add_sequence(coding_model, ze_states) add_sequence(coding_model, ez_states_taa) add_sequence(coding_model, ez_states_tga) add_sequence(coding_model, ez_states_tag) add_sequence(coding_model, donor0_states) add_sequence(coding_model, donor1_states) add_sequence(coding_model, donor2_states)
coding_state0 = State(DiscreteDistribution(c0.p), 'coding state 0')
coding_state1 = State(DiscreteDistribution(c1.p), 'coding state 1')
coding_state2 = State(DiscreteDistribution(c2.p), 'coding state 2')
post = State(DiscreteDistribution(equal_distribution), name='post')
model = HiddenMarkovModel('coding_to_stop')
stop_data = classify(matrixStop, 2)
stop_states = sequence_state_factory(stop_data, 'stop')
model.add_state(coding_state0)
model.add_state(coding_state1)
model.add_state(coding_state2)
add_sequence(model, stop_states)
model.add_state(post)
model.add_transition(model.start, coding_state1, 1)
model.add_transition(coding_state0, coding_state1, 1)
model.add_transition(coding_state1, coding_state2, 1)
model.add_transition(coding_state2, coding_state0, 0.6)
model.add_transition(coding_state2, stop_states[0], 0.4)
model.add_transition(stop_states[-1], post, 1)
model.add_transition(post, post, 0.9)
model.add_transition(post, model.end, 0.1)
model.bake()
with open('../data extractors/exons_end_start_2.txt') as in_file:
not_ok += 1
print(oks / (oks + not_ok))
back = State(DiscreteDistribution(equal_distribution), name='back')
back2 = State(DiscreteDistribution(equal_distribution), name='back2')
matrixZE = numpy.array(matrix_from_exa('../data extractors/starts.exa'))
start_states_data = classify(matrixZE, 2)
start_states = sequence_state_factory(start_states_data, 'start zone')
model = HiddenMarkovModel()
model.add_state(back)
model.add_state(back2)
add_sequence(model, start_states)
model.add_transition(model.start, back, 1)
model.add_transition(back, back, 0.55)
model.add_transition(back, start_states[0], 0.45)
model.add_transition(start_states[-1], back2, 1)
model.add_transition(back2, back2, 0.5)
model.bake()
def train_and_test():
test(model)
lines = []
with open('../data extractors/train_start2.exa') as fi:
tata_states = sequence_state_factory(tata_data, 'tata') post_tata_var_spacers = spacer_states_maker(16, no_coding.p, 'post_tata_var_spacer') post_tata_spacers = spacer_states_maker(4, no_coding.p, 'post_tata_spacer') inr_states = sequence_state_factory(inr_data, 'inr') no_inr_states = sequence_state_factory(no_inr_data, 'no inr') # Add States promoter_utr_model.add_state(back) # Add Sequences #GC add_sequence(promoter_utr_model, gc_states) add_sequence(promoter_utr_model, post_gc_spacers_tata) add_variable_length_sequence(promoter_utr_model, post_gc_var_spacers_tata, post_gc_spacers_tata[0]) add_sequence(promoter_utr_model, post_gc_spacers_tss) add_variable_length_sequence(promoter_utr_model, post_gc_var_spacers_tss, post_gc_spacers_tss[0]) add_sequence(promoter_utr_model, inr_states) add_sequence(promoter_utr_model, no_inr_states) # CAAT add_sequence(promoter_utr_model, cat_states) add_sequence(promoter_utr_model, post_cat_spacers_tss) add_variable_length_sequence(promoter_utr_model, post_cat_var_spacers_tss, post_cat_spacers_tss[0])
coding_state0 = State(DiscreteDistribution(c0.p), 'coding state 0')
coding_state1 = State(DiscreteDistribution(c1.p), 'coding state 1')
coding_state2 = State(DiscreteDistribution(c2.p), 'coding state 2')
donor0_data = classify(matrixDonor0, 2)
donor0_states = sequence_state_factory(donor0_data, 'donor0')
post = State(DiscreteDistribution(equal_distribution), name='post')
model = HiddenMarkovModel('codiing to donor')
model.add_state(coding_state0)
model.add_state(coding_state1)
model.add_state(coding_state2)
add_sequence(model, donor0_states)
model.add_state(post)
model.add_transition(model.start, coding_state0, 1)
model.add_transition(coding_state0, coding_state1, 0.6)
model.add_transition(coding_state0, donor0_states[0], 0.4)
model.add_transition(coding_state1, coding_state2, 0.6)
model.add_transition(coding_state1, donor0_states[0], 0.4)
model.add_transition(coding_state2, coding_state0, 0.6)
model.add_transition(coding_state2, donor0_states[0], 0.4)
model.add_transition(donor0_states[-1], post, 1)
donor_states = sequence_state_factory(donor0_data, 'donor0')
acceptor_states = sequence_state_factory(acceptor0_data, 'acceptor0')
intron_spacer_states = spacer_states_maker(10, no_coding_dist, 'intron spacer')
utr_model = HiddenMarkovModel('utr_model')
# States
exon_state = State(DiscreteDistribution(calculator.utr_exon_5('mcutsa.txt').p),
name='utr exon')
intron_state = State(DiscreteDistribution(no_coding_dist), name='utr intron')
utr_model.add_model(promoter_model)
utr_model.add_state(exon_state)
utr_model.add_state(intron_state)
add_sequence(utr_model, donor_states)
add_sequence(utr_model, acceptor_states)
add_sequence(utr_model, intron_spacer_states)
utr_model.add_transition(utr_model.start, get_state(promoter_model, 'back'), 1)
utr_model.add_transition(get_state(promoter_model, 'inr7'), exon_state, 1)
utr_model.add_transition(get_state(promoter_model, 'no inr7'), exon_state, 1)
utr_model.add_transition(exon_state, exon_state, 0.7)
utr_model.add_transition(exon_state, donor_states[0], 0.2)
utr_model.add_transition(exon_state, utr_model.end, 0.1)
utr_model.add_transition(donor_states[-1], intron_state, 1)
utr_model.add_transition(intron_state, intron_state, 0.5)
utr_model.add_transition(intron_state, intron_spacer_states[0], 0.5)
