def evaluate_gap_position(
L_mer,
gap_index,
sequences,
bg_L_mer_scores,
pssm_scores,
comp_pssm_scores,
options
):
"Evaluate a k-mer with a gap in a particular position."
gap_dist = nucleo_dist_from_mer(L_mer, options.pseudo_count_for_L_mer_scoring, gap_index=gap_index)
gap_pssm = hmm.calculate_log_scores(gap_dist)
gap_comp_pssm = hmm.calculate_complementary_scores(gap_pssm)
gap_pssm_scores = hmm.max_scores_in_sequences(gap_pssm, sequences, bg_L_mer_scores)
gap_comp_pssm_scores = hmm.max_scores_in_sequences(gap_comp_pssm, sequences, bg_L_mer_scores)
max_scores_per_pssm = numpy.array(
[
pssm_scores,
comp_pssm_scores,
gap_pssm_scores,
gap_comp_pssm_scores
]
)
best_scores = max_scores_per_pssm.max(axis=0)
best_scores[best_scores<0.] = 0. # when we didn't find sites, ignore
score = best_scores.sum() / len(L_mer) / len(best_scores)
logging.debug(
'Evaluated: %s; gap: %d; score: %f',
numpy_to_seq(L_mer),
gap_index,
score
)
return score
def evaluate_L_mer(L_mer, sequences, bg_L_mer_scores, gap_positions, options):
dist = nucleo_dist_from_mer(
L_mer,
options.pseudo_count_for_L_mer_scoring,
gap_index=None
)
pssm = hmm.calculate_log_scores(dist)
comp_pssm = hmm.calculate_complementary_scores(pssm)
pssm_scores = hmm.max_scores_in_sequences(pssm, sequences, bg_L_mer_scores)
comp_pssm_scores = hmm.max_scores_in_sequences(comp_pssm, sequences, bg_L_mer_scores)
return max(
(
evaluate_gap_position(
L_mer,
gap_index,
sequences,
bg_L_mer_scores,
pssm_scores,
comp_pssm_scores,
options
),
gap_index
)
for gap_index in gap_positions
)
def evaluate_initialisation_K_mer(self, K_mer, sequences):
"""
Find out how good a starting point this K_mer would be.
"""
pssms = self.pssms_for_K_mer(K_mer)
max_scores_per_pssm = numpy.array([hmm.max_scores_in_sequences(pssm, sequences) for pssm in pssms])
best_scores = max_scores_per_pssm.max(axis=0)
null_score = (self.K + 1) * math.log(0.25)
best_scores[best_scores < null_score] = null_score
return best_scores.sum()
N.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 4]))
logging.info('Scoring sequence')
logging.info(hmm.score_sequence(pssm_scores, seq))
logging.info('Scoring sequence with complementary scores')
logging.info(hmm.score_sequence(comp_scores, seq))
logging.info(hmm.max_score_in_sequence(pssm_scores, seq))
logging.info(hmm.max_score_in_sequence(comp_scores, seq))
long_seqs = [R.random_integers(0, 4, size=10000) for i in xrange(100)]
long_seqs_preprocessed = hmm.preprocess_sequences(long_seqs)
logging.info('Starting to time max scores on long sequences.')
start = time.time()
max_scores = hmm.max_scores_in_sequences(pssm_scores,
long_seqs_preprocessed)
max_comp_scores = hmm.max_scores_in_sequences(comp_scores,
long_seqs_preprocessed)
logging.info(
'Max scores (and complementary scores) over sequences took %f secs' %
(time.time() - start))
long_seq = R.random_integers(0, 4, size=1000000)
long_seq_preprocessed = hmm.preprocess_sequence(long_seq)
logging.info('Starting to time max scores on long sequence.')
start = time.time()
logging.info(hmm.max_score_in_sequence(pssm_scores, long_seq_preprocessed))
logging.info(hmm.max_score_in_sequence(comp_scores, long_seq_preprocessed))
logging.info(
'Max scores (and complementary scores) for %d bases took %f secs' %
logging.info('Calculating complementary scores')
comp_scores = hmm.calculate_complementary_scores(pssm_scores)
logging.info('Preprocessing sequence')
seq = hmm.preprocess_sequence(N.array([0,1,2,3,0,1,2,3,0,1,2,3,0,1,2,3,4]))
logging.info('Scoring sequence')
logging.info(hmm.score_sequence(pssm_scores, seq))
logging.info('Scoring sequence with complementary scores')
logging.info(hmm.score_sequence(comp_scores, seq))
logging.info(hmm.max_score_in_sequence(pssm_scores, seq))
logging.info(hmm.max_score_in_sequence(comp_scores, seq))
long_seqs = [R.random_integers(0,4,size=10000) for i in xrange(100)]
long_seqs_preprocessed = hmm.preprocess_sequences(long_seqs)
logging.info('Starting to time max scores on long sequences.')
start = time.time()
max_scores = hmm.max_scores_in_sequences(pssm_scores, long_seqs_preprocessed)
max_comp_scores = hmm.max_scores_in_sequences(comp_scores, long_seqs_preprocessed)
logging.info('Max scores (and complementary scores) over sequences took %f secs' % (time.time()-start))
long_seq = R.random_integers(0,4,size=1000000)
long_seq_preprocessed = hmm.preprocess_sequence(long_seq)
logging.info('Starting to time max scores on long sequence.')
start = time.time()
logging.info(hmm.max_score_in_sequence(pssm_scores, long_seq_preprocessed))
logging.info(hmm.max_score_in_sequence(comp_scores, long_seq_preprocessed))
logging.info('Max scores (and complementary scores) for %d bases took %f secs' % (len(long_seq), time.time()-start))
