def compute_metanucleotide_counts(read_positions):
left_buffer = 50
right_buffer = 50
# Figure out what lengths were recorded.
length_keys, _, _ = extract_lengths_and_buffers(read_positions)
dinucleotides = list(''.join(pair) for pair in product('TCAG', repeat=2))
features_keys = codons.all_codons# + ['T', 'C', 'A', 'G'] + dinucleotides
metacodon_counts = {features_key: {length: PositionCounts({'feature': 0}, left_buffer, right_buffer)
for length in length_keys
}
for features_key in features_keys}
feature_slice = ('feature', slice(-left_buffer, right_buffer))
for name, read_counts in read_positions.iteritems():
transcript_sequence = read_counts['sequence']
coding_sequence = ''.join(transcript_sequence['start_codon':('stop_codon', 3)])
for c, codon_id in enumerate(codons.codons_from_seq(coding_sequence)):
p = 3 * c
if p >= left_buffer and p <= len(coding_sequence) - right_buffer:
p_slice = ('start_codon', slice(p - left_buffer, p + left_buffer))
for length in length_keys:
counts = read_positions[name][length][p_slice]
metacodon_counts[codon_id][length][feature_slice] += counts
#nucleotide = codon_id[0]
#metacodon_counts[nucleotide][length][feature_slice] += counts
#dinucleotide = codon_id[:2]
#metacodon_counts[dinucleotide][length][feature_slice] += counts
return metacodon_counts
def compute_codon_counts(position_counts, offset_type):
CDS_length = position_counts.values()[0].CDS_length
if CDS_length % 3 != 0:
raise ValueError('CDS length not divisible by 3')
# Note: CDS_length is the index of the first nucleotide of the stop codon.
# Ingolia's original model never has the stop codon in the A site, but
# subsequent data show an accumulation of (typically length 29 or 30) reads
# that do advance this far.
num_codons = CDS_length // 3
landmarks = {
'start_codon': 0,
'stop_codon': num_codons,
}
codon_counts = PositionCounts(landmarks, codon_buffer, codon_buffer)
recorded_lengths = set(position_counts.keys())
known_A_site_lengths = set(A_site_offsets[offset_type].keys())
for length in recorded_lengths & known_A_site_lengths:
A_site_offset = A_site_offsets[offset_type][length]
start_index = -A_site_offset - (codon_buffer * 3)
end_index = CDS_length - A_site_offset + (codon_buffer * 3)
in_frame = slice(start_index, end_index, 3)
one_behind = slice(start_index - 1, end_index - 1, 3)
one_ahead = slice(start_index + 1, end_index + 1, 3)
codon_counts.data += position_counts[length]['start_codon', in_frame] + \
position_counts[length]['start_codon', one_behind] + \
position_counts[length]['start_codon', one_ahead]
sequence_slice = slice(('start_codon', -codon_buffer * 3),
('stop_codon', codon_buffer * 3))
sequence = ''.join(position_counts['sequence'][sequence_slice])
codon_identities_list = list(codons.codons_from_seq(sequence))
codon_identities = PositionCounts(
landmarks,
codon_buffer,
codon_buffer,
data=np.asarray(codon_identities_list),
)
return codon_counts, codon_identities
def compute_metanucleotide_counts(read_positions):
left_buffer = 50
right_buffer = 50
# Figure out what lengths were recorded.
length_keys, _, _ = extract_lengths_and_buffers(read_positions)
dinucleotides = list(''.join(pair) for pair in product('TCAG', repeat=2))
features_keys = codons.all_codons # + ['T', 'C', 'A', 'G'] + dinucleotides
metacodon_counts = {
features_key: {
length: PositionCounts({'feature': 0}, left_buffer, right_buffer)
for length in length_keys
}
for features_key in features_keys
}
feature_slice = ('feature', slice(-left_buffer, right_buffer))
for name, read_counts in read_positions.iteritems():
transcript_sequence = read_counts['sequence']
coding_sequence = ''.join(
transcript_sequence['start_codon':('stop_codon', 3)])
for c, codon_id in enumerate(codons.codons_from_seq(coding_sequence)):
p = 3 * c
if p >= left_buffer and p <= len(coding_sequence) - right_buffer:
p_slice = ('start_codon',
slice(p - left_buffer, p + left_buffer))
for length in length_keys:
counts = read_positions[name][length][p_slice]
metacodon_counts[codon_id][length][feature_slice] += counts
#nucleotide = codon_id[0]
#metacodon_counts[nucleotide][length][feature_slice] += counts
#dinucleotide = codon_id[:2]
#metacodon_counts[dinucleotide][length][feature_slice] += counts
return metacodon_counts
def compute_codon_counts(position_counts, offset_type):
CDS_length = position_counts.values()[0].CDS_length
if CDS_length % 3 != 0:
raise ValueError('CDS length not divisible by 3')
# Note: CDS_length is the index of the first nucleotide of the stop codon.
# Ingolia's original model never has the stop codon in the A site, but
# subsequent data show an accumulation of (typically length 29 or 30) reads
# that do advance this far.
num_codons = CDS_length // 3
landmarks = {'start_codon': 0,
'stop_codon': num_codons,
}
codon_counts = PositionCounts(landmarks, codon_buffer, codon_buffer)
recorded_lengths = set(position_counts.keys())
known_A_site_lengths = set(A_site_offsets[offset_type].keys())
for length in recorded_lengths & known_A_site_lengths:
A_site_offset = A_site_offsets[offset_type][length]
start_index = -A_site_offset - (codon_buffer * 3)
end_index = CDS_length - A_site_offset + (codon_buffer * 3)
in_frame = slice(start_index, end_index, 3)
one_behind = slice(start_index - 1, end_index - 1, 3)
one_ahead = slice(start_index + 1, end_index + 1, 3)
codon_counts.data += position_counts[length]['start_codon', in_frame] + \
position_counts[length]['start_codon', one_behind] + \
position_counts[length]['start_codon', one_ahead]
sequence_slice = slice(('start_codon', -codon_buffer * 3), ('stop_codon', codon_buffer * 3))
sequence = ''.join(position_counts['sequence'][sequence_slice])
codon_identities_list = list(codons.codons_from_seq(sequence))
codon_identities = PositionCounts(landmarks,
codon_buffer,
codon_buffer,
data = np.asarray(codon_identities_list),
)
return codon_counts, codon_identities
