def test_fix_overlap_pair_opposite_directions(self):
"""Account for RBS buffer on both strands.
"""
overlap_start_pos = 21
feature_1_seq = 'AAACCCGGGTTTCCCAAACCCATGTTTAAAGGGTTTCCC'
feature_2_seq = (feature_1_seq[overlap_start_pos:] +
'CCCTTTGGGAAACCCAAACCCGGGTTTCCCAAATTTAAA')
whole_seq = feature_1_seq[:overlap_start_pos] + feature_2_seq
overlap_size = len(feature_1_seq) - overlap_start_pos
# Sanity check.
self.assertEqual(
len(whole_seq),
len(feature_1_seq) + len(feature_2_seq) - overlap_size)
# Create the sequence.
seq = Seq(whole_seq, generic_dna)
seq_record = SeqRecord(seq)
feature_id_to_seq_map = {
1: feature_1_seq,
2: feature_2_seq,
}
feature_1_loc = FeatureLocation(0, len(feature_1_seq), strand=-1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id=1)
seq_record.features.append(feature_1)
self._assert_feature_seq(feature_1, seq_record, feature_id_to_seq_map)
feature_2_loc = FeatureLocation(overlap_start_pos,
overlap_start_pos + len(feature_2_seq),
strand=1)
feature_2 = SeqFeature(feature_2_loc, type='CDS', id=2)
seq_record.features.append(feature_2)
self._assert_feature_seq(feature_2, seq_record, feature_id_to_seq_map)
updated_seq_record = copy.deepcopy(seq_record)
is_fix_success = fix_overlap_pair(feature_1.id, feature_2.id,
updated_seq_record)
self.assertTrue(is_fix_success)
result_seq = str(updated_seq_record.seq)
EXPECTED_SEQUENCE = (
feature_1_seq +
feature_2_seq[overlap_size:overlap_size +
conflicting_pair_common.RBS_BUFFER_SIZE] +
feature_1_seq[overlap_start_pos - conflicting_pair_common.
RBS_BUFFER_SIZE:overlap_start_pos] + feature_2_seq)
self.assertEqual(EXPECTED_SEQUENCE, result_seq)
new_feature_1 = get_feature_by_id(updated_seq_record, feature_1.id)
new_feature_2 = get_feature_by_id(updated_seq_record, feature_2.id)
self.assertEqual(
new_feature_1.location.end +
2 * conflicting_pair_common.RBS_BUFFER_SIZE,
new_feature_2.location.start)
self._assert_feature_seq(new_feature_1, updated_seq_record,
feature_id_to_seq_map)
self._assert_feature_seq(new_feature_2, updated_seq_record,
feature_id_to_seq_map)
def test_fix_overlap_pair_same_direction_forward(self):
"""Account for RBS buffer for right strand.
"""
overlap_start_pos = 39
feature_1_seq = 'ATGTTTGGGAAACCCAAACCCGGGTTTAAACCCGGGTTTATGAAAGGG'
feature_2_seq = feature_1_seq[overlap_start_pos:] + 'CCCAAATTT'
whole_seq = feature_1_seq[:overlap_start_pos] + feature_2_seq
seq = Seq(whole_seq, generic_dna)
seq_record = SeqRecord(seq)
feature_id_to_seq_map = {
1: feature_1_seq,
2: feature_2_seq,
}
feature_1_loc = FeatureLocation(0, len(feature_1_seq), strand=1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id=1)
seq_record.features.append(feature_1)
self._assert_feature_seq(feature_1, seq_record, feature_id_to_seq_map)
feature_2_loc = FeatureLocation(overlap_start_pos,
overlap_start_pos + len(feature_2_seq),
strand=1)
feature_2 = SeqFeature(feature_2_loc, type='CDS', id=2)
seq_record.features.append(feature_2)
self._assert_feature_seq(feature_2, seq_record, feature_id_to_seq_map)
updated_seq_record = copy.deepcopy(seq_record)
is_fix_success = fix_overlap_pair(feature_1.id, feature_2.id,
updated_seq_record)
self.assertTrue(is_fix_success)
EXPECTED_SEQUENCE = (
feature_1_seq +
feature_1_seq[overlap_start_pos - conflicting_pair_common.
RBS_BUFFER_SIZE:overlap_start_pos] + feature_2_seq)
self.assertEqual(EXPECTED_SEQUENCE, str(updated_seq_record.seq))
new_feature_1 = get_feature_by_id(updated_seq_record, feature_1.id)
new_feature_2 = get_feature_by_id(updated_seq_record, feature_2.id)
self.assertEqual(
new_feature_1.location.end +
conflicting_pair_common.RBS_BUFFER_SIZE,
new_feature_2.location.start)
self._assert_feature_seq(new_feature_1, updated_seq_record,
feature_id_to_seq_map)
self._assert_feature_seq(new_feature_2, updated_seq_record,
feature_id_to_seq_map)
def test_fix_overlaps_simple(self):
feature_1_seq = 'ATGTTTGGG'
feature_2_seq = 'GGGCCCAAAGTA'
overlap_start_pos = 6
whole_seq = feature_1_seq[:overlap_start_pos] + feature_2_seq
overlap_size = len(feature_1_seq) - overlap_start_pos
seq = Seq(whole_seq, generic_dna)
seq_record = SeqRecord(seq)
feature_id_to_seq_map = {
1: feature_1_seq,
2: feature_2_seq,
}
feature_1_loc = FeatureLocation(0, len(feature_1_seq), strand=1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id=1)
seq_record.features.append(feature_1)
self._assert_feature_seq(feature_1, seq_record, feature_id_to_seq_map)
feature_2_loc = FeatureLocation(overlap_start_pos,
overlap_start_pos + len(feature_2_seq),
strand=-1)
feature_2 = SeqFeature(feature_2_loc, type='CDS', id=2)
seq_record.features.append(feature_2)
self._assert_feature_seq(feature_2, seq_record, feature_id_to_seq_map)
# Build and use the overlap fixer.
updated_seq_record = copy.deepcopy(seq_record)
refactor_context = RefactorContext(updated_seq_record)
refactor_context.set_forbidden_codon_set(set(['GGG']))
cpf = ConflictingPairFixer(refactor_context)
cpf.fix_overlaps()
EXPECTED_SEQUENCE = feature_1_seq + feature_2_seq
self.assertEqual(EXPECTED_SEQUENCE, str(updated_seq_record.seq))
new_feature_1 = get_feature_by_id(updated_seq_record, feature_1.id)
new_feature_2 = get_feature_by_id(updated_seq_record, feature_2.id)
self.assertEqual(new_feature_1.location.end,
new_feature_2.location.start)
self._assert_feature_seq(new_feature_1, updated_seq_record,
feature_id_to_seq_map)
self._assert_feature_seq(new_feature_2, updated_seq_record,
feature_id_to_seq_map)
def test_add_feature_to_seq_record__regular(self):
SEQ = Seq('ATGTTTGGGTAGAGTA', generic_dna)
seq_record = SeqRecord(SEQ)
FEATURE_1_ID = '1'
FEATURE_1_LOC = FeatureLocation(4, 7)
feature_1 = SeqFeature(FEATURE_1_LOC, type='CDS', id=FEATURE_1_ID)
add_feature_to_seq_record(seq_record, feature_1)
lookup_feature_1 = get_feature_by_id(seq_record, FEATURE_1_ID)
self.assertEqual(feature_1, lookup_feature_1)
def test_add_feature_to_seq_record__regular(self):
SEQ = Seq('ATGTTTGGGTAGAGTA', generic_dna)
seq_record = SeqRecord(SEQ)
FEATURE_1_ID = '1'
FEATURE_1_LOC = FeatureLocation(4, 7)
feature_1 = SeqFeature(FEATURE_1_LOC, type='CDS', id=FEATURE_1_ID)
add_feature_to_seq_record(seq_record, feature_1)
lookup_feature_1 = get_feature_by_id(seq_record, FEATURE_1_ID)
self.assertEqual(feature_1, lookup_feature_1)
def test_fix_overlaps_third_feature_none_overlap(self):
feature_1_seq = 'ATGTTTGGG'
feature_2_seq = 'GGGCCCAAAGTA'
inter_f2_f3_junk = 'GTAGCTATCTATCTGGTTAAATC'
feature_3_seq = 'ATGAAACCCTTTGGGTTTCCCAAA'
overlap_start_pos = 6
whole_seq = (feature_1_seq[:overlap_start_pos] + feature_2_seq +
inter_f2_f3_junk + feature_3_seq)
overlap_size = len(feature_1_seq) - overlap_start_pos
seq = Seq(whole_seq, generic_dna)
seq_record = SeqRecord(seq)
feature_id_to_seq_map = {
1: feature_1_seq,
2: feature_2_seq,
3: feature_3_seq,
}
feature_1_loc = FeatureLocation(0, len(feature_1_seq), strand=1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id=1)
seq_record.features.append(feature_1)
self._assert_feature_seq(feature_1, seq_record, feature_id_to_seq_map)
feature_2_loc = FeatureLocation(overlap_start_pos,
overlap_start_pos + len(feature_2_seq),
strand=-1)
feature_2 = SeqFeature(feature_2_loc, type='CDS', id=2)
seq_record.features.append(feature_2)
self._assert_feature_seq(feature_2, seq_record, feature_id_to_seq_map)
feature_3_start = feature_2_loc.end + len(inter_f2_f3_junk)
feature_3_end = feature_3_start + len(feature_3_seq)
feature_3_loc = FeatureLocation(feature_3_start,
feature_3_end,
strand=1)
feature_3 = SeqFeature(feature_3_loc, type='CDS', id=3)
seq_record.features.append(feature_3)
self._assert_feature_seq(feature_3, seq_record, feature_id_to_seq_map)
# Build and use the overlap fixer.
updated_seq_record = copy.deepcopy(seq_record)
refactor_context = RefactorContext(updated_seq_record)
refactor_context.set_forbidden_codon_set(set(['GGG']))
cpf = ConflictingPairFixer(refactor_context)
cpf.fix_overlaps()
EXPECTED_SEQUENCE = (feature_1_seq + feature_2_seq + inter_f2_f3_junk +
feature_3_seq)
self.assertEqual(EXPECTED_SEQUENCE, str(updated_seq_record.seq))
for feature_id in feature_id_to_seq_map.keys():
new_feature = get_feature_by_id(updated_seq_record, feature_id)
self._assert_feature_seq(new_feature, updated_seq_record,
feature_id_to_seq_map)
def check_rnas_conserved(original_seq_record,
refactored_seq_record,
ignore_problems_in_feature_ids=[],
interval=None):
"""Check that RNA coding sequences are conserved.
"""
print '...Checking RNA\'s are conserved...'
RNA_TYPES = set(['misc_RNA', 'ncRNA', 'rRNA', 'tRNA', 'tmRNA'])
# Filter original features to those in interval, if provided.
if interval:
interval_features = [
f for f in original_seq_record.features
if does_interval_overlap_feature(interval, f)
]
else:
interval_features = original_seq_record.features
original_rna_features = filter(
lambda feature: feature.type in RNA_TYPES and not feature.id in
ignore_problems_in_feature_ids, interval_features)
if interval:
refactored_interval_features = [
f for f in refactored_seq_record.features
if does_interval_overlap_feature(interval, f)
]
else:
refactored_interval_features = refactored_seq_record.features
refactored_rna_features = filter(
lambda feature: feature.type in RNA_TYPES and not feature.id in
ignore_problems_in_feature_ids, refactored_interval_features)
error_msg = "Different number of RNA features."
assert len(original_rna_features) == len(
refactored_rna_features), error_msg
for original_feature in original_rna_features:
original_feature_seq = original_feature.extract(
original_seq_record.seq)
refactored_feature = get_feature_by_id(refactored_seq_record,
original_feature.id)
refactored_feature_seq = refactored_feature.extract(
refactored_seq_record.seq)
error_msg = "RNA not conserved for %s" % original_feature.id
assert str(original_feature_seq) == str(
refactored_feature_seq), error_msg
print '......RNA conservation confirmed.'
return True
def analyze_variation(original_record, recoded_record):
"""Analyzes the variation between the two records.
The start and recoded records must have the same feature ids.
Returns:
Dictionary with the following keys:
* codon_similarity: Proportion of codons unchanged.
* based_similarity: Proportion of bases unchanged.
"""
total_codons = 0
reassigned_codons = 0
total_bases = 0
reassigned_bases = 0
original_record_coding_features = [
feature for feature in original_record.features
if feature.type == 'CDS'
]
for orig_feature in original_record_coding_features:
recoded_feature = get_feature_by_id(recoded_record, orig_feature.id)
orig_seq = orig_feature.extract(original_record.seq)
recoded_seq = recoded_feature.extract(recoded_record.seq)
if not len(orig_seq) == len(recoded_seq):
print '>>>> Omitting ' + str(orig_feature)
continue
total_bases += len(orig_feature)
total_codons += len(orig_feature) / 3
for codon_index in range(0, len(orig_seq), 3):
orig_codon = str(orig_seq[codon_index:codon_index + 3])
recoded_codon = str(recoded_seq[codon_index:codon_index + 3])
if orig_codon == recoded_codon:
continue
else:
reassigned_codons += 1
reassigned_bases += _num_differing_bases(
orig_codon, recoded_codon)
return {
'total_codons': total_codons,
'reassigned_codons': reassigned_codons,
'codon_similarity':
float(total_codons - reassigned_codons) / total_codons,
'total_bases': total_bases,
'reassigned_bases': reassigned_bases,
'base_similarity': float(total_bases - reassigned_bases) / total_bases
}
def test_fix_overlap_pair_opposing_strands(self):
"""This should simply pull them apart, without adding anything in
between.
"""
seq = Seq('ATGTTTGGGCCCAAAGTA', generic_dna)
seq_record = SeqRecord(seq)
feature_1_seq = 'ATGTTTGGG'
feature_2_seq = 'GGGCCCAAAGTA'
feature_id_to_seq_map = {
1: feature_1_seq,
2: feature_2_seq,
}
feature_1_loc = FeatureLocation(0, 9, strand=1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id=1)
seq_record.features.append(feature_1)
self._assert_feature_seq(feature_1, seq_record, feature_id_to_seq_map)
feature_2_loc = FeatureLocation(6, 18, strand=-1)
feature_2 = SeqFeature(feature_2_loc, type='CDS', id=2)
seq_record.features.append(feature_2)
self._assert_feature_seq(feature_2, seq_record, feature_id_to_seq_map)
updated_seq_record = copy.deepcopy(seq_record)
is_fix_success = fix_overlap_pair(feature_1.id, feature_2.id,
updated_seq_record)
self.assertTrue(is_fix_success)
self.assertEqual('ATGTTTGGGGGGCCCAAAGTA', str(updated_seq_record.seq))
new_feature_1 = get_feature_by_id(updated_seq_record, feature_1.id)
new_feature_2 = get_feature_by_id(updated_seq_record, feature_2.id)
self.assertEqual(new_feature_1.location.end,
new_feature_2.location.start)
self._assert_feature_seq(new_feature_1, updated_seq_record,
feature_id_to_seq_map)
self._assert_feature_seq(new_feature_2, updated_seq_record,
feature_id_to_seq_map)
def check_translations_conserved(original_seq_record,
refactored_seq_record,
interval=None):
"""Confirms that the translations of the coding features are preserved
before and after.
"""
print '...Checking translation is conserved...'
original_coding_features = _get_features_passing_interval_filter(
original_seq_record, interval)
original_coding_features = filter(lambda feature: feature.type == 'CDS',
original_coding_features)
refactored_coding_features = _get_features_passing_interval_filter(
refactored_seq_record, interval)
refactored_coding_features = filter(lambda feature: feature.type == 'CDS',
refactored_coding_features)
error_msg = "Different number of CDS features."
assert len(original_coding_features) == len(
refactored_coding_features), error_msg
for original_feature in original_coding_features:
if original_feature.id in TRANSLATION_CONSERVED_EXCEPTIONS:
continue
original_feature_seq = original_feature.extract(
original_seq_record.seq)
original_translation = translate_custom(str(original_feature_seq))
refactored_feature = get_feature_by_id(refactored_seq_record,
original_feature.id)
if not refactored_feature:
raise AssertionError("Feature lost after refactor: %s" %
str(original_feature))
refactored_feature_seq = refactored_feature.extract(
refactored_seq_record.seq)
try:
refactored_translation = translate_custom(
str(refactored_feature_seq))
except TranslationError as e:
print "Error translating %s" % str(original_feature)
raise e
error_msg = "Translation mismatch for feature %s" % original_feature.id
assert original_translation == refactored_translation, error_msg
print '......Translation conservation confirmed.'
# All tests passed.
return True
def resolve_single_homology_issue(refactor_context, homology_pair_obj):
"""Resolves homology issues between two objects.
Args:
refactor_context: The RefactorContext whose SeqRecord will be mutated.
homology_pair_obj: Object produced by
find_features_to_check_for_homology(). This is not guaranteed to
have all the correct keys as of the current implementation.
"""
genome_record = refactor_context.get_genome_record()
# The homology_pair_obj passed in may not have all the necessary keys,
# for example if it's the type of copy we don't know how to deal with yet,
# so for now we just try-catch any KeyError and report that the homology
# was not fixed.
try:
copy_id = homology_pair_obj['copy_id']
copy_seq = homology_pair_obj['copy_seq']
print 'Resolving homology for %s ...' % copy_id
# Parse the details of the feature to modify from homology_pair_obj.
feature_to_modify_id = homology_pair_obj['source_id']
first_codon_to_modify = homology_pair_obj[
'source_seq_first_codon_index']
source_feature = get_feature_by_id(genome_record, feature_to_modify_id)
source_feature_seq = source_feature.extract(genome_record.seq)
num_codons = len(source_feature) / 3
avoid_codons_in_positions = {}
for codon_index in range(first_codon_to_modify, num_codons):
codon = str(source_feature_seq[codon_index * 3:codon_index * 3 +
3])
avoid_codons_in_positions[codon_index] = codon
# Perform the fix.
result = replace_codons_in_single_feature(
refactor_context,
feature_to_modify_id,
start_codon_index=first_codon_to_modify,
avoid_codons_in_positions=avoid_codons_in_positions)
assert str(result['orig_feature_seq']) != str(
result['new_feature_seq'])
assert result['is_success'], "Resolving homology not successful."
update_seq_record_feature(genome_record, feature_to_modify_id, result)
# Homology fixed.
return True
except KeyError:
return False
def generate_recoding_stats(original_genome_record, recoded_genome_record,
outfile):
"""Generate stats related to recoding.
"""
# We only use CDS features for this stat.
original_coding_features = filter(lambda feature: feature.type == 'CDS',
original_genome_record.features)
total_coding_features = len(original_coding_features)
total_codons = 0
num_codons_recoded = 0
percent_recoded_list = []
for idx, feature in enumerate(original_coding_features):
print 'Analyzing feature %s, %d of %d' % (feature.id, idx + 1,
total_coding_features)
recoded_feature = get_feature_by_id(recoded_genome_record, feature.id)
if recoded_feature is None:
continue
orig_seq = feature.extract(original_genome_record).seq
recoded_seq = recoded_feature.extract(recoded_genome_record).seq
codons_recoded_for_feature = 0
feature_len = len(orig_seq)
for codon_index in range(0, len(orig_seq), 3):
total_codons += 1
original_codon = str(orig_seq[codon_index:codon_index + 3])
recoded_codon = str(recoded_seq[codon_index:codon_index + 3])
if original_codon != recoded_codon:
codons_recoded_for_feature += 1
feature_codons = feature_len / 3
feature_percent_recoded = (float(codons_recoded_for_feature) /
feature_codons)
percent_recoded_list.append(feature_percent_recoded)
num_codons_recoded += codons_recoded_for_feature
percent_recoded = float(num_codons_recoded) / total_codons
avg_percent_recoded = (float(sum(percent_recoded_list)) /
len(percent_recoded_list))
data = {
'total_CDS_features': total_coding_features,
'total_codons': total_codons,
'num_codons_recoded': num_codons_recoded,
'proportion_recoded': percent_recoded,
'avg_proportion_recoded': avg_percent_recoded
}
with open(outfile, 'w') as fh:
fh.write(json.dumps(data, indent=4, separators=(',', ': ')))
def main():
source_ids_to_muddle = []
with open(AGN_DEBUG_FILE) as agn_debug_fh:
reader = csv.DictReader(agn_debug_fh)
for row in reader:
if row['Separate'] == '1':
source_ids_to_muddle.append(row['ID'])
print source_ids_to_muddle
record = get_genome_record(RECODED_PATH)
refactor_context = RefactorContext(record)
for source_feature_id in source_ids_to_muddle:
source_feature = get_feature_by_id(record, source_feature_id)
muddle_end(source_feature, record, refactor_context, 20)
# rbs_cp_features = [feature for feature in record.features if
# feature.type == InsertType.FIX_OVERLAP_RBS_COPY]
# overlap_head_cp_features = [feature for feature in record.features if
# feature.type == InsertType.FIX_OVERLAP_HEAD_COPY]
# print 'rbs', len(rbs_cp_features)
# print 'head', len(overlap_head_cp_features)
# head_cp_feature_ids = set()
# for head_feature in overlap_head_cp_features:
# source_feature_id = re.match(r'(?P<feature_id>.*)_' + InsertType.FIX_OVERLAP_HEAD_COPY, head_feature.id).group('feature_id')
# head_cp_feature_ids.add(source_feature_id)
# count = 0
# for rbs_cp_feature in rbs_cp_features:
# downstream = False
# match = re.match(r'(?P<feature_id>.*)_upstream_' + InsertType.FIX_OVERLAP_RBS_COPY, rbs_cp_feature.id)
# if not match:
# downstream = True
# match = re.match(r'(?P<feature_id>.*)_downstream_' + InsertType.FIX_OVERLAP_RBS_COPY, rbs_cp_feature.id)
# source_feature_id = match.group('feature_id')
# if source_feature_id in head_cp_feature_ids:
# print 'HAS HEAD_CP', source_feature_id
# continue
# source_feature = get_feature_by_id(record, source_feature_id)
# num_part = re.match(r'.*_(?P<num>[0-9]+)', source_feature_id).group('num')
# if source_feature.strand == 1:
# assert source_feature.location.start > rbs_cp_feature.location.start
# actual_source_id = ID_ROOT + str(int(num_part) - 1)
# else:
# assert source_feature.location.start < rbs_cp_feature.location.start
# actual_source_id = ID_ROOT + str(int(num_part) + 1)
# try:
# actual_source_feature = get_feature_by_id(record, actual_source_id)
# except:
# print 'NOT MUDDLING', rbs_cp_feature.id
# continue
# if actual_source_feature.strand != rbs_cp_feature.strand:
# print 'NOT MUDDLING', rbs_cp_feature.id
# continue
# print 'MUDDLING', rbs_cp_feature.id
# muddle_end(actual_source_feature, record, refactor_context, len(rbs_cp_feature))
# count += 1
# print 'COUNT', count
with open(OUTFILE, 'w') as fh:
SeqIO.write(record, fh, 'genbank')
def __init__(self, feature_id, original_seq_record, **kwargs):
"""Default constructor.
Args:
feature_id: The string id of the feature to profile.
original_seq_record: The SeqRecord object with that contains all the
data.
kwargs: Dictionary of any of the following keys
* error_tolerance
* error_inc
* value_range
"""
self.original_seq_record = original_seq_record
self.feature = biopython_util.get_feature_by_id(
self.original_seq_record, feature_id)
self.original_feature_seq = str(
self.feature.extract(self.original_seq_record.seq))
self.computation_cache = {}
self.failed_error_treshold_count = 0
if 'error_tolerance' in kwargs:
self.error_tolerance = kwargs['error_tolerance']
if 'error_inc' in kwargs:
self.error_inc = kwargs['error_inc']
if 'value_range' in kwargs:
self.value_range = kwargs['value_range']
if 'sliding_window_size' in kwargs:
self.sliding_window_size = kwargs['sliding_window_size']
if self.feature.strand == 1:
self.polarity_aware_underlying_seq = self.original_seq_record.seq
self.polarity_aware_feature_location_start = (
self.feature.location.start)
self.polarity_aware_feature_location_end = (
self.feature.location.end)
elif self.feature.strand == -1:
self.polarity_aware_underlying_seq = (
self.original_seq_record.seq.reverse_complement())
total_len = len(self.polarity_aware_underlying_seq)
self.polarity_aware_feature_location_start = (
total_len - self.feature.location.end)
self.polarity_aware_feature_location_end = (
self.polarity_aware_feature_location_start + len(self.feature))
else:
raise ValueError("No feature strand.")
# Some basic validation.
self._validate()
# Set or compute the original score profile.
if 'values' in kwargs:
num_codons = len(self.feature) / 3
self.values = kwargs['values']
assert num_codons == len(
self.values), ("Wrong number of "
"values passed to FeatureProfile constructor.")
else:
self.compute_original_feature_values()
def handle_prfB(genome_record):
"""Modifies the genome record to make prfB a CDS and remove
the frameshift.
"""
prfB_locus_tag = 'ECMDS42_2390'
prfB_feature = get_feature_by_id(genome_record, prfB_locus_tag)
prfB_feature_seq = str(prfB_feature.extract(genome_record.seq))
original_length = len(prfB_feature)
# Feature is on the negative strand.
assert prfB_feature.strand == -1
# First delete the frameshift, CTTT -> CTT, by removing the T at the 75th
# position of the feature.
FRAMESHIFT_START = 72
assert 'CTTTGAC' == str(
prfB_feature_seq[FRAMESHIFT_START:FRAMESHIFT_START + 7])
prfB_feature_seq = prfB_feature_seq[:74] + prfB_feature_seq[75:]
assert 'CTTGAC' == str(prfB_feature_seq[FRAMESHIFT_START:FRAMESHIFT_START +
6])
# Next change the RBS just before it AGGGGG -> CGTGGG (as per Chris Gregg
# data from 8/25/13).
RBS_START = 63
assert 'AGGGGG' == str(prfB_feature_seq[63:69])
prfB_feature_seq = prfB_feature_seq[:63] + 'CGT' + prfB_feature_seq[66:]
assert 'CGTGGG' == str(prfB_feature_seq[63:69])
# Now replace the underlying sequence of the genome.
updated_seq = (genome_record.seq[:prfB_feature.location.start] +
reverse_complement(prfB_feature_seq) +
genome_record.seq[prfB_feature.location.end:])
assert len(genome_record.seq) - 1 == len(updated_seq)
genome_record.seq = updated_seq
# Change the prfB feature to be a CDS.
prfB_feature.type = 'CDS'
# Bump the start position one unit right.
# Remember, it's on the negative strand.
prfB_feature.location = FeatureLocation(prfB_feature.location.start,
prfB_feature.location.end - 1,
strand=prfB_feature.strand)
# Update the positions of downstream features.
updated_features = []
for feature in genome_record.features:
if feature.location.start > prfB_feature.location.start:
feature = feature._shift(-1)
updated_features.append(feature)
genome_record.features = updated_features
# Make sure changes went through.
mod_prfB_feature = get_feature_by_id(genome_record, prfB_locus_tag)
mod_prfB_feature_seq = str(mod_prfB_feature.extract(genome_record.seq))
assert original_length - 1 == len(mod_prfB_feature)
assert prfB_feature_seq[:6] == mod_prfB_feature_seq[:6], (
"Before: %s, After: %s" %
(prfB_feature_seq[:6], mod_prfB_feature_seq[:6]))
assert prfB_feature_seq[-10:] == mod_prfB_feature_seq[-10:], (
"Before: %s, After: %s" %
(prfB_feature_seq[-10:], mod_prfB_feature_seq[10:]))
return genome_record
def test_fix_overlap_pair_same_direction_reverse(self):
"""Account for RBS buffer for left strand.
"""
overlap_start_pos = 12
feature_1_seq = 'AAACCCGGGTTTCCCAAAGTA'
feature_2_seq = (feature_1_seq[overlap_start_pos:] +
'CCCTTTGGGAAACCCAAACCCGGGTTTCCCAAATTTGTA')
whole_seq = feature_1_seq[:overlap_start_pos] + feature_2_seq
overlap_seq = feature_1_seq[overlap_start_pos:]
overlap_size = len(overlap_seq)
# Sanity check, for visual debug if necessary.
self.assertEqual(
'AAACCCGGGTTTCCCAAAGTACCCTTTGGGAAACCCAAACCCGGGTTTCCCAAATTTGTA',
whole_seq)
self.assertEqual(
len(whole_seq),
len(feature_1_seq) + len(feature_2_seq) - overlap_size)
# Create the sequence.
seq = Seq(whole_seq, generic_dna)
seq_record = SeqRecord(seq)
feature_id_to_seq_map = {
1: feature_1_seq,
2: feature_2_seq,
}
feature_1_loc = FeatureLocation(0, len(feature_1_seq), strand=-1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id=1)
seq_record.features.append(feature_1)
self._assert_feature_seq(feature_1, seq_record, feature_id_to_seq_map)
feature_2_loc = FeatureLocation(overlap_start_pos,
overlap_start_pos + len(feature_2_seq),
strand=-1)
feature_2 = SeqFeature(feature_2_loc, type='CDS', id=2)
seq_record.features.append(feature_2)
self._assert_feature_seq(feature_2, seq_record, feature_id_to_seq_map)
updated_seq_record = copy.deepcopy(seq_record)
is_fix_success = fix_overlap_pair(feature_1.id, feature_2.id,
updated_seq_record)
self.assertTrue(is_fix_success)
result_seq = str(updated_seq_record.seq)
EXPECTED_SEQUENCE = (
feature_1_seq +
feature_2_seq[overlap_size:overlap_size +
conflicting_pair_common.RBS_BUFFER_SIZE] +
feature_2_seq)
# Sanity check, for visual debug if necessary.
self.assertEqual(
'AAACCCGGGTTTCCCAAAGTA'
'CCCTTTGGGAAACCC'
'CCCAAAGTACCCTTTGGGAAACCCAAACCCGGGTTTCCCAAATTTGTA',
EXPECTED_SEQUENCE)
# Assert correct length. (Simple debug check).
self.assertEqual(len(EXPECTED_SEQUENCE), len(result_seq))
# Somewhat redundant piece-wise checks from debugging, but might as well
# leave them here for the future.
# Assert the entirety of feature_1 is copied over.
self.assertEqual(feature_1_seq, result_seq[:len(feature_1_seq)])
# Assert the RBS extension is properly copied over
self.assertEqual(
EXPECTED_SEQUENCE[len(feature_1_seq):len(feature_1_seq) +
conflicting_pair_common.RBS_BUFFER_SIZE],
result_seq[len(feature_1_seq):len(feature_1_seq) +
conflicting_pair_common.RBS_BUFFER_SIZE])
# Assert the entirety of feature_2 is copied over at the end.
self.assertEqual(feature_2_seq,
result_seq[len(result_seq) - len(feature_2_seq):])
# Assert the whole thing is correct.
self.assertEqual(EXPECTED_SEQUENCE, result_seq)
# Make sure the features are preserved.
new_feature_1 = get_feature_by_id(updated_seq_record, feature_1.id)
new_feature_2 = get_feature_by_id(updated_seq_record, feature_2.id)
self.assertEqual(
new_feature_1.location.end +
conflicting_pair_common.RBS_BUFFER_SIZE,
new_feature_2.location.start)
self._assert_feature_seq(new_feature_1, updated_seq_record,
feature_id_to_seq_map)
self._assert_feature_seq(new_feature_2, updated_seq_record,
feature_id_to_seq_map)
def analyze_codon_motif(motif_set, original_record, recoded_record,
report_file):
"""Find all AGY positions that are are within 30 bases upstream of another
feature.
Args:
motif_set: Set of codons to look for.
"""
AGY_list = []
original_seq = str(original_record.seq).upper()
recoded_seq = str(recoded_record.seq).upper()
# Identify overlapping features (this includes those close enough for RBS)
# to possibly conflict. Use cache.
conflicting_pairs = find_all_overlaps(
None, # genome_record,
None, # forbidden_codons,
cache=True)
original_rbs_profiles = get_mds42_rbs_strength_profile()
for pair in conflicting_pairs:
if (pair['upstream_feature'].type != 'CDS'
or pair['downstream_feature'].type != 'CDS'):
continue
# Ignore prfB.
if (get_feature_gene(pair['upstream_feature']) == 'prfB'
or get_feature_gene(pair['downstream_feature']) == 'prfB'):
continue
original_upstream_feature = get_feature_by_id(
original_record, pair['upstream_feature'].id)
original_downstream_feature = get_feature_by_id(
original_record, pair['downstream_feature'].id)
recoded_upstream_feature = get_feature_by_id(
recoded_record, pair['upstream_feature'].id)
recoded_downstream_feature = get_feature_by_id(
recoded_record, pair['downstream_feature'].id)
if (original_upstream_feature.strand == 1
and original_downstream_feature.strand == 1):
roi_start = original_downstream_feature.location.start - 20
for codon_index in range(0, len(original_upstream_feature), 3):
pos = original_upstream_feature.location.start + codon_index
if pos < roi_start:
continue
else:
codon = original_seq[pos:pos + 3]
if codon in motif_set:
recoded_pos = (
recoded_upstream_feature.location.start +
codon_index)
recoded_codon = recoded_seq[recoded_pos:recoded_pos +
3]
# Get before/after RBS.
orig_rbs_expression = original_rbs_profiles[
original_downstream_feature.id]
recoded_rbs_expression = calc_rbs_score_for_feature(
recoded_downstream_feature,
recoded_seq)['expression']
delta_expression = (recoded_rbs_expression -
orig_rbs_expression)
AGY_list.append({
'pos': pos,
'recoded_pos': recoded_pos,
'ref': codon,
'alt': recoded_codon,
'orig_rbs_expression': orig_rbs_expression,
'recoded_rbs_expression': recoded_rbs_expression,
'delta_expression': delta_expression,
'strand': 1,
})
# elif (original_upstream_feature.strand == -1 and
# original_downstream_feature.strand == -1):
# original_downstream_feature_seq = (
# original_downstream_feature.extract(original_seq))
# recoded_downstream_feature_seq = (
# recoded_downstream_feature.extract(recoded_seq))
# roi_start = original_upstream_feature.location.end + 20
# for codon_index in range(0, len(original_downstream_feature), 3):
# pos = original_downstream_feature.location.end - codon_index
# if pos > roi_start:
# continue
# else:
# codon = original_downstream_feature_seq[
# codon_index:codon_index + 3]
# if codon in AGY_codons:
# recoded_pos = (recoded_downstream_feature.location.start -
# codon_index)
# recoded_codon = recoded_downstream_feature_seq[
# codon_index:codon_index + 3]
# # Get before/after RBS.
# orig_rbs_expression = original_rbs_profiles[
# original_upstream_feature.id]
# recoded_rbs_expression = calc_rbs_score_for_feature(
# recoded_upstream_feature, recoded_seq)[
# 'expression']
# delta_expression = (recoded_rbs_expression -
# orig_rbs_expression)
# AGY_list.append({
# 'pos': pos,
# 'recoded_pos': recoded_pos,
# 'ref': codon,
# 'alt': recoded_codon,
# 'orig_rbs_expression': orig_rbs_expression,
# 'recoded_rbs_expression': recoded_rbs_expression,
# 'delta_expression': delta_expression,
# 'strand': -1
# })
print 'Writing report.'
with open(report_file, 'w') as fh:
FIELD_NAMES = [
'pos',
'recoded_pos',
'ref',
'alt',
'orig_rbs_expression',
'recoded_rbs_expression',
'delta_expression',
'strand',
]
writer = csv.DictWriter(fh, FIELD_NAMES)
writer.writeheader()
for AGY_obj in AGY_list:
writer.writerow(AGY_obj)
def main():
record = get_genome_record(RECODED_PATH)
with open(AGN_DEBUG_FILE) as agn_debug_fh:
reader = csv.DictReader(agn_debug_fh)
for row in reader:
new_codon = row['Codon']
if len(new_codon) != 3:
continue
feature_id = row['ID']
print feature_id
# Identify codon positions from original record.
sequence = row['Sequence']
if len(sequence) < 14:
# Too hard.
continue
orig_codon = sequence[8:11]
if not re.match(r'[A-Z]{3}', orig_codon):
continue
# Identify which codon index the position is.
seq_ending_with_codon = sequence[0:3].upper(
) + sequence[4:7].upper() + orig_codon
print seq_ending_with_codon
orig_feature = get_feature_by_id(MDS42_RECORD, feature_id)
orig_feature_seq = str(orig_feature.extract(MDS42_RECORD.seq))
new_feature = get_feature_by_id(record, feature_id)
new_feature_seq = str(new_feature.extract(record.seq))
# Find the last occurrence.
last_pos = None
for match in re.finditer(seq_ending_with_codon, orig_feature_seq):
last_pos = match.start()
print last_pos
codon_pos = last_pos + 6
print 'expect', orig_feature_seq[codon_pos:codon_pos + 3]
# Translate to codon index.
codon_index = codon_pos / 3
# Make sure codon at that position in recoded record is synonymous.
codon_at_index = new_feature_seq[codon_pos:codon_pos + 3]
print codon_at_index
syn = ORIGINAL_CODON_USAGE_MEMEX.get_synonymous_codons(
codon_at_index)
print syn
assert orig_codon in syn
# Make the change in the target record.
swap_feature_codon_at_position(record, new_feature.id, codon_pos,
codon_at_index, new_codon)
# Maybe do the next one.
maybe_next = row['next'].upper()
if len(maybe_next) == 3:
syn = ORIGINAL_CODON_USAGE_MEMEX.get_synonymous_codons(
maybe_next)
codon_at_index_next = new_feature_seq[codon_pos + 3:codon_pos +
6]
assert codon_at_index_next in syn
swap_feature_codon_at_position(record, new_feature.id,
codon_pos + 3,
codon_at_index_next, maybe_next)
with open(OUTFILE, 'w') as fh:
SeqIO.write(record, fh, 'genbank')