def filter_mappings(self):
num_unmapped = 0
num_entirely_genomic = 0
num_nonunique = 0
num_unique = 0
nongenomic_lengths = Counter()
sam_file = pysam.Samfile(self.file_names['accepted_hits'])
region_fetcher = genomes.build_region_fetcher(self.file_names['genome'],
load_references=True,
sam_file=sam_file,
)
extended_sorter = sam.AlignmentSorter(sam_file.references,
sam_file.lengths,
self.file_names['extended'],
)
filtered_sorter = sam.AlignmentSorter(sam_file.references,
sam_file.lengths,
self.file_names['extended_filtered'],
)
extended_mappings = (trim.extend_polyA_end(mapping, region_fetcher) for mapping in sam_file)
mapping_groups = utilities.group_by(extended_mappings, lambda m: m.qname)
with extended_sorter, filtered_sorter:
for qname, group in mapping_groups:
for m in group:
extended_sorter.write(m)
min_nongenomic_length = min(trim.get_nongenomic_length(m) for m in group)
nongenomic_lengths[min_nongenomic_length] += 1
if min_nongenomic_length == 0:
num_entirely_genomic += 1
continue
nonunique = len(group) > 1 or any(m.mapq < 40 for m in group)
if nonunique:
num_nonunique += 1
continue
num_unique += 1
for m in group:
filtered_sorter.write(m)
self.summary.extend(
[('Mapped with no non-genomic A\'s', num_entirely_genomic),
('Nonunique', num_nonunique),
('Unique', num_unique),
],
)
nongenomic_lengths = utilities.counts_to_array(nongenomic_lengths)
self.write_file('nongenomic_lengths', nongenomic_lengths)
def process_remapped(self):
clean_bam = pysam.Samfile(self.file_names['remapped_accepted_hits'])
type_shape = (self.max_read_length + 1,
self.max_read_length,
fastq.MAX_EXPECTED_QUAL + 1,
6,
6,
)
type_counts = np.zeros(type_shape, int)
remapped_length_counts = Counter()
region_fetcher = genomes.build_region_fetcher(self.file_names['genome'],
load_references=True,
sam_file=clean_bam,
)
for mapping in clean_bam:
trimmed_from_start = trim.trim_mismatches_from_start(mapping,
region_fetcher,
type_counts,
)
# Add back any genomic A's that were trimmed as part of mappings and
# any remaining A's from the first non-genomic onward as soft clipped
# bases for visualization in IGV.
extended = trim.extend_polyA_end(trimmed_from_start,
region_fetcher,
trimmed_twice=True,
)
if not extended.is_unmapped and not extended.is_secondary:
remapped_length_counts[extended.qlen] += 1
yield extended
remapped_lengths = self.zero_padded_array(remapped_length_counts)
self.write_file('lengths', {'remapped': remapped_lengths})
def filter_mappings(self):
num_unmapped = 0
num_entirely_genomic = 0
num_nonunique = 0
num_unique = 0
nongenomic_lengths = Counter()
sam_file = pysam.Samfile(self.file_names['accepted_hits'])
region_fetcher = genomes.build_region_fetcher(
self.file_names['genome'],
load_references=True,
sam_file=sam_file,
)
extended_sorter = sam.AlignmentSorter(
sam_file.references,
sam_file.lengths,
self.file_names['extended'],
)
filtered_sorter = sam.AlignmentSorter(
sam_file.references,
sam_file.lengths,
self.file_names['extended_filtered'],
)
extended_mappings = (trim.extend_polyA_end(mapping, region_fetcher)
for mapping in sam_file)
mapping_groups = utilities.group_by(extended_mappings,
lambda m: m.qname)
with extended_sorter, filtered_sorter:
for qname, group in mapping_groups:
for m in group:
extended_sorter.write(m)
min_nongenomic_length = min(
trim.get_nongenomic_length(m) for m in group)
nongenomic_lengths[min_nongenomic_length] += 1
if min_nongenomic_length == 0:
num_entirely_genomic += 1
continue
nonunique = len(group) > 1 or any(m.mapq < 40 for m in group)
if nonunique:
num_nonunique += 1
continue
num_unique += 1
for m in group:
filtered_sorter.write(m)
self.summary.extend([
('Mapped with no non-genomic A\'s', num_entirely_genomic),
('Nonunique', num_nonunique),
('Unique', num_unique),
], )
nongenomic_lengths = utilities.counts_to_array(nongenomic_lengths)
self.write_file('nongenomic_lengths', nongenomic_lengths)
def combine_mappings(self):
num_unmapped = 0
num_five_unmapped = 0
num_three_unmapped = 0
num_nonunique = 0
num_discordant = 0
num_concordant = 0
five_prime_mappings = pysam.Samfile(self.file_names['five_prime_accepted_hits'])
five_prime_unmapped = pysam.Samfile(self.file_names['five_prime_unmapped'])
all_five_prime = sam.merge_by_name(five_prime_mappings, five_prime_unmapped)
five_prime_grouped = utilities.group_by(all_five_prime, lambda m: m.qname)
three_prime_mappings = pysam.Samfile(self.file_names['three_prime_accepted_hits'])
three_prime_unmapped = pysam.Samfile(self.file_names['three_prime_unmapped'])
all_three_prime = sam.merge_by_name(three_prime_mappings, three_prime_unmapped)
three_prime_grouped = utilities.group_by(all_three_prime, lambda m: m.qname)
group_pairs = izip(five_prime_grouped, three_prime_grouped)
alignment_sorter = sam.AlignmentSorter(five_prime_mappings.references,
five_prime_mappings.lengths,
self.file_names['combined_extended'],
)
region_fetcher = genomes.build_region_fetcher(self.file_names['genome'],
load_references=True,
sam_file=five_prime_mappings,
)
with alignment_sorter:
for (five_qname, five_group), (three_qname, three_group) in group_pairs:
five_annotation = trim.PayloadAnnotation.from_identifier(five_qname)
three_annotation = trim.PayloadAnnotation.from_identifier(three_qname)
if five_annotation['original_name'] != three_annotation['original_name']:
# Ensure that the iteration through pairs is in sync.
print five_qname, three_qname
raise ValueError
five_unmapped = any(m.is_unmapped for m in five_group)
three_unmapped = any(m.is_unmapped for m in three_group)
if five_unmapped:
num_five_unmapped += 1
if three_unmapped:
num_three_unmapped += 1
if five_unmapped or three_unmapped:
num_unmapped += 1
continue
five_nonunique = len(five_group) > 1 or any(m.mapq < 40 for m in five_group)
three_nonunique = len(three_group) > 1 or any(m.mapq < 40 for m in three_group)
if five_nonunique or three_nonunique:
num_nonunique += 1
continue
five_m = five_group.pop()
three_m = three_group.pop()
five_strand = '-' if five_m.is_reverse else '+'
three_strand = '-' if three_m.is_reverse else '+'
tlen = max(five_m.aend, three_m.aend) - min(five_m.pos, three_m.pos)
discordant = (five_m.tid != three_m.tid) or (five_strand) != (three_strand) or (tlen > 10000)
if discordant:
num_discordant += 1
continue
if five_strand == '+':
first_read = five_m
second_read = three_m
elif five_strand == '-':
first_read = three_m
second_read = five_m
gap = second_read.pos - first_read.aend
if gap < 0:
num_discordant += 1
continue
combined_read = pysam.AlignedRead()
# qname needs to come from three_m to include trimmed As
combined_read.qname = three_m.qname
combined_read.tid = five_m.tid
combined_read.seq = first_read.seq + second_read.seq
combined_read.qual = first_read.qual + second_read.qual
combined_read.cigar = first_read.cigar + [(3, gap)] + second_read.cigar
combined_read.pos = first_read.pos
combined_read.is_reverse = first_read.is_reverse
combined_read.mapq = min(first_read.mapq, second_read.mapq)
combined_read.rnext = -1
combined_read.pnext = -1
num_concordant += 1
extended_mapping = trim.extend_polyA_end(combined_read,
region_fetcher,
)
alignment_sorter.write(extended_mapping)
self.summary.extend(
[('Unmapped', num_unmapped),
('Five prime unmapped', num_five_unmapped),
('Three prime unmapped', num_three_unmapped),
('Nonunique', num_nonunique),
('Discordant', num_discordant),
('Concordant', num_concordant),
],
)
