streampile

Forward-only pileups of coordinate-sorted SAM, BAM, and CRAM, and the alleles at every base.

Installation

pip install streampile

Quickstart

Building Pileups

A StreamingPileupBuilder reads records once and piles them up at the 0-based positions you ask for, moving forward only. Give the AlignmentFile threads to decompress the BAM with, which pays at depth; for CRAM, also give it reference_filename.

>>> from pysam import AlignmentFile
>>> from streampile import StreamingPileupBuilder
>>>
>>> with (
...     AlignmentFile("tests/data/reads.bam", threads=4) as reads,
...     StreamingPileupBuilder(reads, min_base_quality=30) as builder,
... ):
...     first = builder.pileup("chr1", 10)
...     second = builder.pileup("chr1", 12)
>>>
>>> first.unfiltered_depth, first.filtered_depth, first.bases
(4, 4, ['A', 'T', 'G', 'A'])
>>> second.unfiltered_depth, second.filtered_depth, second.bases
(4, 3, ['G', 'G', 'G'])

At position 12, the base of lowqual is under the quality floor, so it counts toward unfiltered_depth only.

Filter reads with read_filter, and see each template once with templates(), which calls the bases of overlapping mates into one as fgbio's CallOverlappingConsensusBases does. Overlapping mates read the same bases of their template twice: they agree where they read the same base, and disagree where they read different ones. The mates of pair overlap from position 45 to 54, agree on T at position 50, and disagree at position 52, where the first reads G at Q40 and the second A at Q20:

position  40   45   50   55
pair  99  GATCGGATCCTAGGC
pair 147       GATCCTAAGCTAGCT
                    ^ ^

By default, an agreeing base is called at the sum of the two qualities, and a disagreeing one as the base of the higher quality at their difference:

>>> with (
...     AlignmentFile("tests/data/reads.bam") as reads,
...     StreamingPileupBuilder(reads, read_filter=lambda read: read.is_paired) as builder,
... ):
...     agreeing = builder.pileup("chr1", 50)
...     disagreeing = builder.pileup("chr1", 52)
>>>
>>> for pileup in (agreeing, disagreeing):
...     template = pileup.templates()[0]
...     print(pileup.bases, pileup.qualities, template.base, template.qual)
['T', 'T'] [40, 40] T 80
['G', 'A'] [40, 20] G 20

Other strategies mask a disagreement, or keep the base of the higher quality as it is:

>>> from streampile import DisagreementStrategy
>>>
>>> for strategy in DisagreementStrategy:
...     template = disagreeing.templates(disagreement=strategy)[0]
...     print(strategy, template.base, template.qual)
consensus G 20
mask_both N 2
mask_lower_qual G 40

A mate whose base is under the quality floor does not vote, as it is left out of bases, so it neither masks nor lowers the other mate's base:

>>> with (
...     AlignmentFile("tests/data/reads.bam") as reads,
...     StreamingPileupBuilder(reads, min_base_quality=30) as builder,
... ):
...     floored = builder.pileup("chr1", 52)
>>>
>>> template = floored.templates(disagreement=DisagreementStrategy.mask_both)[0]
>>> floored.bases, template.base, template.qual
(['G'], 'G', 40)

A template also holds its reads, its strand, and its distances to both of its ends:

>>> template = disagreeing.templates()[0]
>>> template.is_reverse, template.five_prime_distance, template.template_end_distance
(False, 12, 7)

Each entry also counts the template's bases from its base to the read's 5′ end and to the template's other end, the 5′ end of its mate in an FR pair, whose CIGAR comes from the MC tag:

>>> with (
...     AlignmentFile("tests/data/reads.bam") as reads,
...     StreamingPileupBuilder(reads) as builder,
... ):
...     pileup = builder.pileup("chr1", 45)
>>>
>>> for entry in pileup.pileups:
...     flag = entry.alignment.flag
...     print(flag, entry.five_prime_distance, entry.template_end_distance)
0 5 None
99 5 14
147 14 5

The unpaired read has no template end, and the two mates of the pair mirror each other. A read has one only when is_fr_pair calls its pair FR, as htsjdk 5.0.0 does: the forward read's aligned 5′ position is at or before the reverse read's.

Tapping Every Record

The function given as tap receives every record, in input order, once the builder has moved past it, so a record can be changed, e.g. tagged, while it is piled up. Each entry's alignment is the very record read, so pass tap=writer.write to write the changed records to an AlignmentFile opened with template=reads. Pileups see each read as it was when the builder read it; changes made after that, including in read_filter, reach tap and alignment but not later pileups. Here every read is written to a new BAM, and a read whose base at position 10 is not the reference A is tagged XV with that base.

>>> from tempfile import mkdtemp
>>>
>>> tagged = f"{mkdtemp()}/tagged.bam"
>>> with (
...     AlignmentFile("tests/data/reads.bam") as reads,
...     AlignmentFile(tagged, "wb", template=reads) as writer,
...     StreamingPileupBuilder(reads, tap=writer.write) as builder,
... ):
...     for entry in builder.pileup("chr1", 10).pileups:
...         if entry.base not in (None, "A"):
...             entry.alignment.set_tag("XV", entry.base)
>>>
>>> with AlignmentFile(tagged) as written:
...     for read in written:
...         if read.has_tag("XV"):
...             print(read.query_name, read.get_tag("XV"))
snv T
mnv G

Sweeping a Territory

The columns method yields the pileup at every position of a span. Its views, such as bases, qualities, and the depths, are computed in Rust, so prefer them to looping over pileups in Python, which is where the time goes at depth.

>>> from collections import Counter
>>>
>>> with (
...     AlignmentFile("tests/data/reads.bam") as reads,
...     StreamingPileupBuilder(reads, min_base_quality=30) as builder,
... ):
...     for pileup in builder.columns("chr1", 9, 13):
...         print(pileup.reference_pos, dict(Counter(pileup.bases)))
9 {'A': 4}
10 {'A': 2, 'T': 1, 'G': 1}
11 {'C': 3, 'G': 1}
12 {'G': 3}

Each pileup is a snapshot that outlives the builder moving on.

Tabulating Alleles

The tabulate function counts the reads of every allele at every base of a bedspec Territory. Alleles are normalized VCF alleles at 1-based positions, so they match a VCF by CHROM, POS, REF, and ALT.

>>> from bedspec import Bed3
>>> from bedspec import Territory
>>> from pysam import FastaFile
>>> from streampile import tabulate
>>>
>>> territory = Territory([Bed3("chr1", start=9, end=12)])
>>> with (
...     AlignmentFile("tests/data/reads.bam") as reads,
...     FastaFile("tests/data/reference.fa") as reference,
... ):
...     bases = list(tabulate(reads, reference, territory, min_base_quality=30))
>>>
>>> for base in bases:
...     print(base.pos, base.ref, base.alts, base.alt_reads, base.alt_fwd, base.alt_rev)
10 A () () () ()
11 A ('T', 'GG') (1, 1) (1, 0) (0, 1)
12 C () () () ()

The alt_fwd and alt_rev fields split each allele's reads by strand, and no_calls counts N bases apart from the depth.

Writing a Table

A path ending in .gz is written with BGZF, indexed as it is written when given an index, and metadata adds ##key=value lines above the header:

>>> from pybgzf import IndexFormat
>>> from streampile import TabulationWriter
>>>
>>> table = f"{mkdtemp()}/counts.tsv.gz"
>>> with TabulationWriter.from_path(
...     table, index=IndexFormat.TBI, metadata={"min_base_quality": 30}
... ) as writer:
...     writer.write_all(bases)

Reading a Table

>>> from streampile import TabulationReader
>>>
>>> for base in TabulationReader.from_path(table):
...     print(base.pos, base.depth, base.alts, base.alt_reads)
10 4 () ()
11 4 ('T', 'GG') (1, 1)
12 4 () ()

With the index, a region can be read alone; it is half-open and 0-based, as in BED:

>>> for base in TabulationReader.query(table, "chr1", 10, 11):
...     print(base.pos, base.depth, base.alts, base.alt_reads)
11 4 ('T', 'GG') (1, 1)

Command Line

streampile tabulate \
    --bam tests/data/reads.bam \
    --ref tests/data/reference.fa \
    --intervals tests/data/territory.bed \
    --min-base-quality 30 \
    --min-mapping-quality 20 \
    --out counts.tsv.gz \
    --index tbi

This writes the table compressed with BGZF and its tabix index, counts.tsv.gz.tbi; decompressed, it reads:

##streampile-tabulation=1
##streampile-version=0.2.0
##bam=tests/data/reads.bam
##reference=tests/data/reference.fa
##territory=tests/data/territory.bed
##min_base_quality=30
##min_mapping_quality=20
##exclude_flags=0xf00
#contig	pos	ref	depth	no_calls	ref_reads	ref_fwd	ref_rev	alt_refs	alts	alt_reads	alt_fwd	alt_rev
chr1	21	T	5	0	5	3	2					
chr1	22	G	5	0	5	3	2					
chr1	23	C	4	0	3	2	1	CA	C	1	0	1
chr1	24	A	4	0	3	2	1					

The --threads option sets the threads decompressing the BAM and compressing a BGZF table: by default, the CPUs available, at most 8.

The examples read tests/data/reads.bam, whose reads are named for what they carry, such as plain, snv, mnv, lowqual, insertion, and deletion.

Development and Testing

See the contributing guide for more information.

The streaming design follows the StreamingPileupBuilder of fgbio of which I was also the author.

See NOTICE for official attribution.