Pysam

Alignment Files: SAM, BAM, and CRAM

This reference targets pysam 0.24.1. All numeric coordinates shown here are 0-based, half-open.

Open Modes and Handles

import pysam

with pysam.AlignmentFile("input.bam", "rb", threads=4) as alignments:
    print(alignments.references)

For CRAM, supply the exact reference where possible:

with pysam.AlignmentFile(
    "input.cram",
    "rc",
    reference_filename="GRCh38.fa",
    threads=4,
) as alignments:
    ...

AlignmentFile can use a path or a real file object that exposes fileno(). In-memory objects such as io.BytesIO are not supported by HTSlib. Use "-" for stdin/stdout. When an existing file object is accepted, duplicate_filehandle=True (the default) prevents pysam from closing the caller's descriptor.

Useful constructor options:

• index_filename=: nonstandard, remote, or separately named index

• require_index=True: fail early if random access is required

• reference_filename=: CRAM reference FASTA

• threads=: compression/decompression threads

• format_options=["key=value"]: HTSlib format options

• ignore_truncation=True: downgrade a missing BGZF EOF marker to a warning; do not combine with threads > 1

Headers and Index State

AlignmentFile.header is an AlignmentHeader, not a plain dictionary.

with pysam.AlignmentFile("input.bam", "rb") as bam:
    header_dict = bam.header.to_dict()
    header_text = str(bam.header)
    contigs = dict(zip(bam.references, bam.lengths))
    has_index = bam.has_index()

Use check_index() when a missing index should be an error. It raises for SAM, closed files, or unusable indexes. get_index_statistics() exposes per-contig mapped/unmapped counts recorded in an available index; these are index statistics, not a fresh scan of every record. CRAI does not store these counts: CRAM mapped, unmapped, nocoordinate, and index-statistic values return zero and must not be interpreted as an empty CRAM. Use a sequential scan for counts.

Iteration Choices

Indexed region query

with pysam.AlignmentFile("input.bam", "rb") as bam:
    for read in bam.fetch("chr1", 1_000, 2_000):
        ...

• Requires BAI/CSI for BAM or CRAI for CRAM.

• Returns reads overlapping the interval, including reads that start before it or end after it.

• Records are returned in coordinate/index order.

• BAM/CRAM fetch() with no region requires an index and visits placed records by reference. Placed unmapped records can appear in both this and region queries; test read.is_unmapped. Unplaced reads need sequential scanning.

• SAM fetch() without a region is sequential; SAM region access is unsupported by AlignmentFile even though standalone samtools can index BGZF SAM.

Sequential scan

with pysam.AlignmentFile("input.bam", "rb") as bam:
    for read in bam.fetch(until_eof=True):
        ...

• Does not require an index.

• Starts at the current file position.

• Preserves file order.

• Includes unplaced unmapped records.

fetch("*") requests only unplaced unmapped records at the end of a coordinate-sorted alignment file.

Multiple active iterators

multiple_iterators belongs to fetch(), not the AlignmentFile constructor:

with pysam.AlignmentFile("input.bam", "rb") as bam:
    chr1_reads = bam.fetch("chr1", multiple_iterators=True)
    chr2_reads = bam.fetch("chr2", multiple_iterators=True)

Each such iterator reopens the file and has overhead. Prefer one ordered pass when possible.

AlignedSegment Essentials

Identity and sequence

• query_name

• query_sequence

• query_qualities: numeric Phred scores, or None

• query_length

• query_alignment_sequence: query bases participating in the alignment

• query_alignment_qualities

• get_forward_sequence() / get_forward_qualities(): original sequencer orientation of the retained sequence (hard-clipped bases cannot be recovered)

Assigning query_sequence invalidates query_qualities. Save and reassign qualities after changing sequence.

Reference placement

• reference_name

• reference_id

• reference_start: 0-based inclusive

• reference_end: 0-based exclusive; derived from CIGAR

• mapping_quality

• next_reference_name, next_reference_start

• template_length

Many placement-derived properties are None or sentinel values for unmapped or CIGAR-less records. Check is_unmapped before using them.

Flags

Common boolean properties:

• pairing: is_paired, is_proper_pair, is_read1, is_read2

• orientation: is_reverse, mate_is_reverse

• mapping: is_unmapped, mate_is_unmapped

• status: is_secondary, is_supplementary, is_qcfail, is_duplicate

For a typical primary mapped-read filter:

def keep_primary(read: pysam.AlignedSegment) -> bool:
    return (
        not read.is_unmapped
        and not read.is_secondary
        and not read.is_supplementary
        and not read.is_qcfail
        and not read.is_duplicate
        and read.mapping_quality >= 20
    )

State whether supplementary alignments and duplicates are intentionally excluded; there is no universal filter for every analysis. MAPQ 255 denotes unavailable quality, and the numeric threshold above keeps it. Exclude it explicitly when the assay requires measured MAPQ. Proper-pair flags describe the aligner's criteria, not independent proof of a valid biological fragment.

CIGAR Operations and Alignment Geometry

cigartuples stores (operation, length), the reverse of textual SAM CIGAR notation.

Prefer enum members in new code. Top-level aliases such as pysam.CMATCH remain in 0.24 for compatibility but are expected to be removed in a future release.

Useful geometry methods:

blocks = read.get_blocks()  # M/=/X blocks; D/N gaps, I can split adjacent blocks
pairs = read.get_aligned_pairs(matches_only=False, with_cigar=True)
reference_positions = read.get_reference_positions(full_length=True)

get_aligned_pairs(with_seq=True) needs an MD tag and returns reference bases derived from that tag. It does not consult a separately opened FASTA.

Optional Tags and Modified Bases

if read.has_tag("NM"):
    edit_distance = read.get_tag("NM")

read.set_tag("XX", 7, value_type="i")
all_tags = read.get_tags(with_value_type=True)

Use standard tags according to the SAM tags specification. Avoid changing alignment-derived tags such as NM/MD without recomputing them.

For base modifications encoded by MM/ML:

for (canonical_base, strand, modification), calls in (
    read.modified_bases or {}
).items():
    for query_position, quality in calls:
        probability_lower_bound = None if quality < 0 else quality / 256.0

The key is (canonical base, strand, modification), where strand is 0 forward or 1 reverse. pysam 0.24 removed the earlier five-modification-type limit and fixed crashes on degenerate empty MM calls. ML values are quantized: quality / 256 is a lower bound, not an exact posterior. These query positions refer to stored SEQ; use modified_bases_forward with get_forward_sequence() when the original orientation is required.

Counting and Coverage

Record count

with pysam.AlignmentFile("input.bam", "rb") as bam:
    raw_overlap_count = bam.count("chr1", 1_000, 2_000)
    filtered_count = bam.count(
        "chr1",
        1_000,
        2_000,
        read_callback="all",
    )

count() defaults to read_callback="nofilter". "all" excludes reads with unmapped, secondary, QC-fail, or duplicate flags. It does not accept a quality= argument. Use a callback for custom record filtering:

count = bam.count(
    "chr1",
    1_000,
    2_000,
    read_callback=lambda read: keep_primary(read),
)

A/C/G/T base coverage

a, c, g, t = bam.count_coverage(
    "chr1",
    1_000,
    2_000,
    quality_threshold=20,
    read_callback="all",
)
depth = [sum(values) for values in zip(a, c, g, t)]

For 0 <= start < stop <= contig_length, the result has stop - start positions, including zero coverage. Validate these bounds: an end past the contig is clipped. In 0.24.1, the array allocation does not derive its span from region= or the end= alias; use numeric contig, start, and stop for this method. Only A/C/G/T bases count. Deletions, reference skips, and ambiguous query bases do not contribute. Overlapping mates count separately; this method does not apply pileup BAQ or pair-overlap adjustments.

Pileup Semantics

with pysam.FastaFile("reference.fa") as fasta, pysam.AlignmentFile(
    "input.bam", "rb"
) as bam:
    iterator = bam.pileup(
        "chr1",
        1_000,
        2_000,
        truncate=True,
        stepper="samtools",
        fastafile=fasta,
        min_mapping_quality=20,
        min_base_quality=20,
        max_depth=100_000,
        ignore_overlaps=True,
        ignore_orphans=True,
    )
    for column in iterator:
        for pileup_read in column.pileups:
            if pileup_read.is_del or pileup_read.is_refskip:
                continue
            query_position = pileup_read.query_position
            base = pileup_read.alignment.query_sequence[query_position]

Key defaults and behaviors:

• Without truncate=True, columns outside the requested interval can appear when overlapping reads extend beyond the interval.

• stepper="all" filters unmapped, secondary, QC-fail, and duplicate reads.

• stepper="nofilter" disables the read callback's flag/MAPQ filtering; base-quality thresholds, the depth cap, and overlap handling still apply.

• stepper="samtools" applies samtools-style processing; provide fastafile for full BAQ/reference behavior.

• Default min_base_quality is 13.

• Default max_depth is 8000.

• Paired overlap detection and orphan filtering are enabled by default.

• nsegments precedes the base-quality filter. Despite its name, get_num_aligned() also counts deletion/reference-skip entries that pass the quality check in 0.24.1. For observed base depth, count column.pileups entries with not is_del, not is_refskip, and a non-None query_position.

• Overlap handling adjusts base qualities; with min_base_quality=0, a zero-quality mate can reappear. It does not universally remove one record.

Consume each PileupColumn before advancing the iterator: its backing buffer changes on advance, not only on iterator destruction. Copy primitive values needed later. For concurrent CRAM pileups open separate file handles; multiple_iterators=True is not implemented for CRAM pileup.

For SNP support, inspect base and quality. For insertions/deletions, use PileupRead.indel, deletion/refskip state, CIGAR, and normalized alleles. Single-base counting is not an indel caller.

Writing Alignments

Preserve an input header

with pysam.AlignmentFile("input.bam", "rb") as source, pysam.AlignmentFile(
    "filtered.bam", "wb", template=source, threads=4
) as destination:
    for read in source.fetch(until_eof=True):
        if keep_primary(read):
            destination.write(read)

The output retains input order and @SQ, @RG, @PG, and optional tags. Keep the reference order intact: record reference IDs are numeric. Check each RG tag against a defined @RG ID and its intended sample before combining files. Per-record filters can drop one mate; they do not repair flags, mate coordinates, TLEN, or fetch a mate outside a requested region. Only index coordinate-sorted output with unmapped records in valid positions.

Construct a new record

header = pysam.AlignmentHeader.from_dict(
    {
        "HD": {"VN": "1.6", "SO": "coordinate"},
        "SQ": [{"SN": "chr1", "LN": 248_956_422}],
    }
)

with pysam.AlignmentFile("new.bam", "wb", header=header) as output:
    read = pysam.AlignedSegment(output.header)
    read.query_name = "read001"
    read.query_sequence = "ACGTACGTAA"
    read.flag = 0
    read.reference_id = output.get_tid("chr1")
    read.reference_start = 100
    read.mapping_quality = 60
    read.cigartuples = [(pysam.CIGAR_OPS.CMATCH, 10)]
    read.query_qualities = pysam.qualitystring_to_array("IIIIIIIIII")
    output.write(read)

Construct records against the destination header so numeric reference IDs map correctly. Sequence length, qualities, and query-consuming CIGAR operations must agree.

Validation

For BAM/CRAM:

pysam.samtools.quickcheck("-v", "filtered.bam")
pysam.samtools.index("filtered.bam", catch_stdout=False)

quickcheck checks basic headers and EOF markers, not biological correctness. Reopen the file, verify header/reference compatibility, and inspect expected regions. Read cram_and_performance.md for CRAM-specific validation.