bio-sam-bam-basics

name	bio-sam-bam-basics
description	View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.
origin	openai4s
category	bioskills/alignment-files
metadata	{"tool_type":"cli","primary_tool":"samtools","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: pysam 0.22+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

SAM/BAM/CRAM Basics

"Read a BAM file" -> Open a binary alignment file and iterate over aligned reads with their mapping coordinates, flags, and quality scores.

• Python: pysam.AlignmentFile() (pysam)
• CLI: samtools view (samtools)
• R: scanBam() (Rsamtools)

View and convert alignment files using samtools and pysam.

Format Overview

Format	Description	Use Case
SAM	Text format, human-readable	Debugging, small files
BAM	Binary compressed SAM	Standard storage format
CRAM	Reference-based compression	Long-term archival, smaller than BAM

SAM Format Structure

@HD VN:1.6 SO:coordinate
@SQ SN:chr1 LN:248956422
@RG ID:sample1 SM:sample1
@PG ID:bwa PN:bwa VN:0.7.17
read1  0   chr1  100  60  50M  *  0  0  ACGT...  FFFF...  NM:i:0

Header lines start with @:

• @HD - Header metadata (version, sort order)
• @SQ - Reference sequence dictionary
• @RG - Read group information
• @PG - Program used to create file

Alignment fields (tab-separated):

1. QNAME - Read name
2. FLAG - Bitwise flag
3. RNAME - Reference name
4. POS - 1-based position
5. MAPQ - Mapping quality
6. CIGAR - Alignment description
7. RNEXT - Mate reference
8. PNEXT - Mate position
9. TLEN - Template length
10. SEQ - Read sequence
11. QUAL - Base qualities
12. Optional tags (NM:i:0, MD:Z:50, etc.)

samtools view

View BAM as SAM

samtools view input.bam | head

View with Header

samtools view -h input.bam | head -100

View Header Only

samtools view -H input.bam

View Specific Region

samtools view input.bam chr1:1000-2000

Count Alignments

samtools view -c input.bam

Format Conversion

Goal: Convert between SAM (text), BAM (binary), and CRAM (reference-compressed) alignment formats.

Approach: Use samtools view with format flags (-b for BAM, -C for CRAM, -h for SAM with header). CRAM requires a reference FASTA with -T.

BAM to SAM

samtools view -h -o output.sam input.bam

SAM to BAM

samtools view -b -o output.bam input.sam

BAM to CRAM

samtools view -C -T reference.fa -o output.cram input.bam

CRAM to BAM

samtools view -b -T reference.fa -o output.bam input.cram

Pipe Conversion

samtools view -b input.sam > output.bam

Common Flags

Flag	Decimal	Meaning
0x1	1	Paired
0x2	2	Proper pair
0x4	4	Unmapped
0x8	8	Mate unmapped
0x10	16	Reverse strand
0x20	32	Mate reverse strand
0x40	64	First in pair
0x80	128	Second in pair
0x100	256	Secondary alignment
0x200	512	Failed QC
0x400	1024	PCR duplicate
0x800	2048	Supplementary

Decode Flags (Bidirectional)

# Number to mnemonics
samtools flags 147
# 0x93 147 PAIRED,PROPER_PAIR,REVERSE,READ2

# Mnemonics to number
samtools flags PAIRED,PROPER_PAIR,REVERSE,READ2   # 147

Secondary vs Supplementary (Different Semantics)

Two different concepts that are routinely conflated:

Bit	Name	Meaning	Filter implication
0x100 (256)	Secondary	An alternative candidate alignment for the same read; not the primary location	-F 256 is correct for SNV/indel calling on short reads
0x800 (2048)	Supplementary	A piece of a chimeric/split alignment (the read is split across loci)	Carries SA:Z tag; required by SV callers (Manta, Sniffles, cuteSV, GRIDSS, Delly)

-F 2304 removes both. Strip supplementary only when downstream is small-variant calling; keep supplementary for SV calling, fusion detection, or any analysis that follows split-reads.

MAPQ Is Not Portable Across Aligners

samtools view -q 30 does different things depending on what produced the BAM. MAPQ is an aligner-specific scale, not a universal probability:

Aligner	MAPQ scale	"Unique" sentinel	Common gotcha
BWA-MEM / BWA-MEM2	0-60	60	-q 30 is sensible "high confidence"
minimap2 (DNA / pbmm2)	0-60	60	Spec-compliant
HISAT2	0-60	60	Spec-compliant
Bowtie2	0-42	42 (rare)	-q 60 drops everything; -q 23 is a common "uniquely mapped" convention (not a probabilistic 99% threshold)
STAR	0, 1, 3, 255	255 = uniquely mapped (sentinel, not a quality)	-q 255 for "unique only"; -q 30 accidentally keeps unique only too
DRAGEN	0 to --mapq-max (default 60)	varies	-q 30 still meaningful; distribution shape differs
Cell Ranger / STARsolo	inherits STAR	255	Same trap as STAR

Verify the actual scale of any unfamiliar BAM:

samtools view input.bam | awk '{print $5}' | sort -un | head
samtools view -H input.bam | grep '^@PG' | head -1   # which aligner produced this BAM

0-Based vs 1-Based Coordinates (Footgun)

Context	Coordinate system
SAM text POS	1-based, inclusive
samtools view chr1:100-200	1-based, closed interval
samtools faidx chr1:100-200	1-based, closed interval
BAM binary internal	0-based, half-open
pysam read.reference_start	0-based
bam.fetch('chr1', 100, 200)	0-based, half-open
BED files	0-based, half-open
VCF	1-based
GFF/GTF	1-based, inclusive

samtools view bam chr1:100-200 and bam.fetch('chr1', 100, 200) return different read sets at boundaries.

CIGAR Operations

Op	Description
M	Alignment match (can be mismatch)
I	Insertion to reference
D	Deletion from reference
N	Skipped region (introns in RNA-seq; do NOT count as covered bases)
S	Soft clipping (sequence in SEQ but not aligned)
H	Hard clipping (sequence not in SEQ)
=	Sequence match (explicit)
X	Sequence mismatch (explicit)
P	Padding (rare; multiple-sequence-alignment context)

Example: 50M2I30M = 50 bases match, 2 base insertion, 30 bases match

CIGAR M is overloaded -- it is the union of = and X. Some aligners emit =/X directly (e.g. minimap2 with --eqx); bcftools / Picard often need M and rebuild MD/NM with samtools calmd. N operations break naive coverage calculations: a 1000 bp RNA-seq read with one 50 kb intron does not cover 50 kb. Distinguish soft-clip (S, bases retained) from hard-clip (H, bases discarded -- irreversible).

Context-Specific Tags

Beyond the standard fields, downstream tools depend on optional tags whose presence depends on aligner and assay. Inspect with samtools view input.bam | head -1 | tr '\t' '\n' or pysam read.get_tag('XX').

Tag	Set by	Meaning	Required by
NM:i	bwa, samtools calmd	Edit distance to reference	mapDamage, many filters
MD:Z	bwa, samtools calmd	Mismatch positions (text)	bcftools mpileup BAQ, IGV mismatch coloring
MC:Z	samtools fixmate -m	Mate CIGAR	samtools markdup
ms:i	samtools fixmate -m	Mate score (lowercase per SAMtags)	samtools markdup
RG:Z	aligner from -R	Read group ID	GATK BQSR, MarkDuplicates LB lookup
SA:Z	All split-read aligners	Comma-list of supplementary coords	Sniffles, Manta, cuteSV, GRIDSS, Delly
NH:i	STAR, HISAT2	Number of reported hits	featureCounts multimapper handling, Salmon
HI:i	STAR	Hit index among NH (1-based by default; --outSAMattrIHstart 0 for 0-based)	RSEM
XS:A	STAR (--outSAMstrandField intronMotif), HISAT2	Strand inferred from splice motif	StringTie, Cufflinks
ts:A	minimap2 -ax splice	Transcript strand from splice motif	StringTie
CB:Z	Cell Ranger, STARsolo	Corrected cell barcode	scRNA quantification
UB:Z	Cell Ranger, STARsolo	Corrected UMI	UMI-aware dedup
RX:Z	fgbio AnnotateBamWithUmis	Raw UMI (bulk)	fgbio GroupReadsByUmi
MI:Z	fgbio GroupReadsByUmi	Molecular identifier (UMI group)	CallMolecularConsensusReads, duplex calling
cs:Z	minimap2 --cs	Compact CIGAR-with-bases	paftools, SV tools

Missing tags fail in two modes: silently wrong (featureCounts ignoring multimappers without NH; markdup marking nothing without MC/MS) or loudly (consensus tools rejecting input without MD).

Provenance: @PG Chain

The @PG lines record every tool that touched the BAM, linked through PP (previous program) tags. This is the audit trail.

samtools view -H input.bam | grep '^@PG'

A clean germline pipeline:

@PG ID:bwa-mem PN:bwa VN:0.7.17
@PG ID:samtools.1 PN:samtools VN:1.20 PP:bwa-mem CL:samtools sort
@PG ID:samtools.2 PN:samtools VN:1.20 PP:samtools.1 CL:samtools fixmate
@PG ID:samtools.3 PN:samtools VN:1.20 PP:samtools.2 CL:samtools markdup

A broken/missing chain (no PP, unknown tools, gaps) means the BAM cannot be reliably reproduced. Production pipelines often reject inputs without a complete chain.

CRAM Reference Resolution (Critical)

CRAM stores reads relative to a reference; without it, the file is unreadable. htslib resolves the reference in this order:

1. Command-line -T ref.fa / --reference
2. REF_CACHE env var (local MD5-named cache; searched before REF_PATH)
3. REF_PATH env var (colon-separated; each element matched by the @SQ M5: MD5). A remote server such as EBI ENA is consulted only if its URL is present here -- it was the built-in default through htslib 1.21, but that default was removed in 1.22 to reduce EBI load, so modern htslib does no network lookup unless that URL is added explicitly.
4. Local file named in the @SQ UR: header tag (local / file:// paths only; http/ftp URIs in UR: are ignored)

On HPC nodes without internet, populate a local cache once:

mkdir -p $HOME/cram_cache
seq_cache_populate.pl -root $HOME/cram_cache reference.fa
export REF_CACHE=$HOME/cram_cache/%2s/%2s/%s
export REF_PATH=$REF_CACHE   # local only; no network/ENA lookup

samtools quickcheck -v file.cram   # header + EOF only
samtools view -c file.cram          # forces full decode; proves reference reachable

CRAM can be made irreversibly lossy, but the archive profile is NOT how: --output-fmt-option archive is a lossless maximum-compression preset (fqzcomp quality codec, name tokenization, larger slices) that does not alter bases or qualities. Irreversible loss comes instead from explicit quality binning (e.g. Illumina 8-bin), which must be applied deliberately and is harmful for low-coverage / somatic / forensic / archival data. Convert against the exact reference the BAM was aligned to (matched by @SQ M5:); a different reference silently corrupts bases on read-back.

pysam Python Alternative

Goal: Read and manipulate alignment data programmatically in Python.

Approach: Use pysam.AlignmentFile to open BAM/CRAM files, iterate over reads, and access properties like coordinates, flags, CIGAR, and tags.

Open and Iterate

import pysam

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for read in bam:
        print(f'{read.query_name}\t{read.reference_name}:{read.reference_start}')

Access Header

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for sq in bam.header['SQ']:
        print(f'{sq["SN"]}: {sq["LN"]} bp')

Read Alignment Properties

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for read in bam:
        print(f'Name: {read.query_name}')
        print(f'Flag: {read.flag}')
        print(f'Chrom: {read.reference_name}')
        print(f'Pos: {read.reference_start}')  # 0-based
        print(f'MAPQ: {read.mapping_quality}')
        print(f'CIGAR: {read.cigarstring}')
        print(f'Seq: {read.query_sequence}')
        print(f'Qual: {read.query_qualities}')
        break

Check Flag Properties

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for read in bam:
        if read.is_paired and read.is_proper_pair:
            if read.is_reverse:
                strand = '-'
            else:
                strand = '+'
            print(f'{read.query_name} on {strand} strand')

Fetch Region

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for read in bam.fetch('chr1', 1000, 2000):
        print(read.query_name)

Convert BAM to SAM

with pysam.AlignmentFile('input.bam', 'rb') as infile:
    with pysam.AlignmentFile('output.sam', 'w', header=infile.header) as outfile:
        for read in infile:
            outfile.write(read)

Convert to CRAM

with pysam.AlignmentFile('input.bam', 'rb') as infile:
    with pysam.AlignmentFile('output.cram', 'wc', reference_filename='reference.fa', header=infile.header) as outfile:
        for read in infile:
            outfile.write(read)

Quick Reference

Task	samtools	pysam
View BAM	samtools view file.bam	AlignmentFile('file.bam', 'rb')
View header	samtools view -H file.bam	bam.header
Count reads	samtools view -c file.bam	sum(1 for _ in bam)
Get region	samtools view file.bam chr1:1-1000	bam.fetch('chr1', 0, 1000)
BAM to SAM	samtools view -h -o out.sam in.bam	Open with 'w' mode
SAM to BAM	samtools view -b -o out.bam in.sam	Open with 'wb' mode
BAM to CRAM	samtools view -C -T ref.fa -o out.cram in.bam	Open with 'wc' mode

Related Skills

• alignment-indexing - Create indices for random access (required for fetch/region queries)
• alignment-sorting - Sort alignments by coordinate or name
• alignment-filtering - Filter alignments by flags, quality, regions
• alignment-validation - Sequence dictionary cross-validation (M5 checksums)
• bam-statistics - Generate statistics from alignment files
• reference-operations - REF_PATH/REF_CACHE setup for CRAM
• sequence-io/read-sequences - Parse FASTA/FASTQ input files