bio-alignment-filtering

name	bio-alignment-filtering
description	Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
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.

Alignment Filtering

"Filter my BAM file to keep only high-quality reads" -> Select reads by FLAG bits, mapping quality, and genomic regions using samtools view or pysam.

• CLI: samtools view with -F/-f/-q/-L flags (samtools)
• Python: pysam.AlignmentFile iteration with attribute filters (pysam)

Filter alignments by flags, quality, and regions using samtools and pysam.

Filter Flags

Option	Description
-f FLAG	Include reads with ALL bits set
-F FLAG	Exclude reads with ANY bits set
-G FLAG	Exclude reads with ALL bits set
-q MAPQ	Minimum mapping quality
-L BED	Include reads overlapping regions

Common FLAG Values

Flag	Hex	Meaning
1	0x1	Paired
2	0x2	Proper pair
4	0x4	Unmapped
8	0x8	Mate unmapped
16	0x10	Reverse strand
32	0x20	Mate reverse strand
64	0x40	First in pair (read1)
128	0x80	Second in pair (read2)
256	0x100	Secondary alignment
512	0x200	Failed QC
1024	0x400	Duplicate
2048	0x800	Supplementary

Filter by FLAG

Keep Only Mapped Reads

samtools view -F 4 -o mapped.bam input.bam

Keep Only Unmapped Reads

samtools view -f 4 -o unmapped.bam input.bam

Keep Only Properly Paired

samtools view -f 2 -o proper.bam input.bam

Remove Duplicates

samtools view -F 1024 -o nodup.bam input.bam

Remove Secondary and Supplementary

samtools view -F 2304 -o primary.bam input.bam

Keep Only Primary Alignments

samtools view -F 256 -F 2048 -o primary.bam input.bam
# Or combined: -F 2304

Keep Read1 Only

samtools view -f 64 -o read1.bam input.bam

Keep Read2 Only

samtools view -f 128 -o read2.bam input.bam

Forward Strand Only

samtools view -F 16 -o forward.bam input.bam

Reverse Strand Only

samtools view -f 16 -o reverse.bam input.bam

Filter by Mapping Quality

Minimum MAPQ

samtools view -q 30 -o highqual.bam input.bam

MAPQ and Mapped

samtools view -F 4 -q 30 -o filtered.bam input.bam

Aligner-Aware MAPQ Thresholds

MAPQ scales differ by aligner; the same -q 30 filter does different things. See sam-bam-basics for the full MAPQ-by-aligner table. Filtering recommendations:

Aligner	"Drop ambiguous"	"High confidence"
BWA-MEM / BWA-MEM2	-q 1	-q 30 (or -q 60 for unique only)
Bowtie2	-q 1	-q 23 (Bowtie2 MAPQ maxes at 42 end-to-end; 23 is a community "uniquely mapped" convention derived from analysis of Bowtie2's MAPQ scoring, not stated in the official manual)
STAR	-q 255	-q 255 (STAR emits only MAPQ 0/1/3/255, 255 = unique; -q 60 is therefore equivalent to -q 255 -- there are no values between 3 and 255)
HISAT2	-q 1	-q 60
minimap2 (DNA, long-read)	-q 1	-q 60
pbmm2 (PacBio)	-q 1	-q 60

For Phred-scaled aligners (BWA, minimap2), MAPQ Q maps to ~10^(-Q/10) probability of wrong mapping. For STAR, the only emitted values are 0/1/3/255 (sentinels, not probabilities).

Drop Ambiguous Across Aligners (Universal)

samtools view -q 1 in.bam   # exclude MAPQ=0; works for all aligners

Filter by Region

Single Region

samtools view -o region.bam input.bam chr1:1000000-2000000

Multiple Regions

samtools view -o regions.bam input.bam chr1:1000-2000 chr2:3000-4000

Regions from BED File

samtools view -L targets.bed -o targets.bam input.bam

Combine Region and Quality

samtools view -q 30 -L targets.bed -o filtered.bam input.bam

Combined Filters

Standard Quality Filter

Goal: Produce a clean BAM containing only primary, mapped, non-duplicate reads with high mapping confidence.

Approach: Combine FLAG exclusion (-F for unmapped + secondary + duplicate + supplementary) with a MAPQ threshold.

Reference (samtools 1.19+):

samtools view -F 3332 -q 30 -o filtered.bam input.bam
# 3332 = 4 (unmapped) + 256 (secondary) + 1024 (duplicate) + 2048 (supplementary)

Variant Calling Prep -- Assay-Aware

Goal: Choose a filter that matches what the downstream caller expects. Stripping supplementary alignments breaks SV callers; requiring proper-pair drops valid spliced RNA-seq reads.

Assay / caller	Recommended filter	Why
Germline WGS short-variant (HaplotypeCaller, DeepVariant)	-f 2 -F 3328 -q 20	Primary, no dup, proper pair, MAPQ>=20
Somatic short-variant (Mutect2, Strelka2)	-F 3328 -q 1	Drop only MAPQ=0; somatic callers handle low MAPQ; chimeric reads at SVs may carry real somatic SNVs
Long-read short-variant (clair3, DeepVariant ONT)	-F 3328 -q 5	Long-read MAPQ scale is lower
Long-read SV (Sniffles, cuteSV)	-F 1024 only	Keep supplementary -- SA tag is the SV signal
Short-read SV (Manta, GRIDSS, Delly, SvABA)	-F 1024 only	Same -- supplementary required
ChIP-seq peak calling	-F 1804 -q 30	Drop dup + secondary + unmapped + mate-unmapped + QC-fail (supplementary reads are kept -- 1804 has no 2048 bit)
ATAC-seq	-F 1804 -q 30 -f 2	Same plus proper pair
RNA-seq quantification (STAR)	-q 255	Unique only (STAR sentinel)
RNA-seq quantification (HISAT2)	-F 256 -q 60	Different aligner semantics
RNA-seq variant (after SplitNCigarReads)	-F 3328 -q 20	Standard germline after split-N-trim
Panel / amplicon	After samtools ampliconclip; -F 1024 -q 20	Primer overlap makes proper-pair unreliable
ctDNA / cfDNA (UMI)	After fgbio consensus; do not pre-filter raw

Reference (samtools 1.19+):

# Short-variant germline
samtools view -f 2 -F 3328 -q 20 -o clean.bam input.bam
# 3328 = 256 (secondary) + 1024 (duplicate) + 2048 (supplementary)

# SV calling: KEEP supplementary
samtools view -F 1024 -o sv_input.bam input.bam   # NOT -F 2304 or -F 3328

# ChIP-seq / ATAC-seq common filter
samtools view -F 1804 -q 30 -o filtered.bam input.bam
# 1804 = 4 + 8 + 256 + 512 + 1024 = unmapped + mate-unmapped + secondary + QC-fail + duplicate

Cost of getting this wrong: filtering -F 2304 or -F 3328 before SV calling produces zero SV calls -- a single-flag mistake that silently invalidates the analysis.

Subsample Reads (Deterministic, Pair-Consistent)

samtools view -s SEED.FRAC -- integer is the hash seed; fractional is the keep fraction. The hash is on QNAME, so:

1. Mate consistency: read1 and read2 are kept or dropped together.
2. Reproducibility: same seed + same fraction returns the same reads.
3. Sequential downsampling requires different seeds. With the same seed the keep-sets are nested, so -s 1.5 then -s 1.25 keeps a nested 1/4 (25%) of the original, not 12.5%. Use different integer seeds for independent samples.

# 10% with seed 42 (always the same reads; pair-consistent)
samtools view -s 42.1 -b -o subset.bam input.bam

# Sequential cuts with INDEPENDENT seeds
samtools view -s 1.5 -b in.bam > half1.bam
samtools view -s 2.25 -b half1.bam > quarter.bam   # 12.5% of original

# Coverage-matching to a target read count
total=$(samtools view -c -F 2304 input.bam)
target=10000000
frac=$(awk -v t=$target -v n=$total 'BEGIN{printf "%.6f", t/n}')
samtools view -s "1.${frac#*.}" -b -o matched.bam input.bam

# Tumor-normal coverage matching (pull tumor down to normal)
normal_reads=$(samtools view -c -F 2308 normal.bam)
tumor_reads=$(samtools view -c -F 2308 tumor.bam)
if [ "$tumor_reads" -gt "$normal_reads" ]; then
    frac=$(awk -v n=$normal_reads -v t=$tumor_reads 'BEGIN{printf "%.6f", n/t}')
    samtools view -s "1.${frac#*.}" -b -o tumor_matched.bam tumor.bam
fi

A subsampled BAM without an integer seed (-s 0.1) is non-reproducible -- production pipelines should reject it.

Expression Filtering

samtools view -e EXPR (or --expr, since samtools 1.12; the sclen helper used below and improved null-tag handling arrived in 1.16) supports arbitrary expression filtering on tags, FLAG, MAPQ, RNAME, CIGAR, etc. Powerful for filtering by NM, AS, NH, cs, etc. that the FLAG-based filters cannot reach:

# Reads with >=2 mismatches (NM tag)
samtools view -e '[NM] >= 2' in.bam

# Soft clip on the left, on chr1
samtools view -e 'cigar=~"^[0-9]+S" && rname=="chr1"' in.bam

# Combine with FLAG and MAPQ
samtools view -F 2308 -q 30 -e '[NM] <= 5 && [AS] >= 100' in.bam

# Drop reads with low mapped fraction (samtools-internal helpers)
samtools view -e 'sclen / qlen < 0.2' in.bam

Note: in samtools 1.16+, ![NM] is true only if NM is missing (was buggy in earlier versions); NULL values from missing tags propagate through arithmetic.

Filter by Read Group

samtools view -r library_A in.bam              # single read group
samtools view -R rg_list.txt in.bam            # multiple via file (one ID per line)

pysam Python Alternative

Basic Filtering

import pysam

with pysam.AlignmentFile('input.bam', 'rb') as infile:
    with pysam.AlignmentFile('filtered.bam', 'wb', header=infile.header) as outfile:
        for read in infile:
            if read.is_unmapped:
                continue
            if read.mapping_quality < 30:
                continue
            if read.is_duplicate:
                continue
            outfile.write(read)

Filter with Function

Goal: Apply a multi-criteria quality filter to produce clean alignments for downstream analysis.

Approach: Define a predicate checking mapped status, primary alignment, duplicate flag, and MAPQ; stream reads through it.

Reference (pysam 0.22+):

import pysam

def passes_filter(read):
    if read.is_unmapped:
        return False
    if read.is_secondary or read.is_supplementary:
        return False
    if read.is_duplicate:
        return False
    if read.mapping_quality < 30:
        return False
    return True

with pysam.AlignmentFile('input.bam', 'rb') as infile:
    with pysam.AlignmentFile('filtered.bam', 'wb', header=infile.header) as outfile:
        for read in infile:
            if passes_filter(read):
                outfile.write(read)

Filter by Region

import pysam

with pysam.AlignmentFile('input.bam', 'rb') as infile:
    with pysam.AlignmentFile('region.bam', 'wb', header=infile.header) as outfile:
        for read in infile.fetch('chr1', 1000000, 2000000):
            outfile.write(read)

Filter from BED File

Goal: Extract only reads overlapping target regions defined in a BED file.

Approach: Parse BED into a list of (chrom, start, end) tuples, then fetch reads from each region and write to output.

Reference (pysam 0.22+):

import pysam

def read_bed(bed_path):
    regions = []
    with open(bed_path) as f:
        for line in f:
            if line.startswith('#'):
                continue
            parts = line.strip().split('\t')
            regions.append((parts[0], int(parts[1]), int(parts[2])))
    return regions

regions = read_bed('targets.bed')

with pysam.AlignmentFile('input.bam', 'rb') as infile:
    with pysam.AlignmentFile('targets.bam', 'wb', header=infile.header) as outfile:
        for chrom, start, end in regions:
            for read in infile.fetch(chrom, start, end):
                outfile.write(read)

Subsample (Pair-Consistent)

Hash on QNAME so mates stay together (a fresh random.random() per read drops mates inconsistently and breaks paired-end tools):

import pysam
import zlib

fraction = 0.1
seed = 42
threshold = int(0xffffffff * fraction)

def template_hash(qname, seed):
    return zlib.crc32(qname.encode()) ^ seed

with pysam.AlignmentFile('input.bam', 'rb') as infile:
    with pysam.AlignmentFile('subset.bam', 'wb', header=infile.header) as outfile:
        for read in infile:
            if template_hash(read.query_name, seed) <= threshold:
                outfile.write(read)

Quick Reference

Task	samtools command
Mapped only	view -F 4
Unmapped only	view -f 4
Properly paired	view -f 2
Primary only	view -F 2304
No duplicates	view -F 1024
High MAPQ	view -q 30
Region	view file.bam chr1:1-1000
BED regions	view -L file.bed
Subsample 10% (reproducible)	view -s 42.1
Standard filter	view -F 3332 -q 30

Common Filter Combinations

Purpose	Flags
Clean reads	-F 3332 -q 30 (mapped, primary, no dups, high qual)
Variant calling	-f 2 -F 3328 -q 20 (proper pair, primary, no dups)
Coverage analysis	-F 1284 -q 1 (mapped, no secondary, no dups; supplementary retained)
Count unique	-F 2304 (primary only)

Flag breakdowns:

• 2304 = 256 + 2048 (secondary + supplementary)
• 3328 = 256 + 1024 + 2048 (secondary + duplicate + supplementary)
• 3332 = 4 + 256 + 1024 + 2048 (unmapped + secondary + duplicate + supplementary)
• 1284 = 4 + 256 + 1024 (unmapped + secondary + duplicate)

Related Skills

• sam-bam-basics - FLAG semantics, MAPQ-by-aligner, secondary vs supplementary
• alignment-sorting - Sort before/after filtering
• alignment-indexing - Required for region filtering
• alignment-amplicon-clipping - Primer clipping for amplicon panels
• duplicate-handling - Mark duplicates before filtering
• bam-statistics - Check filter effects