Bio Duplicate Handling

Version Compatibility

Reference examples tested with: picard 3.1+, 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.

Duplicate Handling

"Remove PCR duplicates from my BAM file" → Mark or remove duplicate reads using the fixmate-sort-markdup pipeline to prevent duplicate bias in variant calling.

• CLI: samtools fixmate, samtools markdup (samtools)
• Python: pysam.fixmate(), pysam.markdup() (pysam)

Mark and remove PCR/optical duplicates using samtools.

Why Remove Duplicates?

PCR duplicates are identical copies of the same original molecule, created during library preparation. They inflate coverage, bias allele frequencies, and create false positive variant calls. Optical duplicates are flowcell-proximity artifacts of bridge amplification (especially on patterned NovaSeq / NovaSeq X / NextSeq 1000 flowcells).

When to Mark Duplicates -- and When NOT To

Standard samtools markdup is the right tool for some assays and actively harmful for others. The decision is assay-driven:

If the BAM came from 10x Cell Ranger / STARsolo and samtools markdup produces a 50-95% duplicate rate, it is the wrong tool, not a bug.

Tool Selection: markdup vs Picard vs UMI-aware

Picard UmiAwareMarkDuplicatesWithMateCigar is BETA and has known bugs on transcriptome-aligned BAMs (silently keeps duplicates). Avoid for RNA-seq UMIs.

Optical Distance Is Platform-Specific

samtools markdup default is -d 0, meaning optical-duplicate detection is disabled by default. Set explicitly per platform:

samtools markdup -d 2500 -t -f stats.txt input.bam marked.bam

# Count optical (SQ) vs library/PCR (LB) duplicates
samtools view -f 1024 marked.bam | grep -o 'dt:Z:[A-Z][A-Z]' | sort | uniq -c

Setting -d 2500 on a HiSeq run does no harm. Forgetting -d 2500 on NovaSeq systematically under-marks optical duplicates and overestimates library complexity.

Multi-Library Pooled Marking

Without --use-read-groups, multi-library BAMs systematically over-mark: independent molecules from different libraries with the same coordinates get wrongly flagged as PCR duplicates. With --use-read-groups, RG tags must also match for two reads to be a duplicate (verify availability with samtools markdup --help):

samtools markdup --use-read-groups -d 2500 -t in.bam out.bam

samtools --use-read-groups keys on RG ID; Picard's library-aware behavior keys on the LB tag (allowing dedup across multiple lanes of the same library). For multi-lane single-library BAMs, Picard MarkDuplicates with READ_NAME_REGEX is closer to canonical.

Duplicate Marking Workflow

Goal: Mark PCR/optical duplicates so they can be excluded from downstream variant calling and coverage analysis.

Approach: Name-sort, add mate tags with fixmate, coordinate-sort, then run markdup. The pipeline version avoids intermediate files.

Reference (samtools 1.19+):

# 1. Sort by name (required for fixmate)
samtools sort -n -o namesort.bam input.bam

# 2. Add mate information with fixmate
samtools fixmate -m namesort.bam fixmate.bam

# 3. Sort by coordinate (required for markdup)
samtools sort -o coordsort.bam fixmate.bam

# 4. Mark duplicates
samtools markdup coordsort.bam marked.bam

# 5. Index result
samtools index marked.bam

Pipeline Version (Optimized)

# collate is faster than sort -n; -u/-O between piped tools skips BGZF round-trips
samtools collate -O -u input.bam tmpdir/collate | \
    samtools fixmate -m -u - - | \
    samtools sort -u -@ 4 -T tmpdir/sort - | \
    samtools markdup -@ 4 -d 2500 -t --use-read-groups \
        -f markdup_stats.txt - marked.bam

samtools index marked.bam

This is ~30% faster than sort -n | fixmate | sort | markdup on typical 30x WGS.

Critical pitfall: samtools markdup requires ms (mate score, lowercase) and MC (mate CIGAR) tags from fixmate -m. A re-sort that loses aux tags via Python round-trip silently produces a markdup output that marks almost nothing. If duplicate counts look implausibly low, verify MC:Z: is present in the input to markdup.

samtools fixmate

Adds mate information required by markdup. Must be run on name-sorted BAM.

Basic Usage

samtools fixmate namesorted.bam fixmate.bam

Add Mate Score Tag (-m)

# Required for markdup to work correctly
samtools fixmate -m namesorted.bam fixmate.bam

Multi-threaded

samtools fixmate -m -@ 4 namesorted.bam fixmate.bam

Remove Secondary/Unmapped

samtools fixmate -r -m namesorted.bam fixmate.bam

samtools markdup

Marks or removes duplicate alignments. Requires coordinate-sorted BAM with mate tags from fixmate.

Mark Duplicates (Keep in File)

samtools markdup input.bam marked.bam

Remove Duplicates

samtools markdup -r input.bam deduped.bam

Output Statistics

samtools markdup -s input.bam marked.bam 2> markdup_stats.txt

Optical Duplicate Distance

# Default -d 0 disables optical detection. Set per platform; see decision table above.
samtools markdup -d 2500 -t input.bam marked.bam   # NovaSeq / patterned
samtools markdup -d 100 -t input.bam marked.bam    # HiSeq / random

Multi-threaded

samtools markdup -@ 4 input.bam marked.bam

Write Stats to File

samtools markdup -f stats.txt input.bam marked.bam

Duplicate Statistics

Check Duplicate Rate

samtools flagstat marked.bam
# Look for "duplicates" line

Count Duplicates

# Count reads with duplicate flag
samtools view -c -f 1024 marked.bam

Percentage Duplicates

total=$(samtools view -c marked.bam)
dups=$(samtools view -c -f 1024 marked.bam)
echo "scale=2; $dups * 100 / $total" | bc

pysam Python Alternative

Full Pipeline

import pysam

# Sort by name
pysam.sort('-n', '-o', 'namesort.bam', 'input.bam')

# Fixmate
pysam.fixmate('-m', 'namesort.bam', 'fixmate.bam')

# Sort by coordinate
pysam.sort('-o', 'coordsort.bam', 'fixmate.bam')

# Mark duplicates
pysam.markdup('coordsort.bam', 'marked.bam')

# Index
pysam.index('marked.bam')

Check Duplicate Flag

import pysam

with pysam.AlignmentFile('marked.bam', 'rb') as bam:
    total = 0
    duplicates = 0
    for read in bam:
        total += 1
        if read.is_duplicate:
            duplicates += 1

    print(f'Total: {total}')
    print(f'Duplicates: {duplicates}')
    print(f'Rate: {duplicates/total*100:.2f}%')

Filter Out Duplicates

import pysam

with pysam.AlignmentFile('marked.bam', 'rb') as infile:
    with pysam.AlignmentFile('nodup.bam', 'wb', header=infile.header) as outfile:
        for read in infile:
            if not read.is_duplicate:
                outfile.write(read)

Production Tools, Not Hand-Rolled

For real BAMs, always use a production marker. A naive Python implementation keyed on (chrom, pos, strand) ignores 5' position correction for soft clips, ignores library/RG, treats optical = PCR, and mis-handles supplementary alignments. The result is silently wrong duplicate marks. Use samtools markdup, Picard, or fgbio depending on assay (see decision tables above).

Alternative: From Aligner

Some aligners can mark duplicates directly during streaming:

BWA-MEM2 with samblaster

bwa-mem2 mem ref.fa R1.fq R2.fq | \
    samblaster | \
    samtools sort -o marked.bam

Picard MarkDuplicates

java -jar picard.jar MarkDuplicates \
    I=input.bam \
    O=marked.bam \
    M=metrics.txt \
    OPTICAL_DUPLICATE_PIXEL_DISTANCE=2500

UMI-Aware Deduplication

For UMI libraries (10x scRNA, ctDNA panels, Twist/IDT/Roche UMI capture), naive markdup destroys information. Use UMI-aware tools:

# 10x / scRNA -- group by cell barcode + UMI
umi_tools dedup --stdin=cellranger_possorted.bam --stdout=dedup.bam \
    --extract-umi-method=tag --umi-tag=UB --cell-tag=CB \
    --per-cell --method=directional

# Bulk UMI / ctDNA -- consensus calling (best practice for low-VAF detection)
fgbio AnnotateBamWithUmis -i raw.bam -f umi.fastq -o annotated.bam
fgbio GroupReadsByUmi -i annotated.bam -o grouped.bam --strategy=adjacency --edits=1
fgbio CallMolecularConsensusReads -i grouped.bam -o consensus.bam --min-reads=1
# Or for duplex (xGen-Prism, NEBNext duplex):
fgbio CallDuplexConsensusReads -i grouped.bam -o duplex.bam --min-reads 1 1 0

--method=directional is the default and correct -- do not use --method=unique, which treats single-base UMI errors as different molecules. samtools markdup --barcode-tag RX (since ~1.13) does exact-match UMI grouping; adequate for IDT xGen Duplex but insufficient for single-UMI applications where 1-edit errors are common.

Quick Reference

Duplicate FLAG

Filter Commands

# View only duplicates
samtools view -f 1024 marked.bam

# View non-duplicates only
samtools view -F 1024 marked.bam

# Count non-duplicates
samtools view -c -F 1024 marked.bam

Common Errors

Lossy Operations

samtools markdup -r (remove duplicates) is irreversible -- the records are dropped. Default to marking, not removing; downstream tools can filter on FLAG 1024. Removing pre-emptively destroys data needed for re-running QC, library complexity estimation, or switching dedup strategies.

Related Skills

• alignment-sorting - Sort by name/coordinate; collate vs sort -n decision
• alignment-filtering - Filter duplicates from output
• alignment-amplicon-clipping - Use ampliconclip instead of markdup for amplicon panels
• bam-statistics - Check duplicate rates with flagstat / mosdepth
• variant-calling/variant-calling - Standard variant calling expects deduped BAMs
• read-qc/quality-reports - Pre-alignment QC including UMI extraction