read-qc-umi-processing

name	read-qc-umi-processing
description	Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep includes UMIs and accurate molecule counting is needed, such as in single-cell RNA-seq, low-input RNA-seq, or targeted sequencing to distinguish PCR from biological duplicates.
tool_type	cli
primary_tool	umi_tools

Version Compatibility

Reference examples tested with: pandas 2.2+, 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.

UMI Processing

"Deduplicate reads using UMIs" -> Extract UMI barcodes, group reads by UMI+position, and collapse PCR duplicates to count unique molecules.

• CLI: umi_tools extract + umi_tools dedup (UMI-tools)
• CLI: fgbio GroupReadsByUmi + fgbio CallMolecularConsensusReads

UMIs (Unique Molecular Identifiers) are short random sequences added during library preparation to tag individual molecules before PCR amplification. This enables accurate PCR duplicate removal and molecule counting.

UMI Workflow Overview

Raw FASTQ with UMIs
    |
    v
[umi_tools extract] --> Move UMI to read header
    |
    v
[Alignment] --> bwa/STAR/bowtie2
    |
    v
[umi_tools dedup] --> Remove PCR duplicates based on UMI + position
    |
    v
Deduplicated BAM

Extract UMIs from Reads

UMI in Read Sequence

# UMI at start of R1 (8bp UMI)
umi_tools extract \
    --stdin=R1.fastq.gz \
    --read2-in=R2.fastq.gz \
    --stdout=R1_extracted.fastq.gz \
    --read2-out=R2_extracted.fastq.gz \
    --bc-pattern=NNNNNNNN

# UMI at start of R2
umi_tools extract \
    --stdin=R1.fastq.gz \
    --read2-in=R2.fastq.gz \
    --stdout=R1_extracted.fastq.gz \
    --read2-out=R2_extracted.fastq.gz \
    --bc-pattern2=NNNNNNNN

# UMI in both reads
umi_tools extract \
    --stdin=R1.fastq.gz \
    --read2-in=R2.fastq.gz \
    --stdout=R1_extracted.fastq.gz \
    --read2-out=R2_extracted.fastq.gz \
    --bc-pattern=NNNNNNNN \
    --bc-pattern2=NNNNNNNN

UMI Pattern Syntax

Pattern	Meaning
N	UMI base (extracted)
C	Cell barcode (extracted, kept separate)
X	Discard base
NNNNNNNN	8bp UMI
CCCCCCCCNNNNNNNN	8bp cell barcode + 8bp UMI
NNNXXXNNN	3bp UMI, skip 3bp, 3bp UMI

Complex Patterns

# 10X Genomics 3' v3 (16bp cell barcode + 12bp UMI in R1)
umi_tools extract \
    --stdin=R1.fastq.gz \
    --read2-in=R2.fastq.gz \
    --stdout=R1_extracted.fastq.gz \
    --read2-out=R2_extracted.fastq.gz \
    --bc-pattern=CCCCCCCCCCCCCCCCNNNNNNNNNNNN

# Skip bases between barcode and UMI
umi_tools extract \
    --stdin=R1.fastq.gz \
    --stdout=R1_extracted.fastq.gz \
    --bc-pattern=NNNNNNNNXXXX  # 8bp UMI, skip 4bp

# Fixed anchor sequence
umi_tools extract \
    --stdin=R1.fastq.gz \
    --stdout=R1_extracted.fastq.gz \
    --bc-pattern='(?P<umi_1>.{8})ATGC(?P<discard_1>.{4})'

UMI in Separate Index Read

# UMI in I1 index read
umi_tools extract \
    --stdin=R1.fastq.gz \
    --read2-in=R2.fastq.gz \
    --stdout=R1_extracted.fastq.gz \
    --read2-out=R2_extracted.fastq.gz \
    --bc-pattern=NNNNNNNN \
    --extract-method=string \
    --umi-separator=":"

Quality Filtering During Extraction

# Filter by UMI quality
umi_tools extract \
    --stdin=R1.fastq.gz \
    --stdout=R1_extracted.fastq.gz \
    --bc-pattern=NNNNNNNN \
    --quality-filter-threshold=20 \
    --quality-encoding=phred33

# Filter UMIs with N bases
umi_tools extract \
    --stdin=R1.fastq.gz \
    --stdout=R1_extracted.fastq.gz \
    --bc-pattern=NNNNNNNN \
    --filter-cell-barcode

Deduplication

Basic Deduplication

# Must be sorted and indexed first
samtools sort -o aligned_sorted.bam aligned.bam
samtools index aligned_sorted.bam

# Deduplicate
umi_tools dedup \
    --stdin=aligned_sorted.bam \
    --stdout=deduplicated.bam \
    --log=dedup.log

Deduplication Methods

# Default: directional (recommended for most cases)
umi_tools dedup -I input.bam -S output.bam --method=directional

# Unique: only exact UMI matches (most stringent)
umi_tools dedup -I input.bam -S output.bam --method=unique

# Cluster: network-based clustering
umi_tools dedup -I input.bam -S output.bam --method=cluster

# Adjacency: cluster with adjacency
umi_tools dedup -I input.bam -S output.bam --method=adjacency

# Percentile: for highly duplicated data
umi_tools dedup -I input.bam -S output.bam --method=percentile

Method Selection Guide

Method	Use Case	Speed
directional	Standard RNA-seq, most cases	Fast
unique	Very high diversity, PCR-free	Fastest
cluster	Low diversity, high errors	Slow
adjacency	Balance of accuracy/speed	Medium
percentile	Extremely high duplication	Fast

Paired-End Deduplication

# Paired-end mode
umi_tools dedup \
    -I aligned_sorted.bam \
    -S deduplicated.bam \
    --paired

# Use read2 for grouping (for R2-based libraries)
umi_tools dedup \
    -I aligned_sorted.bam \
    -S deduplicated.bam \
    --paired \
    --read2-in-read1

Gene-Level Deduplication

# Deduplicate per gene (for RNA-seq)
umi_tools dedup \
    -I aligned_sorted.bam \
    -S deduplicated.bam \
    --per-gene \
    --gene-tag=GX

# With GTF file for gene assignment
umi_tools dedup \
    -I aligned_sorted.bam \
    -S deduplicated.bam \
    --per-gene \
    --per-cell \
    --gene-tag=XT

UMI Counting

Count UMIs per Gene

# Count table (gene x cell for single-cell)
umi_tools count \
    -I deduplicated.bam \
    -S counts.tsv \
    --per-gene \
    --gene-tag=GX \
    --per-cell \
    --cell-tag=CB

# Wide format (matrix)
umi_tools count \
    -I deduplicated.bam \
    -S counts.tsv \
    --per-gene \
    --gene-tag=GX \
    --wide-format-cell-counts

Count Table Format

gene    cell    count
ENSG00000139618    ACGT    15
ENSG00000139618    TGCA    8
ENSG00000141510    ACGT    42

Group UMIs Without Deduplication

# Add UMI group tag to BAM (BX tag)
umi_tools group \
    -I aligned_sorted.bam \
    -S grouped.bam \
    --group-out=groups.tsv \
    --output-bam

Complete Workflows

Standard RNA-seq with UMIs

Goal: Process UMI-tagged RNA-seq reads from raw FASTQ through alignment to a deduplicated BAM file.

Approach: Extract UMIs from read headers with umi_tools, align with STAR, then deduplicate based on UMI + mapping position to produce a PCR-artifact-free BAM.

#!/bin/bash
set -euo pipefail

SAMPLE=$1
REFERENCE=$2

# 1. Extract UMIs (8bp at start of R1)
umi_tools extract \
    --stdin=${SAMPLE}_R1.fastq.gz \
    --read2-in=${SAMPLE}_R2.fastq.gz \
    --stdout=${SAMPLE}_R1_umi.fastq.gz \
    --read2-out=${SAMPLE}_R2_umi.fastq.gz \
    --bc-pattern=NNNNNNNN

# 2. Align with STAR
STAR --runThreadN 8 \
    --genomeDir $REFERENCE \
    --readFilesIn ${SAMPLE}_R1_umi.fastq.gz ${SAMPLE}_R2_umi.fastq.gz \
    --readFilesCommand zcat \
    --outFileNamePrefix ${SAMPLE}_ \
    --outSAMtype BAM SortedByCoordinate

# 3. Index
samtools index ${SAMPLE}_Aligned.sortedByCoord.out.bam

# 4. Deduplicate
umi_tools dedup \
    -I ${SAMPLE}_Aligned.sortedByCoord.out.bam \
    -S ${SAMPLE}_deduplicated.bam \
    --output-stats=${SAMPLE}_dedup_stats \
    --paired

# 5. Index deduplicated BAM
samtools index ${SAMPLE}_deduplicated.bam

echo "Done: ${SAMPLE}_deduplicated.bam"

Single-Cell Workflow (Post-CellRanger)

# CellRanger output has CB (cell barcode) and UB (UMI) tags
# Deduplicate per cell per gene
umi_tools dedup \
    -I possorted_genome_bam.bam \
    -S deduplicated.bam \
    --per-cell \
    --cell-tag=CB \
    --umi-tag=UB \
    --extract-umi-method=tag \
    --per-gene \
    --gene-tag=GX

Statistics and QC

Deduplication Stats

# Generate stats file
umi_tools dedup \
    -I input.bam \
    -S output.bam \
    --output-stats=dedup_stats

# Output files:
# dedup_stats_per_umi_per_position.tsv
# dedup_stats_per_umi.tsv
# dedup_stats_edit_distance.tsv

Interpret Deduplication Rate

import pandas as pd

stats = pd.read_csv('dedup.log', sep='\t', comment='#')
total_reads = stats['total_reads'].iloc[0]
unique_reads = stats['unique_reads'].iloc[0]
dedup_rate = 1 - (unique_reads / total_reads)
print(f'Deduplication rate: {dedup_rate:.1%}')

Performance Tips

# Increase speed with multiple cores (dedup only)
umi_tools dedup -I input.bam -S output.bam --parallel

# Reduce memory for large files
umi_tools dedup -I input.bam -S output.bam --buffer-whole-contig

# Skip statistics for speed
umi_tools dedup -I input.bam -S output.bam --no-sort-output

Alternative: fastp UMI Handling

For simple UMI extraction during QC:

# Extract 8bp UMI from R1 to header
fastp -i R1.fq.gz -I R2.fq.gz \
      -o R1_umi.fq.gz -O R2_umi.fq.gz \
      --umi --umi_loc read1 --umi_len 8

Note: fastp extracts UMIs but doesn't deduplicate - use umi_tools dedup after alignment.

Related Skills

• fastp-workflow - Simple UMI extraction during preprocessing
• quality-filtering - QC before UMI extraction
• alignment-files/sam-bam-basics - BAM sorting/indexing required before dedup
• single-cell/preprocessing - scRNA-seq workflows use UMI counting