tools-only UMI Processing
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
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[umi_tools extract] --> Move UMI to read header
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[Alignment] --> bwa/STAR/bowtie2
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[umi_tools dedup] --> Remove PCR duplicates based on UMI + position
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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
#!/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