markdup 0.0.17

Fast, accurate BAM deduplication with intelligent UMI detection and correct fragment detection

MarkDup

A fast, accurate BAM deduplication tool with intelligent UMI detection and correct fragment detection.

Why MarkDup?

Existing BAM deduplication tools suffer from several critical issues: buggy duplicate detection due to incorrect biological positioning and strand handling, poor performance especially with UMI-based deduplication, and inadequate UMI clustering that leads to over-merging.

MarkDup solves these problems with:

• Correct fragment detection using proper strand-aware coordinate handling
• Significantly faster processing through optimized algorithms and parallel processing
• Smart UMI clustering that prevents over-merging with frequency-aware algorithms
• Automatic detection that handles both UMI and non-UMI data without requiring different tools

Quick Start

# Installation
pip install markdup

# Basic usage (auto-detects everything)
markdup input.bam output.bam

# With multiple threads
markdup input.bam output.bam --threads 8

# Force coordinate-based (no UMIs)
markdup input.bam output.bam --no-umi

Key Features

🧬 Correct Fragment Detection

• Strand-aware coordinates: Properly handles forward/reverse strand reads
• CIGAR-aware positioning: Correctly processes indels and complex alignments
• Biological positioning: Uses 5'/3' positions, not reference positions

⚡ High Performance

• Significantly faster UMI clustering with optimized algorithms
• Parallel processing: Multi-core support for large files
• Memory efficient: Window-based processing for large datasets

🔄 Automatic Detection

• UMI auto-detection: Finds UMIs in read names or BAM tags
• Sequencing type detection: Automatically detects single-end vs paired-end
• Quality metrics: Selects the best quality criteria automatically

🎯 Smart UMI Clustering

• Frequency-aware: Prevents over-clustering of high-frequency UMIs
• Edit distance: Configurable similarity thresholds
• Exact matching: Handles identical UMIs efficiently

Performance

• Significantly faster UMI clustering with optimized algorithms
• Multi-core processing for parallel performance
• Memory efficient window-based processing for large files
• Automatic optimization based on input data characteristics

How It Works

1. Auto-detect: UMI presence, sequencing type, and quality metrics
2. Group fragments: By biological position and strand
3. Cluster UMIs: Using edit distance and frequency-aware algorithms
4. Select best: The highest quality read from each cluster
5. Output: Deduplicated reads with cluster information

Documentation

Usage Examples

Basic Deduplication

# Auto-detect UMIs and process
markdup input.bam output.bam

# Force coordinate-based method (no UMIs)
markdup input.bam output.bam --no-umi

Advanced Options

# Custom UMI settings
markdup input.bam output.bam --umi-tag UB --min-edit-dist-frac 0.15

# Start-only positioning (useful for ChIP-seq)
markdup input.bam output.bam --start-only

# Keep duplicates and mark them
markdup input.bam output.bam --keep-duplicates

Performance Tuning

# Use 8 threads
markdup input.bam output.bam --threads 8

# Use larger windows for better performance
markdup input.bam output.bam --window-size 200000

Detailed Documentation

• Installation Guide - How to install MarkDup
• Usage Guide - How to use MarkDup
• Algorithm Details - How MarkDup works and fixes existing problems
• FAQ - Frequently asked questions
• Contributing - How to contribute

Command Line Options

Option	Description	Default
INPUT_BAM	Input BAM file	Required
OUTPUT_BAM	Output BAM file	Required
--threads	Number of threads	1
--no-umi	Force coordinate-based method	Auto-detect
--umi-tag	UMI BAM tag (e.g., UB)	Auto-detect
--start-only	Use start position only	False
--end-only	Use end position only	False
--keep-duplicates	Keep and mark duplicates	False
--max-dist-frac	UMI edit distance threshold	0.1
--max-frequency-ratio	UMI frequency threshold	0.1

Output Format

MarkDup adds BAM tags to track deduplication:

Tag	Description
cn	Cluster name (chr:start-end:strand:UMI)
cs	Cluster size (number of reads)

License

MIT License - see LICENSE for details.

 

Copyright © 2025-present Chang Y