Krewlyzer: Comprehensive cfDNA Feature Extraction Toolkit

Krewlyzer is a high-performance toolkit for extracting biological features from cell-free DNA (cfDNA) sequencing data. Designed for cancer genomics, liquid biopsy research, and clinical bioinformatics.

Built with Python + Rust for maximum performance. The compute-intensive core uses PyO3 to deliver 5-50x speedups over pure Python.

Tip

Full Documentation: msk-access.github.io/krewlyzer

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Why Krewlyzer?

Cancer cells leave molecular fingerprints in your blood. Krewlyzer finds them.

The Fragmentomics Advantage

Traditional Liquid Biopsy	Fragmentomics with Krewlyzer
Look for specific mutations	Analyze how DNA is cut
Need prior knowledge of tumor	Works without knowing mutations
Miss ~50% of early cancers	Detect more cancers, earlier

Key insight: Tumor DNA fragments are shorter (~145bp) than healthy DNA (~166bp). Krewlyzer quantifies this difference and extracts ML-ready features.

What You Get

Feature	Clinical Use
Fragment size ratios	Tumor burden estimation
Cutting patterns	Tissue of origin identification
Nucleosome positioning	Epigenetic profiling
Mutation-specific sizes	MRD monitoring

New to cfDNA? Read What is Cell-Free DNA? for background.

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Quick Install

# Docker (recommended - all data bundled)
docker pull ghcr.io/msk-access/krewlyzer:latest

# Clone + Install (development)
git clone https://github.com/msk-access/krewlyzer.git && cd krewlyzer
git lfs pull && pip install -e .

# pip + Data Clone (custom environments)
pip install krewlyzer
git clone --depth 1 https://github.com/msk-access/krewlyzer.git ~/.krewlyzer-data
cd ~/.krewlyzer-data && git lfs pull
export KREWLYZER_DATA_DIR=~/.krewlyzer-data/src/krewlyzer/data

Note

pip users: The KREWLYZER_DATA_DIR env var is required to locate bundled assets. See Installation Guide for details.

Quick Start

# Run all fragmentomics features
krewlyzer run-all -i sample.bam --reference hg19.fa --output results/

# Generate unified JSON for ML pipelines
krewlyzer run-all -i sample.bam --reference hg19.fa --output results/ --generate-json

# Individual tools
krewlyzer extract -i sample.bam -r hg19.fa -o output/
krewlyzer fsc -i output/sample.bed.gz -o output/

# Panel data (MSK-ACCESS) with target regions
krewlyzer run-all -i sample.bam -r hg19.fa -o results/ \
    --target-regions panel_targets.bed \
    --pon-model msk-access.pon.parquet

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Features

Command	Description	Output
extract	Extract fragments from BAM	.bed.gz
motif	End motif & MDS scores	.EndMotif.tsv, .MDS.tsv
fsc	Fragment size coverage	.FSC.tsv
fsr	Fragment size ratios	.FSR.tsv
fsd	Size distribution by arm	.FSD.tsv
wps	Windowed protection score	.WPS.parquet
ocf	Orientation-aware fragmentation	.OCF.tsv
region-entropy	TFBS/ATAC size entropy	.TFBS.tsv, .ATAC.tsv
uxm	Fragment-level methylation	.UXM.tsv
mfsd	Mutant vs wild-type sizes	.mFSD.tsv
build-pon	Build Panel of Normals	.pon.parquet
run-all	All features in one pass	All outputs
--generate-json	Unified JSON for ML	.features.json

Panel Mode (--target-regions)

For targeted sequencing panels (MSK-ACCESS):

krewlyzer run-all -i sample.bam -r hg19.fa -o results/ \
    --target-regions panel_targets.bed

• GC model: Trained on off-target fragments (unbiased)
• Outputs: Split into .tsv (off-target) and .ontarget.tsv
• Auto-PON: Use -A xs2 to auto-load bundled PON for z-scores
• ML negatives: Use -A xs2 --skip-pon to output raw features (no z-scores)

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Documentation

• Getting Started - 5-minute quickstart
• Installation - Docker, pip, development
• Usage Guide - CLI reference
• Feature Details - Per-feature documentation
• Nextflow Pipeline - Batch processing

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Citation

If you use Krewlyzer, please cite:

• DELFI (FSR): Cristiano S, et al. Nature 2019
• WPS: Snyder MW, et al. Cell 2016
• OCF: Sun K, et al. Genome Res 2019
• UXM: Loyfer N, et al. Nature 2022

See Citation & Scientific Background for full references.

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License

GNU Affero General Public License v3.0 (AGPL-3.0). See LICENSE.

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Developed by Ronak Shah (@rhshah) at Memorial Sloan Kettering Cancer Center.