Krewlyzer: Comprehensive cfDNA Feature Extraction Toolkit

Krewlyzer is a robust, user-friendly command-line toolkit for extracting a wide range of biological features from cell-free DNA (cfDNA) sequencing data. It is designed for cancer genomics, liquid biopsy research, and clinical bioinformatics, providing high-performance, reproducible feature extraction from BAM files. Krewlyzer draws inspiration from cfDNAFE and implements state-of-the-art methods for fragmentation, motif, and methylation analysis, all in a modern Pythonic interface with rich parallelization and logging.

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Table of Contents

• System Requirements
• Installation
• Reference Data
• Command Summary
• Typical Workflow
• Feature Details & Usage
  • Motif-based Feature Extraction
  • Fragment Size Coverage (FSC)
  • Fragment Size Ratio (FSR)
  • Fragment Size Distribution (FSD)
  • Windowed Protection Score (WPS)
  • Orientation-aware Fragmentation (OCF)
  • Fragment-level Methylation (UXM)
  • Run All Features
• Output Structure Examples
• Troubleshooting
• Citation & Acknowledgements

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System Requirements

• Linux or macOS (tested on Ubuntu 20.04, macOS 12+)
• Python 3.8+
• ≥16GB RAM recommended for large BAM files
• Docker (optional, for easiest setup)

Installation

With Docker (Recommended)

docker pull ghcr.io/msk-access/krewlyzer:latest
# Example usage:
docker run --rm -v $PWD:/data ghcr.io/msk-access/krewlyzer:latest motif /data/sample.bam -g /data/hg19.fa -o /data/motif_out

With uv Virtual Environment

uv venv .venv
source .venv/bin/activate
uv pip install .

Or install from PyPI:

uv pip install krewlyzer

Reference Data

• Reference Genome (FASTA):
  • Download GRCh37/hg19 from UCSC
  • BAMs must be sorted, indexed, and aligned to the same build
• Bin/Region/Marker Files:
  • Provided in krewlyzer/data/ (see options for each feature)

Command Summary

Command	Description
motif	Motif-based feature extraction
fsc	Fragment size coverage
fsr	Fragment size ratio
fsd	Fragment size distribution
wps	Windowed protection score
ocf	Orientation-aware fragmentation
uxm	Fragment-level methylation (SE/PE)
run-all	Run all features for a BAM

Typical Workflow

# 1. Motif extraction (produces .bed.gz files)
krewlyzer motif sample.bam -g hg19.fa -o motif_out

# 2. Extract additional features from motif output:
krewlyzer fsc motif_out --output fsc_out
krewlyzer fsr motif_out --output fsr_out
krewlyzer fsd motif_out --arms-file krewlyzer/data/ChormosomeArms/hg19_arms.bed --output fsd_out
krewlyzer wps motif_out --output wps_out
krewlyzer ocf motif_out --output ocf_out
krewlyzer uxm /path/to/bam_folder --output uxm_out

# 3. Run all features in one call:
krewlyzer run-all sample.bam --reference hg19.fa --output all_features_out

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Feature Details & Usage

Motif-based Feature Extraction

Purpose: Extracts end motif, breakpoint motif, and Motif Diversity Score (MDS) from sequencing fragments.

Biological context: Motif analysis of cfDNA fragment ends can reveal tissue-of-origin, nucleosome positioning, and mutational processes. MDS quantifies motif diversity, which may be altered in cancer.

Usage:

krewlyzer motif path/to/input.bam -g path/to/reference.fa -o path/to/output_dir \
    --minlen 65 --maxlen 400 -k 3 --verbose

• Output: EDM, BPM, and MDS subfolders in output directory.
• Rich logging and progress bars for user-friendly feedback.

Fragment Size Coverage (FSC)

Purpose: Computes z-scored coverage of cfDNA fragments in different size ranges, per genomic bin (default: 100kb), with GC correction.

Biological context: cfDNA fragment size profiles are informative for cancer detection and tissue-of-origin. FSC quantifies the coverage of short (65-150bp), intermediate (151-260bp), long (261-400bp), and total (65-400bp) fragments, normalized to genome-wide means.

Usage:

krewlyzer fsc motif_out --output fsc_out [options]

• Input: .bed.gz files from motif command
• Output: One .FSC file per sample
• Options:
  • --bin-input, -b: Bin file (default: data/ChormosomeBins/hg19_window_100kb.bed)
  • --windows, -w: Window size (default: 100000)
  • --continue-n, -c: Super-bin size (default: 50)
  • --threads, -t: Number of processes

Fragment Size Ratio (FSR)

Purpose: Calculates the ratio of short/intermediate/long fragments per bin, using DELFI-inspired cutoffs.

Biological context: The DELFI method (Mouliere et al., 2018) showed that cfDNA fragment size ratios are highly informative for cancer detection. Krewlyzer uses short (65-150bp), intermediate (151-220bp), and long (221-400bp) bins, with GC correction.

Usage:

krewlyzer fsr motif_out --output fsr_out [options]

• Input: .bed.gz files from motif command
• Output: One .FSR file per sample
• Options: Same as FSC

Fragment Size Distribution (FSD)

Purpose: Computes high-resolution (5bp bins) fragment length distributions per chromosome arm.

Biological context: cfDNA fragmentation patterns at chromosome arms can reflect nucleosome positioning, chromatin accessibility, and cancer-specific fragmentation signatures.

Usage:

krewlyzer fsd motif_out --arms-file krewlyzer/data/ChormosomeArms/hg19_arms.bed --output fsd_out [options]

• Input: .bed.gz files from motif command
• Output: One .FSD file per sample
• Options:
  • --arms-file, -a: Chromosome arms BED (required)
  • --threads, -t: Number of processes

Windowed Protection Score (WPS)

Purpose: Computes nucleosome protection scores (WPS) for each region in a transcript/region file.

Biological context: The WPS (Snyder et al., 2016) quantifies nucleosome occupancy and chromatin accessibility by comparing fragments spanning a window to those ending within it. High WPS indicates nucleosome protection; low WPS, open chromatin.

Usage:

krewlyzer wps motif_out --output wps_out [options]

• Input: .bed.gz files from motif command
• Output: .WPS.tsv.gz per region/sample
• Options:
  • --tsv-input: Transcript region file (default: data/TranscriptAnno/transcriptAnno-hg19-1kb.tsv)
  • --wpstype: WPS type (L for long [default], S for short)
  • --threads, -t: Number of processes

Orientation-aware Fragmentation (OCF)

Purpose: Computes orientation-aware cfDNA fragmentation (OCF) values in tissue-specific open chromatin regions.

Biological context: OCF (Sun et al., Genome Res 2019) measures the phasing of upstream (U) and downstream (D) fragment ends in open chromatin, informing tissue-of-origin of cfDNA.

Usage:

krewlyzer ocf motif_out --output ocf_out [options]

• Input: .bed.gz files from motif command
• Output: .sync.end files per tissue and summary all.ocf.csv per sample
• Options:
  • --ocr-input, -r: Open chromatin region BED (default: data/OpenChromatinRegion/7specificTissue.all.OC.bed)
  • --threads, -t: Number of processes

Fragment-level Methylation (UXM)

Purpose: Computes the proportions of Unmethylated (U), Mixed (X), and Methylated (M) fragments per region, supporting both single-end (SE) and paired-end (PE) BAMs.

Biological context: Fragment-level methylation (UXM, Sun et al., Nature 2023) reveals cell-of-origin and cancer-specific methylation patterns in cfDNA. Krewlyzer supports both SE and PE mode, pairing reads as in cfDNAFE.

Usage:

# Single-end (default)
krewlyzer uxm /path/to/bam_folder --output uxm_out [options]
# Paired-end mode
krewlyzer uxm /path/to/bam_folder --output uxm_out --type PE [options]

• Input: Folder of sorted, indexed BAMs
• Output: .UXM.tsv file per sample
• Options:
  • --mark-input, -m: Marker BED file (default: data/MethMark/Atlas.U25.l4.hg19.bed)
  • --map-quality, -q: Minimum mapping quality (default: 30)
  • --min-cpg, -c: Minimum CpG per fragment (default: 4)
  • --methy-threshold, -tM: Methylation threshold (default: 0.75)
  • --unmethy-threshold, -tU: Unmethylation threshold (default: 0.25)
  • --type: Fragment type: SE or PE (default: SE)
  • --threads, -t: Number of processes

Run All Features

Runs all feature extraction commands (motif, fsc, fsr, fsd, wps, ocf, uxm) for a single BAM file in one call.

Usage:

krewlyzer run-all sample.bam --reference hg19.fa --output all_features_out [--threads N] [--type SE|PE]

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Output Structure Examples

After krewlyzer motif:

outdir/
├── sample1.bed.gz
├── EDM/
│   └── sample1.EndMotif
├── BPM/
│   └── sample1.BreakPointMotif
├── MDS/
│   └── sample1.MDS

After krewlyzer fsc/fsr/fsd:

fsc_out/
├── sample1.FSC
fsr_out/
├── sample1.FSR
fsd_out/
├── sample1.FSD

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Troubleshooting

• FileNotFoundError: Ensure all input files/paths exist and are readable. Use absolute paths if possible.
• PermissionError: Check output directory permissions.
• Missing dependencies: Use Docker or follow Installation for all requirements.
• Reference mismatch: BAM and reference FASTA must be from the same genome build.
• Memory errors: Use ≥16GB RAM for large BAMs or process in batches.

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Citation & Acknowledgements

If you use Krewlyzer in your work, please cite this repository and cfDNAFE. Krewlyzer implements or adapts methods from the following primary literature:

• DELFI (FSR): Mouliere F, Chandrananda D, Piskorz AM, et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci Transl Med. 2018;10(466):eaat4921. https://doi.org/10.1126/scitranslmed.aat4921

• WPS: Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J. Cell-free DNA Comprises an In Vivo Nucleosome Footprint that Informs Its Tissues-Of-Origin. Cell. 2016;164(1-2):57-68. https://doi.org/10.1016/j.cell.2015.11.050

• OCF: Sun K, Jiang P, Chan KC, et al. Orientation-aware plasma cell-free DNA fragmentation analysis in open chromatin regions informs tissue of origin. Genome Res. 2019;29(3):418-427. https://doi.org/10.1101/gr.242719.118

• UXM: Sun K, et al. Fragment-level methylation measures cell-of-origin and cancer-specific signals in cell-free DNA. Nature. 2023;616(7956):563-571. https://doi.org/10.1038/s41586-022-05580-6

• cfDNAFE:

@misc{cfDNAFE,
  author = {Wanxin Cui et al.},
  title = {cfDNAFE: A toolkit for comprehensive cell-free DNA fragmentation feature extraction},
  year = {2022},
  howpublished = {\url{https://github.com/Cuiwanxin1998/cfDNAFE}}
}

• Developed by the MSK-ACCESS team at Memorial Sloan Kettering Cancer Center.

References

1. Mouliere F, Chandrananda D, Piskorz AM, et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci Transl Med. 2018;10(466):eaat4921. https://doi.org/10.1126/scitranslmed.aat4921
2. Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J. Cell-free DNA Comprises an In Vivo Nucleosome Footprint that Informs Its Tissues-Of-Origin. Cell. 2016;164(1-2):57-68. https://doi.org/10.1016/j.cell.2015.11.050
3. Sun K, Jiang P, Chan KC, et al. Orientation-aware plasma cell-free DNA fragmentation analysis in open chromatin regions informs tissue of origin. Genome Res. 2019;29(3):418-427. https://doi.org/10.1101/gr.242719.118
4. Sun K, et al. Fragment-level methylation measures cell-of-origin and cancer-specific signals in cell-free DNA. Nature. 2023;616(7956):563-571. https://doi.org/10.1038/s41586-022-05580-6

License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). See the LICENSE file for full terms.