fragmentomics 🔬
A pipeline for analyzing cell-free DNA profiles
This is the home of the pipeline, fragmentomics. Its long-term goals: to provide a standardized, reproducible, and scalable framework for cfDNA fragmentomics analysis, enabling comprehensive characterization of fragmentation patterns and supporting biomarker discovery across research and clinical applications.!
Overview¶
Welcome to fragmentomics's documentation! This guide is the main source of documentation for users that are getting started with the long pipeline name.
The ./fragmentomics pipeline is composed several inter-related sub commands to setup and run the pipeline across different systems. Each of the available sub commands perform different functions:
fragmentomics is a comprehensive workflow for analyzing fragmentation patterns in cell-free DNA sequencing data. It runs FinaleToolkit1, a python-based fragmentomics package, to process sorted and indexed BAM files generated from whole-genome sequencing, whole-genome bisulfite sequencing, or ChIP-seq experiments. The pipeline characterizes multiple cfDNA fragmentation features, including fragment-length distributions, genomic coverage, fragment-end motifs, motif diversity scores, window protection scores, GC-corrected DELFI short-to-long fragment ratios, and cleavage profiles. Together, these measurements provide a detailed view of cfDNA fragmentation patterns and generate standardized outputs that can support downstream biomarker discovery, disease classification, and other research applications.. It relies on technologies like Singularity2 to maintain the highest-level of reproducibility. The pipeline consists of a series of data processing and quality-control steps orchestrated by Snakemake3, a flexible and scalable workflow management system, to submit jobs to a cluster.
As input, it accepts a set of paired-end Illumina FastQ or BAM files (FastQ and BAM cannot be mixed in a single run) and can be run locally on a compute instance or on-premise using a cluster. A user can define the method or mode of execution. The pipeline can submit jobs to a cluster using a job scheduler like SLURM (more coming soon!). A hybrid approach ensures the pipeline is accessible to all users. Before getting started, we highly recommend reading through the usage section of each available sub command.
Documentation¶
The documentation is organized into three parts:
New users should start with fragmentomics run. If you are providing BAM files rather than FastQ files, also read BAM normalization and the reference contig filter, which describe how your alignments are checked against the selected genome build and subset to its contigs. If you are providing FastQ files, FastQ alignment describes which reference the reads are aligned to and how the analysis BAM is filtered.
For more information about issues or trouble-shooting a problem, please checkout our FAQ prior to opening an issue on Github.
Contribute¶
This site is a living document, created for and by members like you. fragmentomics is maintained by the members of OpenOmics and is improved by continous feedback! We encourage you to contribute new content and make improvements to existing content via pull request to our GitHub repository .
Citation¶
If you use this software, please cite it as below:
Citation coming soon!
Citation coming soon!
References¶
1. James Wenhan Li, Ravi Bandaru, Kundan Baliga, Yaping Liu. FinaleToolkit: Accelerating Cell-Free DNA Fragmentation Analysis with a High-Speed Computational Toolkit. Bioinformatics Advances, 2025, vbaf236.
2. Kurtzer GM, Sochat V, Bauer MW (2017). Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459.
3. Koster, J. and S. Rahmann (2018). "Snakemake-a scalable bioinformatics workflow engine." Bioinformatics 34(20): 3600.