Introduction
What Is cfDNA Fragmentomics
cfDNA fragmentomics focuses on how cell-free DNA is cut and packaged, not only on sequence changes.
During programmed cell death, tissue injury or active secretion, cells release short DNA fragments into blood.
These fragments carry characteristic lengths, genomic positions and end sequences that mirror chromatin structure and tissue-of-origin.
Biological characteristics of cell-free DNA. (Ding S C, Lo Y M D., Diagnostics, 2022)
In cell-free DNA (cfDNA) fragmentomics sequencing, we use whole-genome sequencing (WGS) of plasma cell-free DNA to read these patterns.
Instead of targeting a few genes, WGS surveys the entire genome and reconstructs each DNA fragment's start, end and size.
Coverage can be tuned to your liquid biopsy research design.
From this WGS data, our cfDNA fragmentomics pipeline derives multiple feature classes:
- Fragment size distribution and fragment size ratio (FSD/FSR) to describe global and locus-specific length patterns.
- End motifs and breakpoint patterns to capture sequence preferences at cleavage sites.
- Preferred ends and jagged ends that highlight nuclease activity and chromatin accessibility.
- Nucleosome footprints inferred from periodic coverage around promoters, enhancers and open-chromatin regions.
- Copy-number variation and SNP profiles that add broad genetic context.
Together, these cfDNA fragmentation features create a rich, genome-wide signal.
Researchers can combine them with sample metadata, imaging readouts and other omics to develop multi-cancer early detection models, tissue-of-origin classifiers and new biomarker hypotheses based on cfDNA fragmentomics analysis.
Why Use Cell-Free DNA Fragmentomics in Liquid Biopsy Research
Many liquid biopsy projects still rely only on mutation panels.
These designs often face low variant allele fractions, limited genomic coverage and high sequencing costs when cohorts scale up.
cfDNA fragmentomics sequencing offers a different route.
By using whole-genome sequencing of plasma cfDNA, you capture fragmentation, copy-number and chromatin-related signals across the entire genome, even when specific mutations are rare or unknown.
Move beyond mutation-only assays
- Broader signal: Fragment size, end motifs, nucleosome footprints and copy-number profiles provide orthogonal information to point mutations.
- Better for discovery: You can explore new fragmentation-based biomarkers without redesigning targeted panels.
- Robust at low tumour fraction: Fragmentation patterns can remain detectable even when tumour-derived cfDNA is a small proportion of total cfDNA.
Enable scalable, cost-conscious studies
- Single WGS assay per sample gives you genome-wide fragmentomics features, CNV context and basic variant calls.
- Coverage levels are adjustable, allowing you to balance per-sample cost against feature depth.
- Same data, multiple analyses: The same WGS dataset can support initial discovery, model training and later refinement without re-sequencing.
Gain insight into tissue-of-origin and epigenetic context
- Nucleosome footprints around promoters and enhancers reflect chromatin accessibility and gene activity patterns in contributing tissues.
- End motif and preferred-end signatures can highlight nuclease activity and tissue-specific cleavage behaviour.
- Integration with other research readouts (sample metadata, imaging or other omics) supports multi-modal models for early detection, tissue-of-origin classification and prognostic research based on cfDNA fragmentomics analysis.
For teams running liquid biopsy projects, cell-free DNA fragmentomics turns a single WGS assay into a multidimensional data source.
This helps reduce per-project risk, extract more insight from each sample, and generate results that are easier to translate into follow-up studies and validation workflows.
Service Overview
CD Genomics cfDNA Fragmentomics Sequencing Service at a Glance
CD Genomics offers an end-to-end cfDNA fragmentomics sequencing service built around WGS of plasma cell-free DNA and a specialised fragmentomics analysis pipeline.
The service is designed for research teams who need robust, scalable cfDNA fragmentomics analysis without building new wet-lab or bioinformatics infrastructure.
Core service components
- Sample type
Human plasma samples containing circulating cell-free DNA suitable for liquid biopsy and fragmentomics research.
- Sequencing technology
High-quality WGS of plasma cfDNA, using paired-end sequencing to accurately reconstruct fragment start and end positions.
- Coverage options
Flexible WGS depth (for example 1–5×) to match your study design, cohort size and budget, with the possibility to extend coverage when integrated with additional analyses.
- Fragmentomics analysis pipeline
A cfDNA-focused workflow that derives key fragmentomics metrics such as fragment size profiles, end motifs, nucleosome footprints and CNV/SNP signals from the same WGS dataset.
Designed for liquid biopsy and multi-cancer research
By combining plasma cfDNA WGS with a dedicated fragmentomics pipeline, this service turns each sample into a rich, multi-feature dataset.
You can use these outputs to support discovery research, train prediction models and generate publication-ready figures based on cfDNA fragmentomics sequencing.
All services are delivered for research use only.
Applications
Key Applications of cfDNA Fragmentomics
Our cfDNA fragmentomics sequencing service is designed for research across oncology, transplantation and prenatal fields.
By extracting fragmentation, copy-number and chromatin-related features from plasma cfDNA WGS, you can address diverse liquid biopsy questions without redesigning your assay for each project.
Multi-cancer early detection and screening research
- Use cell-free DNA fragmentomics sequencing to compare fragmentation patterns between non-disease and disease cohorts.
- Train and validate models that combine FSD, FSR, end motifs, nucleosome footprints and CNV signals.
- Explore how cfDNA fragmentomics features change with disease stage, risk factors or treatment exposure.
Tumour tissue-of-origin and disease localisation
- Analyse nucleosome footprints and fragmentation profiles around tissue-specific promoters and enhancers.
- Infer which tissues contribute most to the cfDNA pool under different biological conditions.
- Support research into tumour tissue-of-origin and disease localisation based on fragmentomics signatures.
Transplant rejection and graft injury research
- Quantify cfDNA fragmentation patterns associated with donor-derived and recipient-derived DNA.
- Monitor changes in fragment size, CNV pattern and nucleosome signal over time.
- Investigate fragmentomics-based markers that may correlate with graft injury, immune activation or rejection events in research cohorts.
Pregnancy and prenatal research
- Combine cfDNA fragmentomics metrics with existing prenatal research data.
- Study how fragment size profiles, nucleosome footprints and tissue-of-origin signals relate to fetal fraction and pregnancy outcomes.
- Develop fragmentomics-based hypotheses for pregnancy complications in non-invasive prenatal research settings.
Prognostic and treatment-response biomarker exploration
- Integrate cfDNA fragmentomics analysis with other omics, imaging and outcome data.
- Search for fragmentation signatures linked to prognosis, response or minimal residual disease at the research stage.
- Prioritise candidate biomarker panels for further validation in independent cohorts.
Sequencing
Sequencing Strategy: Whole-Genome Sequencing of Plasma cfDNA
Our cfDNA fragmentomics sequencing service is built on high-quality WGS of plasma cell-free DNA.
The laboratory workflow is optimised to preserve native fragment length patterns and accurately reconstruct fragment start and end positions for downstream cfDNA fragmentomics analysis.
Wet-lab workflow overview
- Plasma cfDNA extraction
Plasma samples are processed using cfDNA-focused extraction methods that enrich short, circulating DNA fragments and minimise contamination from high–molecular-weight genomic DNA.
This helps maintain clean fragment length distributions and reduces background noise in size- and end-based analyses.
- Library preparation for WGS
We prepare WGS libraries with protocols adapted for cfDNA, with careful control of fragment repair, adapter ligation and PCR steps.
The goal is to preserve original fragment boundaries as much as possible, so fragment size, end motifs and nucleosome footprints can be reliably measured.
- Paired-end WGS of plasma cfDNA
Libraries are sequenced in paired-end mode, allowing precise reconstruction of each fragment's start and end coordinates across the genome.
Typical WGS coverage levels (for example 1–5×) are configured according to your cohort size, feature requirements and budget.
Configurable WGS parameters
To align with different cfDNA fragmentomics sequencing study designs, we offer:
- Flexible coverage tiers for pilot, discovery and larger screening cohorts.
- Standard short-read, paired-end configurations suitable for fragment size and end motif analysis.
- Options to coordinate WGS settings with parallel assays (for example, methylation or targeted sequencing) within the same project.
This plasma cfDNA WGS setup is optimised for fragment size, end motif and nucleosome footprint analysis in cfDNA fragmentomics projects.
Bioinformatics
cfDNA Fragmentomics Bioinformatics Analysis: What We Profile
Once plasma cfDNA WGS is complete, all data pass through our dedicated cfDNA fragmentomics analysis pipeline.
This workflow turns raw reads into curated fragmentation, chromatin and copy-number features that are ready for statistical analysis and modelling.
Data processing and quality control
- Raw data QC
We assess base quality, adapter content, duplication rate and mapping performance.
This ensures only high-quality reads enter fragmentomics calculations and downstream cfDNA fragmentomics analysis.
- Alignment and fragment reconstruction
Reads are aligned to the reference genome, and paired-end information is used to reconstruct individual DNA fragments.
Accurate fragment start and end coordinates are essential for fragment size, end motif and nucleosome footprint metrics.
- Fragmentomics-specific QC checks
We review fragment length histograms, mono- and di-nucleosome peaks, GC bias, and other pattern-based indicators.
These checks help confirm that sample processing and sequencing preserved genuine cfDNA fragmentation profiles.
Core cfDNA fragmentomics features
From the processed WGS data, we extract multiple feature classes commonly used in cfDNA fragmentomics sequencing projects:
- Fragment size distribution (FSD) and fragment size ratio (FSR)
Global and locus-specific length profiles summarise how fragment sizes differ between regions, conditions or time points.
- End motifs and breakpoint patterns
Sequence motifs at fragment ends and breakpoints reveal cleavage preferences and nuclease-driven signatures.
- Preferred ends and jagged ends
Recurrent start or end sites, and the presence of jagged single-stranded ends, provide additional structure and accessibility information.
- Nucleosome footprints
Oscillating coverage around promoters, enhancers and accessible regions reflects nucleosome positioning and chromatin state in contributing tissues.
- Copy-number variation (CNV) and SNP profiles
Broad CNV landscapes and basic variant calls add genetic context that complements fragmentation-based signals.
Analysis-ready outputs for downstream research
Our bioinformatics team delivers outputs that integrate smoothly into your existing workflows:
- Fragment-level and region-level feature tables suitable for statistical testing and model training.
- Genome browser tracks for nucleosome footprints and fragmentation density, useful for figure generation.
- Summary reports that document methods, key QC metrics and main cfDNA fragmentomics features derived from your WGS data.
With this pipeline, you move from raw reads to interpretable cfDNA fragmentomics sequencing results without building and maintaining your own bioinformatics infrastructure.
Deliverables
Deliverables: What You Receive
Our service takes you from plasma cfDNA WGS to clear, analysis-ready cfDNA fragmentomics results.
1. Sequencing data
- FASTQ files
Demultiplexed raw reads for each sample.
- Aligned BAM/CRAM + index
Reads mapped to the reference genome, ready for genome browser inspection or custom analysis.
2. Fragmentomics and genomic outputs
- Variant and CNV files
Basic SNP/indel calls (VCF) and genome-wide copy-number profiles.
- Fragmentomics feature tables
Structured matrices with FSD, FSR, end motifs, breakpoint patterns, preferred/jagged ends, nucleosome footprints and other agreed cfDNA fragmentomics metrics.
- Genome tracks
bigWig or similar tracks for nucleosome footprints and fragmentation density at specific loci.
3. Summary documents
- Project summary report
Methods, key QC metrics and an overview of main fragmentomics features.
- Standard plots
Core visualisations such as fragment length histograms, FSR comparisons, nucleosome footprint profiles and CNV plots that you can adapt for slides and manuscripts.
These deliverables are structured so you can quickly check data quality, integrate cfDNA fragmentomics sequencing results into your models and communicate findings across your team.
Example cfDNA Fragmentomics Outputs (Demo Data)
The figures below illustrate typical fragment size distribution, nucleosome footprints and multi-feature cancer prediction results generated by our cfDNA fragmentomics analysis pipeline.
Chromosome fragment size distribution (FSD)
Nucleosome distribution
ROC curves and feature heatmap
Sample
Sample Requirements
Our cfDNA fragmentomics sequencing service currently accepts the following sample type:
| Item |
Requirement |
| Sample type |
Human plasma |
| Volume |
Recommended ≥ 2 mL plasma per sample |
Quality Control and Data Reliability
Reliable cfDNA fragmentomics sequencing depends on both laboratory and bioinformatics controls.
CD Genomics applies multi-layer QC to protect fragment length patterns and downstream analysis quality.
Laboratory QC
- Input assessment
We review plasma volume and cfDNA yield to confirm suitability for WGS-based fragmentomics.
- Library quality
Library size profiles, adapter content and complexity are checked to minimise artefacts that could distort fragment size or end motif metrics.
- Sequencing performance
Key indicators such as read depth, Q30 scores and duplication rates are monitored for every run.
Fragmentomics-specific QC
- Fragment length distributions
We inspect fragment length histograms, including mono- and di-nucleosome peaks, to confirm expected cfDNA patterns.
- Coverage and GC bias checks
Coverage uniformity and GC bias are evaluated to support robust CNV and nucleosome footprint analysis.
- Consistency across samples
Basic cross-sample comparisons help identify outliers that may reflect pre-analytical or technical issues.
QC summaries are included in your project report, so you can quickly judge whether cfDNA fragmentomics results meet the requirements of your study before moving into downstream modelling and interpretation.
Integrating Analysis
Integrating cfDNA Fragmentomics With Other cfDNA Assays
For many projects, cfDNA fragmentomics sequencing is one layer in a broader liquid biopsy strategy.
CD Genomics can coordinate plasma cfDNA WGS with other cfDNA assays within the same study to create richer, multi-dimensional datasets.
Combine fragmentomics with cfDNA methylation
- What we offer
Align cfDNA fragmentomics outputs from WGS with separate cfDNA methylation assays (such as whole-genome bisulfite sequencing or targeted methylation panels).
- Why it helps
Fragmentation features highlight chromatin structure and nuclease activity, while methylation profiles capture CpG-level epigenetic changes.
- What you gain
A more complete view of regulatory changes in disease or development, supporting stronger biomarker hypotheses and multi-modal models.
Integrate with mutation or targeted panels
- What we offer
Coordinate cfDNA WGS for fragmentomics with existing mutation panels run on the same or matched samples.
- Why it helps
Variants, CNV profiles and fragmentomics signals become complementary inputs rather than separate, disconnected datasets.
- What you gain
You can evaluate whether adding cfDNA fragmentomics analysis improves model performance, risk stratification or early signal detection in your research cohorts.
All such combinations are planned and executed for research use only, and can be tailored to your assay portfolio, budget and timelines.
Advantages
Why Partner With CD Genomics for cfDNA Fragmentomics
Choosing a cfDNA partner is not only about running WGS.
It is about getting reliable cfDNA fragmentomics sequencing data that your team can trust and use.
Specialised in cfDNA and fragmentomics
- We focus on whole-genome sequencing of plasma cfDNA and cfDNA fragmentomics analysis, not generic DNA sequencing alone.
- Our pipelines are tuned for fragment size, end motifs, nucleosome footprints and CNV, rather than only variants.
- You gain data products that match current fragmentomics literature and are easier to turn into publications and models.
End-to-end scientific support
- We help you plan coverage, sample size and analysis scope before you start.
- Our team can discuss feature selection, cohort design and basic data interpretation with your scientists.
- This end-to-end support reduces trial-and-error and keeps your cfDNA fragmentomics sequencing project on a clear path.
Flexible and scalable collaboration
- Suitable for small pilot studies, method development projects and large multi-centre cohorts.
- Analysis modules and deliverables can be tailored to your internal capabilities and preferred tools.
- You can start with a focused fragmentomics panel of features and expand as your research questions evolve.
FAQ
FAQs About cfDNA Fragmentomics Sequencing
- Q1. What is cfDNA fragmentomics sequencing?
- Q2. How is cfDNA fragmentomics different from mutation-only liquid biopsy tests?
- Q3. What sequencing depth do I need for cfDNA fragmentomics?
- Q4. Can I use the same data for both fragmentomics and variant/CNV analysis?
- Q5. What kind of projects are a good fit for cfDNA fragmentomics?
- Q6. Is this cfDNA fragmentomics service suitable for diagnosis or patient management?
References
- Wan, J., Massie, C., Garcia-Corbacho, J. et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer 17, 223–238 (2017).
- Tsui, WH Adrian, Peiyong Jiang, and YM Dennis Lo. "Cell-free DNA fragmentomics in cancer." Cancer Cell (2025).
- Ding, Spencer C., and YM Dennis Lo. "Cell-free DNA fragmentomics in liquid biopsy." Diagnostics 12.4 (2022): 978.
- Adalsteinsson, V.A., Ha, G., Freeman, S.S. et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun 8, 1324 (2017). https://doi.org/10.1038/s41467-017-00965-y
- Cristiano, S., Leal, A., Phallen, J. et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 570, 385–389 (2019).
- Sun K, Jiang P, Cheng SH, 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. doi:10.1101/gr.242719.118
- Wang S, Meng F, Li M, et al. Multidimensional Cell-Free DNA Fragmentomic Assay for Detection of Early-Stage Lung Cancer. Am J Respir Crit Care Med. 2023;207(9):1203-1213. doi:10.1164/rccm.202109-2019OC
