Sample Submission Guidelines
Turn Plasma cfDNA into Multi-Layer Research Evidence
Cell-free DNA is a short-fragment DNA population released into blood and other biofluids. In research settings, cfDNA can carry genomic, epigenomic, fragmentomic, and sample-context information. That makes it useful when teams need molecular evidence from plasma or other biofluid samples, especially when tissue sampling is limited or repeated sampling is important.
Our cfDNA Detection and Analysis Platform is designed to help you move from sample planning to interpretable results. We help your team choose the right assay route, apply cfDNA-specific QC, generate sequencing data, and organize the findings into a report your R&D team can actually use.
Why cfDNA Is More Than a Mutation Signal
Many teams first think of cfDNA as ctDNA mutation detection. That is one important use case, but cfDNA can provide broader research evidence.
Depending on the project, cfDNA may be used to study:
- SNVs and indels
- Copy number changes
- Methylation patterns
- Fragment size distribution
- Fragment end motifs
- Nucleosome-related footprints
- Longitudinal signal changes
- Vector-related or integration-site evidence
- Tissue-origin or cell-type deconvolution research
This wider view is important for oncology biomarker discovery, translational research, CGT safety research, preclinical studies, and serial sample analysis.
Where This Platform Fits in Translational and CGT Research
A cfDNA platform is most useful when your project needs molecular information from plasma or other biofluids, when tissue sampling is difficult, or when multiple timepoints need to be compared under a consistent analysis framework.
We support research questions such as:
- Which cfDNA features differ between study groups?
- Are mutation or CNV signals detectable in serial plasma samples?
- Do methylation patterns suggest tissue-origin or cell-type changes?
- Are fragmentomic features useful for sample comparison?
- Can plasma cfDNA support preclinical longitudinal monitoring?
- Is vector-related analysis needed for CGT or gene therapy research?
For broader liquid biopsy research context, our Liquid Biopsy Solutions page provides additional service background.
What We Can Detect and Analyze from cfDNA
cfDNA analysis works best when the platform is modular. Some studies need targeted mutation detection. Others need low-pass WGS for CNV or fragmentomics. Some require methylation profiling or serial sample comparison. CGT projects may also need vector-related analysis.
We help your team select the assay layer that matches the research question instead of forcing every project into the same workflow.
Mutation and Small Variant Detection
Targeted cfDNA sequencing can support research focused on known genes, hotspots, or custom genomic regions. It is most useful when the study question involves SNVs, indels, selected variants, or defined biomarker regions.
- Variant tables
- Read support summaries
- Allele fraction estimates where applicable
- Coverage summaries
- Sample-level variant overview
- Report notes for research interpretation
This approach is a good fit when your team already knows the genomic regions that matter.
Copy Number and Genome-Wide Signal Analysis
Low-pass WGS or other genome-wide approaches may be used when the research question involves CNV, CNA, broad genomic imbalance, or multi-feature cfDNA analysis.
- Genome-wide CNV or CNA plots
- Segment-level copy-number tables
- Chromosome-level signal summaries
- QC notes on coverage and noise
- Integration with fragmentomics where appropriate
Low-pass WGS can also support cfDNA fragmentomics when the study is designed around genome-wide fragment-level features.
Methylation and Tissue-Origin Research
cfDNA methylation profiling can help researchers study epigenetic patterns, tissue-origin signals, or cell-type deconvolution in research contexts. This can be useful when sequence variation alone does not provide enough biological context.
- Methylation signal tables
- Differential methylation results where applicable
- Feature matrices for downstream modeling
- Tissue-origin or cell-type deconvolution research outputs when supported by the design
- Visual summaries of methylation patterns
Fragmentomics and Nucleosome-Related Features
cfDNA fragmentomics examines properties of cfDNA fragments rather than only their sequence. These features may include fragment length, short-to-long fragment ratios, end motifs, breakpoint patterns, nucleosome-related footprints, and genome-wide fragmentation patterns.
CD Genomics provides cfDNA Fragmentomics Service by Low-Pass WGS Sequencing for projects where fragment-level features are central.
- Fragment size distribution
- Fragment length ratios
- End motif patterns
- Nucleosome-related footprint features
- Genome-wide fragmentation summaries
- Feature matrices for downstream analysis
Longitudinal Monitoring and Serial Sample Comparison
Many cfDNA studies become more informative when multiple timepoints are included. Serial plasma samples can help researchers compare signal changes over time, across conditions, or between study groups.
- Timepoint-level feature tables
- Variant or CNV trend plots
- Methylation or fragmentomics trend summaries
- Sample-to-sample distance visualization
- Longitudinal heatmaps
- Research interpretation notes
We do not over-interpret serial cfDNA data. Our goal is to organize molecular trends clearly so your team can decide which patterns deserve deeper review.
Vector-Related and Integration Site Research Applications
For CGT, gene therapy, or in vivo cell therapy research, cfDNA may support specialized questions related to vector-derived sequences, vector-genome junction evidence, or integration-site research.
This is one module of the platform, not the whole platform.
- Vector-related sequence detection
- Vector-genome junction review
- Integration-site mapping where technically supported
- Clonal abundance trend analysis
- Longitudinal comparison across plasma samples
- Research reports for safety assessment discussions
This module should be selected only when the research question requires vector-related evidence.
Our Platform Capability Advantage for cfDNA Projects
cfDNA projects are technically sensitive. Sample handling, cfDNA yield, library bias, molecular barcodes strategy, sequencing depth, and bioinformatics preprocessing can all influence interpretation. We help you plan these details before data generation begins, because the best report starts with the right study design.
cfDNA-Specific Wet-Lab Planning
cfDNA is often low-input and fragmented. It can also be affected by hemolysis, genomic DNA contamination, plasma separation conditions, storage, and freeze-thaw history.
- Sample type review
- Plasma preparation considerations
- cfDNA extraction strategy
- Input feasibility review
- Fragment size QC
- gDNA contamination check
- Library strategy selection
- Sample metadata review
The goal is simple: reduce avoidable variability before sequencing starts.
NGS Strategy Matched to the Research Question
There is no single cfDNA assay that answers every question. A targeted panel, low-pass WGS, methylation workflow, fragmentomics design, or specialized module may be appropriate depending on the project.
- Targeted vs genome-wide question
- Mutation vs epigenetic feature
- Single-timepoint vs longitudinal design
- Oncology vs CGT vs preclinical research
- Known biomarker vs discovery question
- Need for molecular barcodes-aware consensus analysis
- Required report outputs
This helps keep the project focused and avoids spending sample or budget on data layers that will not answer the main question.
Bioinformatics That Turns cfDNA Reads into Reviewable Evidence
cfDNA sequencing data require careful processing. Generic tissue-DNA pipelines may not fully address cfDNA-specific biases, fragment features, low-input signal, or serial sample comparison.
- Alignment and coverage review
- molecular barcodes-aware consensus generation where applicable
- Variant calling or CNV/CNA analysis
- Methylation signal processing
- Fragmentomics feature extraction
- Longitudinal comparison
- Specialized vector-related analysis
- QC and interpretation notes
We structure the outputs so both scientists and bioinformaticians can review the evidence behind the figures.
Flexible Scope Without Overbuilding the Study
A broad platform does not mean every project needs every module. We help you avoid unnecessary complexity while keeping the study strong enough to answer the research question.
- Use targeted cfDNA sequencing for defined variants or regions.
- Use low-pass WGS for CNV/CNA or fragmentomics.
- Use methylation profiling for epigenetic or tissue-origin research.
- Use fragmentomics when fragment-level features matter.
- Use a longitudinal design when serial samples are the core of the study.
- Add vector-related analysis only when the research question requires it.
cfDNA Detection Workflow with QC Checkpoints
Our workflow follows the sample from study design to final report. Each step includes a QC checkpoint because cfDNA data are strongly affected by pre-analytical handling, library strategy, sequencing quality, and analysis choices.
Step 1 — Study Design and Assay Scope Review: We begin by reviewing the biological question and deciding which cfDNA modules are appropriate. We may ask for sample source, disease model or research context, plasma or biofluid type, single-timepoint or longitudinal design, targeted regions or genome-wide analysis needs, methylation or fragmentomics goals, CGT or vector-related research requirements, matched tissue or matched normal availability, and existing data format if reanalysis is requested. QC checkpoint: We confirm that the assay strategy matches the research question and available sample type.
Step 2 — Plasma / Biofluid Sample Intake and cfDNA Extraction: Plasma or other biofluid samples are reviewed before extraction. If whole blood is submitted for plasma preparation, collection tube type and processing conditions should be reviewed in advance. cfDNA extraction focuses on recovering short DNA fragments while reducing contamination from high-molecular-weight genomic DNA. QC checkpoint: We review sample condition, volume, hemolysis risk, extraction feasibility, and metadata completeness.
Step 3 — cfDNA QC and Library Strategy Selection: After extraction, cfDNA quality is reviewed before library construction. Fragment size distribution, yield, and gDNA contamination risk can affect downstream performance. Library design depends on the analysis module, including targeted cfDNA sequencing, low-pass WGS, methylation profiling, fragmentomics, molecular barcodes-aware variant analysis, or vector-related specialized analysis. QC checkpoint: We check whether cfDNA quality and input are suitable for the selected library strategy.
Step 4 — Sequencing and Primary Data QC: Sequencing generates the raw data used for downstream analysis. QC may include read quality, mapping rate, duplication level, coverage profile, on-target rate where applicable, molecular barcodes family structure where applicable, and sample identity review. QC checkpoint: We evaluate whether the sequencing data are suitable for the planned analysis module.
Step 5 — Bioinformatics Analysis and Report Delivery: Bioinformatics converts cfDNA sequencing data into reportable results. Analysis may include variant detection, CNV/CNA analysis, methylation profiling, fragmentomics feature extraction, longitudinal comparison, or specialized vector-related analysis. The final report organizes methods, QC results, feature tables, figures, and interpretation notes. Your team receives both the data outputs and a structured summary for R&D review. QC checkpoint: Before delivery, we review consistency across metadata, QC summaries, result tables, figures, and final interpretation.
Sample Requirements for cfDNA Detection Projects
Sample requirements depend on assay type, project design, biofluid source, and expected cfDNA yield. Final requirements should be confirmed after project review.
| Sample Type | Recommended Input | Collection Container | Shipping | QC Checkpoints | Notes |
|---|---|---|---|---|---|
| Plasma cfDNA | Input confirmed after project review | cfDNA-compatible tube or plasma aliquot | Cold chain or dry ice as advised | cfDNA yield, fragment size, gDNA contamination | Provide disease model, timepoint, and intended assay module |
| Whole blood for plasma preparation | Collection volume confirmed after project review | EDTA or cfDNA stabilization tube as advised | Condition depends on tube type and processing plan | Hemolysis, processing time, plasma separation quality | Use when plasma preparation support is needed |
| Animal model plasma | Input confirmed after project review | Project-specific | Cold chain or dry ice as advised | Plasma volume, cfDNA yield, fragment size | Useful for preclinical longitudinal studies |
| Other biofluids | Feasibility confirmed after sample review | Project-specific | As advised | cfDNA recovery, inhibitor risk, sample matrix compatibility | Include only after technical feasibility review |
| Existing sequencing data | FASTQ/BAM plus metadata | Digital files | Secure file transfer | File integrity, metadata completeness, reference compatibility | Useful for reanalysis or second-opinion bioinformatics |
For serial sampling projects, consistency matters. Differences in tube type, processing time, storage, extraction method, and sequencing strategy can create batch effects that complicate interpretation.
Bioinformatics Analysis and Deliverables
The "analysis" part of a cfDNA platform is not optional. The value of the project depends on how well the sequencing data are transformed into structured, reviewable results.
Minimum Deliverables
- Raw sequencing files
- Sample and library QC summary
- cfDNA fragment size / quality summary
- Alignment and coverage summary
- Assay-specific result tables
- Variant table where applicable
- CNV/CNA summary where applicable
- Methylation profile where applicable
- Fragmentomics feature table where applicable
- Longitudinal comparison plots where applicable
- Final report with methods, QC, results, and interpretation notes
Optional Add-ons by Research Question
- molecular barcodes-aware consensus analysis
- Targeted panel design support
- Low-pass WGS fragmentomics
- Methylation profiling
- Tissue-of-origin or cell-type deconvolution research
- CNV/CNA analysis
- Longitudinal trend analysis
- Vector-related or integration-site module
- Matched tissue or matched normal comparison
- Custom biomarker panel support
- Multi-feature modeling-ready data matrix
How Results Are Organized for R&D Review
We organize cfDNA results around the question your team needs to answer.
- Variant-focused projects receive variant tables, coverage summaries, and confidence notes.
- CNV/CNA projects receive genome-wide plots and segment-level tables.
- Methylation projects receive methylation profiles, feature matrices, or deconvolution outputs where supported.
- Fragmentomics projects receive fragment-length, end-motif, nucleosome-related, or multi-feature outputs.
- Longitudinal projects receive timepoint-level trend plots and sample comparison summaries.
- Vector-related projects receive module-specific evidence tables when technically supported.
We avoid unsupported claims. The report is written to support research interpretation and follow-up planning.
Choosing the Right cfDNA Strategy: Targeted Panel, Low-Pass WGS, Methylation, Fragmentomics, or Specialized Analysis
A strong cfDNA project starts with the right assay choice. The best strategy depends on the feature type, sample amount, research goal, and analysis depth required.
| Strategy | Biological Question Answered | Best Sample Type | Strengths | Limitations | Typical Deliverables |
|---|---|---|---|---|---|
| Targeted cfDNA sequencing | Are known variants or selected regions detectable? | Plasma cfDNA | Focused, efficient, compatible with defined biomarker questions | Limited to selected regions | Variant table, coverage summary, allele fraction where applicable |
| Low-pass WGS | Are genome-wide CNV/CNA or fragmentomics features informative? | Plasma cfDNA | Genome-wide view, supports CNV and fragment-level features | Lower resolution for small variants | CNV/CNA plots, fragmentomics features |
| cfDNA methylation profiling | Do methylation patterns suggest epigenetic or tissue-origin signals? | Plasma cfDNA | Captures epigenetic information | Requires appropriate methylation workflow and reference strategy | Methylation matrix, DMR table, deconvolution outputs where applicable |
| cfDNA fragmentomics | Are fragment size, end motif, or nucleosome-related features informative? | Plasma cfDNA / low-pass WGS data | Adds feature layer beyond sequence variants | Sensitive to library and preprocessing choices | Fragment size, motif, footprint, feature matrix |
| Standard ctDNA panel | Are oncology-associated variants present in a predefined panel? | Plasma cfDNA | Focused oncology biomarker approach | Not designed for broad multi-feature cfDNA research | Panel report, variant summary |
| Tissue DNA analysis | What is the tissue-specific genomic context? | Tissue DNA | Local tissue evidence | Not serial liquid biopsy | Variant, CNV, methylation or other tissue-based outputs |
| Product gDNA / cellular DNA analysis | What is present in a cell product or cellular sample? | Cellular DNA | Useful for product-level genomic evidence | Not blood-based cfDNA monitoring | Product-level genomic results |
| Specialized integration-site module | Is vector-genome junction evidence needed? | Project-dependent cfDNA or DNA samples | Supports CGT/vector-related research questions | Not needed for most cfDNA projects | Integration-site table, junction evidence, trend analysis where supported |
Selection Rules by Research Question
- Use targeted cfDNA sequencing when known variants or defined genomic regions are the core question.
- Use low-pass WGS when genome-wide CNV/CNA or fragmentomics is needed.
- Use methylation profiling when tissue-origin or epigenetic biomarker discovery matters.
- Use fragmentomics when fragment size, end motif, nucleosome footprint, or multi-feature modeling is important.
- Use longitudinal design when serial plasma samples are central to the project.
- Use vector-related integration-site analysis only when the research question involves vector-genome junction evidence.
- Use tissue or cellular DNA analysis when local tissue context or product-level genomic evidence is required.
- Avoid overbuilding a multi-module platform if the project question can be answered with one focused assay.
Applications in Oncology, CGT, Gene Therapy, and Translational Research
The cfDNA Detection and Analysis Platform can support a broad range of research programs. We tailor the assay strategy to the biological question rather than forcing every project into the same workflow.
Oncology Biomarker Discovery
cfDNA can support research into tumor-associated variants, CNV/CNA signals, methylation patterns, fragmentomics, or multi-feature biomarker discovery. The platform can be adapted for defined target regions or broader discovery-oriented workflows.
Methylation and Tissue-Origin Research
Methylation profiling can support tissue-origin and cell-type deconvolution research when the assay and reference strategy are appropriate. This may be useful when genomic variation alone does not provide enough biological context.
cfDNA Fragmentomics and Multi-Feature Modeling
Fragmentomics can add another layer of information to cfDNA sequencing. Fragment size, end motif, nucleosome-related footprints, and genome-wide fragmentation patterns may support multi-feature research models.
CGT, Gene Therapy, and Vector-Related Research
For CGT and gene therapy research, cfDNA can be evaluated as part of a broader safety research strategy. Specialized vector-related analysis may be added when the project involves vector-derived sequences, vector-genome junctions, integration-site research, or longitudinal clonal trend questions.
Preclinical and Longitudinal Sample Studies
Animal model and serial plasma studies can benefit from consistent cfDNA workflows. Longitudinal designs allow teams to compare molecular features across timepoints, treatment conditions, or study groups.
The platform is especially useful when a project needs repeatable sample processing, stable analysis rules, and reportable trends across multiple samples.
References
- A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data
- Systematic evaluation of methylation-based cell type deconvolution methods for plasma cell-free DNA
- Cancer liquid biopsies by Oxford Nanopore Technologies sequencing of cell-free DNA: from basic research to clinical applications
- Single-molecule methylation profiles of cell-free DNA in cancer with nanopore sequencing
- Liquid Biopsy Based on Cell-Free DNA and RNA
- Cell-free nucleic acid fragmentomics: A non-invasive window into cellular epigenomes
Demo Results: What Your cfDNA Report May Include
The final report should make cfDNA data easier to inspect and discuss. Demo outputs vary by assay module, but the following examples show the types of visual summaries we can prepare.
Variant and Copy-Number Summary Dashboard
A variant/CNV dashboard can combine targeted variant results with genome-wide or segment-level signal summaries.
Typical outputs may include SNV/indel table, coverage summary, allele fraction view where applicable, CNV/CNA genome plot, sample-level QC notes, and confidence or review flags.
This gives your team a quick view of both molecular findings and the evidence behind them.
Methylation / Fragmentomics Feature Profile
Methylation and fragmentomics projects often require feature-level summaries rather than a single result table.
Typical outputs may include methylation feature matrix, differential methylation summary where applicable, fragment size distribution, fragment length ratio plots, end motif feature table, nucleosome-related feature visualization, and multi-feature modeling-ready matrix.
These outputs help organize feature-rich cfDNA data into patterns that can be reviewed and compared.
Longitudinal cfDNA Monitoring View
For serial samples, results can be organized by timepoint.
Typical outputs may include timepoint-level feature trends, variant or CNV trajectory plots, methylation or fragmentomics trend heatmaps, sample-to-sample distance plots, study-group comparison summaries, and notes on batch effects or sample consistency.
These demo outputs are designed to help your team review patterns, not to make unsupported clinical conclusions.
FAQ: Planning a cfDNA Detection and Analysis Project
1. Is this platform only for ctDNA mutation detection?
No. Mutation detection is one module. We can also support CNV/CNA analysis, methylation profiling, fragmentomics, longitudinal comparison, and specialized vector-related research applications when technically appropriate.
2. What can be analyzed from cfDNA besides mutations?
Depending on the assay design, cfDNA can be analyzed for copy-number changes, methylation patterns, fragment size distribution, end motifs, nucleosome-related features, tissue-origin signals, longitudinal trends, and vector-related evidence.
3. When should we choose targeted cfDNA sequencing?
Choose targeted sequencing when the project focuses on known variants, selected genes, hotspots, or custom genomic regions. It is best when the question is defined and does not require genome-wide feature analysis.
4. When should we choose low-pass WGS?
Choose low-pass WGS when genome-wide CNV/CNA or fragmentomics features are important. It is especially useful when your team wants a broader cfDNA signal beyond a predefined targeted panel.
5. What is cfDNA fragmentomics?
cfDNA fragmentomics studies the properties of cfDNA fragments, such as fragment length, end motifs, genomic positioning, and nucleosome-related patterns. It adds a feature layer beyond mutation detection.
6. When is cfDNA methylation profiling useful?
Methylation profiling is useful when the research question involves epigenetic patterns, tissue-origin inference, or cell-type deconvolution. It should be selected when methylation information directly supports the study goal.
7. Can serial plasma samples be compared?
Yes. Serial plasma samples can be compared when collection, processing, library strategy, and analysis rules are kept consistent. Longitudinal analysis can show how selected cfDNA features change across timepoints.
8. Can the platform support CGT or vector-related research?
Yes, when the project design supports it. Vector-related or integration-site analysis can be added as a specialized module for CGT, gene therapy, or in vivo cell therapy research. It is not required for most general cfDNA projects.
9. What sample types can be used?
Common inputs include plasma cfDNA, whole blood for plasma preparation, animal model plasma, selected other biofluids after feasibility review, and existing sequencing data for reanalysis.
10. What bioinformatics outputs are included?
Outputs may include FASTQ files, BAM or CRAM files where applicable, VCF files where applicable, CNV/CNA tables, methylation matrices, fragmentomics feature matrices, QC reports, plots, and a final PDF report.
Literature-Supported Case Example: Robust cfDNA Fragmentomic Feature Extraction
Published Research Highlight
A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data
Journal: Genome Biology
Published: 2025
Source: A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data
This case is based on a 2025 Genome Biology paper that developed a standardized framework for cfDNA fragmentomic feature extraction. It is included because it directly supports the need for cfDNA-specific QC, preprocessing, feature extraction, and transparent reporting.
Background
cfDNA fragmentomics can provide research signals beyond sequence variants. But fragmentomic features are sensitive to library preparation, preprocessing, alignment, trimming, and feature definition. A generic sequencing pipeline may not capture these issues well.
Wang and colleagues addressed this problem by developing a standardized framework for robust cfDNA fragmentomic feature extraction from sequencing data.
Methods
The study compared cfDNA features derived from whole-genome sequencing of ten healthy donors. It evaluated nine library kits and ten data-processing routes. The authors then validated feature behavior across 1182 plasma samples from published studies.
The paper introduced the Trim Align Pipeline for cfDNA-specific preprocessing and the cfDNAPro R package for feature calculation and visualization.
Figure 1 in A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data shows the study overview, including plasma cfDNA extraction, library preparation, WGS, preprocessing, feature extraction, and visualization.
Results
The study showed that library and data-processing choices can affect cfDNA feature quantification. It also presented cfDNAPro utilities for extracting and visualizing fragmentomic features, including fragment length, fragment end motif, copy number aberration, and SNV features.
The authors validated feature behavior across 1182 plasma samples, supporting the value of standardized preprocessing and transparent feature extraction in cfDNA analysis.
Figure 1 from Wang et al. shows the study overview, including plasma cfDNA extraction, library preparation, WGS, preprocessing, feature extraction, and visualization.
Conclusion
This study supports a core principle of our cfDNA Detection and Analysis Platform: cfDNA data need cfDNA-specific QC, preprocessing, feature extraction, visualization, and reporting. For R&D teams, this matters because small differences in sample handling, library construction, or analysis routes can change how cfDNA features are interpreted.
Reference
This service is intended for Research Use Only (RUO). It is not intended for clinical diagnosis, treatment selection, or direct patient-management decisions.
