Digital PCR vs qPCR: Choosing the Right Quantification Method

Quantitative PCR (qPCR) and digital PCR (dPCR) both measure nucleic acid targets, but they answer different quantification questions. qPCR compares cycle threshold values against a standard curve to estimate relative or absolute amounts, while dPCR partitions a sample into thousands of individual reactions and counts positive partitions to calculate absolute concentration without a standard curve. For diagnostic laboratories, the choice between these methods affects sensitivity at low target levels, precision across replicates, tolerance to inhibitors, workflow complexity, and cost per result. This article compares the two methods across these dimensions and provides a decision framework for selecting the appropriate approach based on your specific application.

Core Principles of qPCR and dPCR

How qPCR Quantifies Nucleic Acids

Real-time quantitative PCR measures fluorescence accumulation during each amplification cycle. The cycle at which fluorescence rises above background, called the threshold cycle or Ct value, correlates inversely with the starting amount of target nucleic acid. To convert Ct values into quantities, the assay requires a standard curve generated from known concentrations of a calibrator material. The accuracy of qPCR quantification therefore depends on the quality and accuracy of the calibration standards, the efficiency of the amplification reaction, and the consistency of the standard curve across runs.

Relative quantification is a common qPCR application where target amounts are expressed relative to a reference gene or a control sample instead of as absolute copy numbers. This approach is widely used in gene expression studies and does not require a standard curve with absolute values. The qBase framework describes advanced models for relative quantification with proper error propagation across the entire calculation track, addressing the challenge of accurate processing of raw qPCR measurements. For diagnostic applications that require absolute values, such as viral load monitoring, qPCR depends on external calibrators traceable to reference materials.

How dPCR Quantifies Nucleic Acids

Digital PCR partitions the sample into thousands of individual micro-reactions before amplification. After PCR, each partition is scored as positive or negative for the target. The proportion of negative partitions follows a Poisson distribution, allowing calculation of the absolute target concentration without reference to a standard curve. This partitioning strategy is the fundamental difference between the two methods and explains why dPCR provides absolute quantification while qPCR requires calibration.

The partitioning formats differ by platform. Droplet digital PCR (ddPCR) uses water-oil emulsion droplets, while chip-based systems use nanoliter-scale wells on a consumable array. Both formats achieve the same analytical principle of counting positive and negative partitions. The choice between platforms affects throughput, consumables cost, and hands-on time, but the quantification principle remains consistent.

Sensitivity and Precision at Low Target Concentrations

Detection at Low Abundance

The sensitivity difference between qPCR and dPCR becomes most apparent when target concentrations are very low. In a multicenter study of BCR::ABL1 p210 transcript detection, digital PCR showed a higher detection rate than real-time quantitative PCR for samples in the 0.001% to 0.01% range, with detection rates of 85.56% versus 68.00% respectively. This finding demonstrates that dPCR can outperform qPCR when targets are present at extremely low levels, which matters for applications such as minimal residual disease monitoring.

For urine-based bladder cancer detection, a digital PCR assay targeting TERT promoter and FGFR3 hotspot mutations showed higher sensitivity than the earlier qPCR version and urine cytology. The dPCR assay achieved 75.6% sensitivity versus 51.2% for qPCR and 42.3% for cytology while maintaining 94.0% specificity. The area under the receiver operating characteristic curve was 0.848 for dPCR, exceeding qPCR by 0.122. These results support dPCR as a more sensitive diagnostic adjunct for detecting low-abundance mutations in clinical samples.

Precision and Reproducibility

Digital PCR generally provides better precision than qPCR because each partition is an independent reaction, and the counting statistics are less affected by amplification efficiency variations. In wastewater surveillance for SARS-CoV-2, two commercial dPCR platforms demonstrated statistically equivalent quantification across all targets, with mean differences of 0.12 log copies per liter or less and coefficients of variation ranging from 3.96% to 7.61%. Measurement variability correlated strongly with wastewater treatment plant site instead of platform, indicating that sample matrix characteristics drive precision more than the analytical platform.

For chronic myeloid leukemia monitoring, a qPCR assay showed coefficients of variation of 14.4% and 15.6% at 14.1% IS and 0.233% IS respectively, with linearity across a 5-log range. The same study reported high concordance between qPCR and droplet digital PCR at clinically relevant molecular response thresholds, with concordance of 95.9% at the 0.1% IS threshold when compared to an established qPCR assay and 90.5% when compared to ddPCR. This suggests that both methods can produce clinically actionable results, but the precision characteristics differ.

Absolute versus Relative Quantification

Standard Curve Dependency

The most significant operational difference between qPCR and dPCR is the requirement for a standard curve. qPCR absolute quantification requires calibrators with known concentrations, and the accuracy of results depends on the accuracy of those calibrators. Variability in calibration materials, assay design, extraction methods, and gene targets complicates standardization across laboratories. Digital PCR eliminates this dependency by counting partitions directly, providing absolute quantification without external calibrators.

This distinction has practical consequences for interlaboratory comparability. In the BCR::ABL1 multicenter study, conversion factors were calculated and validated for each laboratory to harmonize results across sites. Both RT-PCR and dPCR showed good consistency among laboratories, with bias within 1.2-fold and 95% limits of agreement within 5-fold. However, the dPCR approach reduces the need for such harmonization because results are inherently absolute instead of relative to a laboratory-specific standard curve.

Applications Requiring Relative Quantification

Relative quantification remains a valid and often preferred approach for gene expression studies where the biological question concerns changes in expression relative to a reference condition. The qBase framework provides universally applicable models for relative quantification with proper error propagation, and this approach is widely used in expression profiling. For applications such as measuring m6A RNA methylation at specific residues, RT-qPCR based approaches have been developed that quantify methylation relative to total transcript levels.

For diagnostic applications, the choice between absolute and relative quantification depends on the clinical question. Viral load monitoring requires absolute values to track disease progression and treatment response. Gene expression studies often only require relative changes. Copy number variation analysis requires accurate ratios of target to reference genes, which can be achieved with either method but benefits from the absolute quantification of dPCR.

Workflow and Operational Considerations

Sample Preparation and Partitioning

Both qPCR and dPCR require nucleic acid extraction, but the downstream workflows differ. qPCR involves setting up reactions in tubes or plates, running the thermal cycling protocol, and analyzing Ct values. dPCR adds a partitioning step before amplification, which requires specialized consumables and instruments. The partitioning step increases hands-on time but provides the analytical benefits of absolute quantification.

The choice of extraction method affects both qPCR and dPCR performance. A systematic comparison of whole blood and dried blood spot collection for malaria molecular surveillance found that DNA recovery was 5 to 10 fold higher for whole blood compared to dried blood spots, resulting in a 2 to 3 fold lower limit of detection for both extraction methods. For whole blood, a magnetic bead based method achieved 88% to 98% DNA recovery compared to 17% to 33% for a spin column based method. These differences in DNA recovery directly affect the limit of detection regardless of whether qPCR or dPCR is used for quantification.

Throughput and Turnaround Time

Throughput differences between qPCR and dPCR affect laboratory workflow planning. In a comparison of two dPCR platforms for wastewater surveillance, the Bio-Rad QX200 processed samples 32% faster at 305 minutes versus 435 minutes per 96 wells, while the QIAGEN QIAcuity offered 29% lower consumables cost at 4.68 versus 6.11 per well. These operational differences matter for laboratories processing large numbers of samples.

qPCR generally offers higher throughput because it does not require a partitioning step and uses standard plate formats. For applications with high sample volumes and established standard curves, qPCR remains the more efficient choice. dPCR becomes more attractive when sample numbers are lower but the need for absolute quantification or sensitivity at low concentrations is higher.

Multiplexing Capabilities

Both methods support multiplexing, but the constraints differ. qPCR multiplexing is limited by the number of fluorescence channels available and the need to optimize primer and probe concentrations to avoid competition between targets. dPCR multiplexing can be more flexible because each partition is scored independently, and the partitioning process reduces competition effects.

A quadruplex ddPCR assay for simultaneous detection of Staphylococcus aureus, Listeria monocytogenes, Cronobacter sakazakii, and Mycobacterium bovis in dairy products demonstrated the multiplexing potential of dPCR. The assay showed high specificity with no cross-reactivity against common dairy-associated bacteria, limits of detection ranging from 7.04 to 10.31 copies per reaction, and coefficients of variation below 14% for intra-assay and 10% for inter-assay repeatability. Notably, the ddPCR assay detected a 25% co-contamination rate compared to 13% by qPCR among 120 dairy samples, suggesting higher sensitivity for low-abundance targets in mixed samples.

Inhibitor Tolerance and Matrix Effects

Humic Acids and Environmental Samples

Environmental samples such as wastewater and soil contain substances that interfere with PCR amplification. Humic acids are a primary interference mechanism in qPCR quantification of SARS-CoV-2 in wastewater surveillance. Higher concentrations of humic acids led to increasing Ct values, which could not be adequately described by polymerase inhibition or DNA template complexation models. Instead, humic acids caused fluorescence damping of the FAM reporter dye, resulting in an overall decrease in fluorescence intensity. The Ct values could be corrected based on end-point fluorescence reduction.

Digital PCR shows reduced sensitivity to inhibition compared to qPCR because the partitioning process dilutes inhibitors across many reactions, and the endpoint readout is less affected by fluorescence quenching. This makes dPCR particularly attractive for environmental surveillance applications where sample matrices are complex and variable. Wastewater-based epidemiology has become essential for tracking pathogen spread, and the choice of quantification method affects the reliability of results.

Food and Agricultural Matrices

Food matrices present similar challenges for PCR quantification. Dairy products contain fats, proteins, and other components that can inhibit amplification. The quadruplex ddPCR assay for dairy pathogens demonstrated that dPCR can achieve reliable quantification in these challenging matrices. For genetically modified organism testing, dPCR enables absolute quantification without calibration standards and shows reduced sensitivity to inhibitors, supporting its use for routine GMO analysis.

The European Union requires mandatory labeling of food and feed products when authorized genetically modified organisms exceed 0.9% per ingredient. Event-specific PCR assays validated according to ISO/IEC 17025 are the reference approach for GMO detection. An in-house validation of a duplex dPCR assay targeting the maize GM event NK603 and the HMG reference gene across three platforms met all Joint Research Centre acceptance criteria, with high specificity, sensitivity with a limit of quantification below 35 copies per reaction, and precision with relative standard deviation and bias below 25%.

Platform Selection and Validation

Comparing dPCR Platforms

Multiple dPCR platforms are commercially available, and the choice between them affects laboratory operations. A blinded comparison of the Bio-Rad QX200 and QIAGEN QIAcuity platforms using 93 archived wastewater influent samples spanning three orders of magnitude in SARS-CoV-2 concentration found statistically equivalent quantification across all targets. Process limits of detection ranged from 2,160 to 2,680 copies per liter for Bio-Rad QX200 and 5,650 to 9,700 copies per liter for QIAGEN QIAcuity for N1 and N2 targets respectively.

A duplex dPCR assay for the NK603 maize event was validated across three platforms: Bio-Rad QX200, Qiagen QIAcuity, and Thermo Fisher QuantStudio Absolute Q. All validation parameters met the Joint Research Centre acceptance criteria, demonstrating that the assay can be used for routine GMO analysis across different platforms. This cross-platform compatibility is important for laboratories that may need to switch platforms or collaborate with other facilities.

For adenovirus quantification, the Qiagen QIAcuityDx platform showed an average bias of -0.31 log copies per milliliter relative to quantitative real-time PCR. Both dPCR systems demonstrated excellent correlation and equivalent analytical sensitivity at 1000 copies per milliliter. Strong agreement was observed across assays for viral loads at or above 1000 copies per milliliter regardless of genotype, supporting the utility of dPCR for calibration and standardization in clinical molecular testing.

Validation Requirements

Method validation is essential for both qPCR and dPCR before routine diagnostic use. The Assay Guidance Manual from the National Center for Advancing Translational Sciences provides a framework for assay development and validation. The Bioanalytical Method Validation Guidance from the U.S. Food and Drug Administration outlines expectations for analytical methods used in regulatory submissions. These documents emphasize the need to establish accuracy, precision, selectivity, sensitivity, reproducibility, and stability for any quantification method.

For dPCR validation, key parameters include limit of blank, limit of detection, limit of quantification, linearity, precision, and trueness. The NK603 maize assay validation demonstrated that all parameters met acceptance criteria, including a limit of quantification below 35 copies per reaction and relative standard deviation and bias below 25%. For qPCR validation, additional parameters include amplification efficiency, standard curve linearity, and calibration traceability.

At a Glance

Practical Workflow Decisions

Step 1: Define the Quantification Question

Determine whether your application requires absolute or relative quantification. Viral load monitoring, minimal residual disease detection, and copy number variation analysis require absolute values. Gene expression studies and some diagnostic applications can use relative quantification. If absolute values are required, dPCR offers the advantage of standard curve independence.

Step 2: Assess Target Concentration Range

Consider the expected concentration of your target in clinical or environmental samples. If targets are expected at very low levels, such as minimal residual disease monitoring or pathogen detection in complex matrices, dPCR provides better sensitivity. The BCR::ABL1 study demonstrated that dPCR had a higher detection rate than RT-PCR for samples in the 0.001% to 0.01% range. For targets present at higher concentrations, qPCR may provide sufficient sensitivity with lower cost.

Step 3: Evaluate Sample Matrix Complexity

Complex matrices such as wastewater, soil, food, and blood contain inhibitors that affect PCR performance. Digital PCR shows reduced sensitivity to inhibition compared to qPCR. For environmental surveillance and food safety applications, dPCR may provide more reliable results. The wastewater surveillance study found that sample matrix characteristics drive precision more than the analytical platform, emphasizing the importance of matrix considerations.

Step 4: Consider Throughput and Cost

Estimate your sample volume and turnaround time requirements. qPCR offers higher throughput and lower consumables cost, making it suitable for high-volume screening applications. dPCR requires a partitioning step and specialized consumables, increasing cost per sample. For laboratories processing large numbers of samples with established standard curves, qPCR remains the more efficient choice.

Step 5: Plan for Validation

Allocate resources for method validation regardless of platform choice. Follow established validation frameworks such as the Assay Guidance Manual and the Bioanalytical Method Validation Guidance. Establish limits of blank, detection, and quantification for your specific assay and matrix combination. Document all validation results for regulatory and quality assurance purposes.

Records and Measurements

Essential Records for qPCR

Maintain records of standard curve parameters including slope, intercept, and R-squared values for each run. Document the source and lot number of calibration materials. Record Ct values for all samples and controls. Track amplification efficiency and note any deviations from established ranges. Document any corrections applied for fluorescence damping or other interference effects.

Essential Records for dPCR

Record the number of partitions analyzed, the number of positive partitions, and the calculated concentration for each sample. Document the partitioning format and platform used. Track the limit of detection and limit of quantification for each assay and matrix combination. Record any samples that fall below the limit of quantification and note the implications for interpretation.

Quality Control Records

Maintain records for positive and negative controls in every run. Document the performance of internal controls and processing controls. For wastewater surveillance, a bovine coronavirus processing control is used to monitor extraction and amplification efficiency. Record any control failures and the corrective actions taken. Document interlaboratory comparison results when available, as these provide valuable information about method harmonization.

Common Failure Patterns

qPCR Failures

Standard curve degradation is a common qPCR failure pattern. Calibrators may degrade over time, leading to inaccurate quantification. Amplification efficiency may drift between runs due to reagent lot changes or instrument variability. Fluorescence damping from matrix components can increase Ct values and lead to underestimation of target concentration. The humic acid interference study demonstrated that fluorescence damping is a primary interference mechanism in wastewater samples.

dPCR Failures

Partitioning failures can occur when samples contain debris or viscous components that interfere with droplet or well formation. Incomplete partitioning leads to inaccurate concentration calculations. Low partition counts reduce the precision of the Poisson-based concentration estimate. Samples with concentrations above the dynamic range of the assay require dilution and reanalysis.

Cross-Platform Variability

Different dPCR platforms may have different limits of detection and quantification for the same assay. The wastewater surveillance study found process limits of detection ranging from 2,160 to 2,680 copies per liter for Bio-Rad QX200 and 5,650 to 9,700 copies per liter for QIAGEN QIAcuity. Laboratories should validate assays on their specific platform and not assume that performance characteristics transfer directly between platforms.

Safety and Regulatory Context

Laboratory Biosafety

All PCR work requires appropriate biosafety practices. The World Health Organization Laboratory Biosafety Manual provides guidance on safe handling of biological materials. Wastewater surveillance for SARS-CoV-2 requires occupational safety measures in managing sewage collection and processing. Laboratories should follow established biosafety protocols for sample handling, nucleic acid extraction, and amplification procedures.

Quality Management

The World Health Organization Laboratory Quality Management System Handbook provides a framework for establishing and maintaining quality in diagnostic laboratories. This includes documentation, validation, quality control, and continuous improvement processes. Laboratories should integrate their qPCR or dPCR methods into a broader quality management system that addresses all aspects of laboratory operations.

Regulatory Compliance

Diagnostic laboratories must comply with applicable regulations for their jurisdiction. The Bioanalytical Method Validation Guidance from the U.S. Food and Drug Administration outlines expectations for analytical methods used in regulatory submissions. Laboratories should be aware of the regulatory requirements for their specific applications and ensure that their validation documentation meets these requirements.

Professional Escalation Criteria

When to Escalate to Digital PCR

Consider transitioning from qPCR to dPCR when qPCR results are unreliable at low target concentrations. The BCR::ABL1 study demonstrated that dPCR had a higher detection rate than RT-PCR for samples in the 0.001% to 0.01% range. If your application requires detection at these levels and qPCR is failing to detect known positive samples, dPCR may provide the needed sensitivity.

Escalate to dPCR when sample matrices cause consistent qPCR inhibition or interference. The wastewater surveillance and food safety applications demonstrated that dPCR provides more reliable quantification in complex matrices. If you are observing fluorescence damping or other interference effects in qPCR, dPCR may provide more accurate results.

When to Seek External Consultation

Consult with reference laboratories or method developers when validation results do not meet acceptance criteria. The multicenter BCR::ABL1 study demonstrated that interlaboratory harmonization requires careful attention to conversion factors and validation. If your results are not comparable to other laboratories, seek external consultation to identify the source of variability.

Escalate to regulatory authorities or public health agencies when results have public health implications. Wastewater surveillance results may inform public health decisions, and the World Health Organization Laboratory Quality Management System Handbook provides guidance on quality requirements for such applications. If you are uncertain about the interpretation of your results, consult with appropriate authorities.

Frequently Asked Questions

What is the main difference between qPCR and dPCR?

qPCR measures fluorescence accumulation during amplification and compares Ct values against a standard curve to estimate target quantity. dPCR partitions the sample into thousands of individual reactions and counts positive partitions to calculate absolute concentration without a standard curve. This fundamental difference affects sensitivity, precision, and workflow.

When should I choose dPCR over qPCR?

Choose dPCR when you need absolute quantification without a standard curve, when targets are present at very low concentrations, or when sample matrices contain inhibitors that affect qPCR performance. The BCR::ABL1 study demonstrated higher detection rates with dPCR at very low target levels, and wastewater surveillance studies showed reduced sensitivity to inhibition.

Does dPCR always provide better sensitivity than qPCR?

Digital PCR provides better sensitivity at very low target concentrations, as demonstrated in the BCR::ABL1 study where dPCR detected 85.56% of samples in the 0.001% to 0.01% range compared to 68.00% for RT-PCR. However, at higher concentrations, both methods can provide comparable results. The sensitivity advantage of dPCR is most pronounced when targets are present at extremely low levels.

Can I use the same primers and probes for qPCR and dPCR?

Primers and probes designed for qPCR can often be adapted for dPCR, but the assays must be revalidated on the dPCR platform. The NK603 maize event assay was adapted from real-time PCR to dPCR and validated across three platforms. Optimization of primer and probe concentrations may be required for dPCR, and the annealing temperature may need adjustment.

How does sample matrix affect the choice between qPCR and dPCR?

Complex sample matrices such as wastewater, soil, and food contain inhibitors that affect PCR performance. Humic acids in wastewater cause fluorescence damping in qPCR, leading to inaccurate results. Digital PCR shows reduced sensitivity to inhibition because partitioning dilutes inhibitors and the endpoint readout is less affected by fluorescence quenching.

What are the cost differences between qPCR and dPCR?

Digital PCR generally has higher consumables cost per reaction due to the partitioning consumables. The wastewater surveillance study found consumables costs of 4.68 per well for QIAGEN QIAcuity and 6.11 per well for Bio-Rad QX200. qPCR has lower consumables cost but requires calibration standards and may require more replicates to achieve equivalent precision.

How do I validate a dPCR assay for diagnostic use?

Validation should follow established frameworks such as the Assay Guidance Manual and the Bioanalytical Method Validation Guidance. Key parameters include limit of blank, limit of detection, limit of quantification, linearity, precision, and trueness. The NK603 maize assay validation demonstrated that all parameters met acceptance criteria, including a limit of quantification below 35 copies per reaction.

Can dPCR replace qPCR for all applications?

Digital PCR is not a universal replacement for qPCR. qPCR offers higher throughput and lower cost for high-volume screening applications. Relative quantification for gene expression studies is well established with qPCR and may not require the absolute quantification provided by dPCR. The choice depends on the specific application, sample volume, target concentration, and matrix complexity.

Related Diagnostic Guides

• Digital Droplet PCR vs qPCR: A Practical Comparison for Absolute Quantification
• Digital Droplet PCR (ddPCR) for Absolute Quantification of Swine Enteric Coronaviruses in Fecal and Oral Fluid Samples: Analytical Sensitivity and Clinical Utility
• Digital Droplet PCR for Absolute Quantification of Canine Parvovirus in Fecal Samples: A High-Sensitivity Molecular Diagnostic Approach
• Digital Droplet PCR for Absolute Quantification of Animal Viruses
• Digital Droplet PCR for Absolute Quantification of Feline Enteric Coronavirus RNA in Fecal Samples: Diagnostic Utility and Prognostic Implications

References and Further Reading

• Laboratory Quality Management System Handbook. World Health Organization.
• Laboratory Biosafety Manual. World Health Organization.
• Assay Guidance Manual. National Center for Advancing Translational Sciences.
• Bioanalytical Method Validation Guidance. U.S. Food and Drug Administration.
• NCBI Literature Resources. National Center for Biotechnology Information.
• Wastewater surveillance for SARS-CoV-2: Lessons learnt from recent studies to define future applications.. The Science of the total environment, 2021.
• [Comparison of quantitative detection of BCR::ABL1 p210 transcript levels: a multicenter study].. Zhonghua bing li xue za zhi = Chinese journal of pathology, 2024.
• Comparison of immunohistochemistry with PCR for assessment of ER, PR, and Ki-67 and prediction of pathological complete response in breast cancer.. BMC cancer, 2017.
• Digital PCR-Based Uromonitor for Molecular Detection of Bladder Cancer: A Multicenter Validation Study Comparing Quantitative PCR and Urine Cytology.. Clinical genitourinary cancer, 2026.
• Bio-Rad and QIAGEN digital PCR platforms provide equivalent quantification for wastewater-based SARS-CoV-2 surveillance.. Applied and environmental microbiology, 2026.
• Tenfold difference in DNA recovery rate: systematic comparison of whole blood vs. dried blood spot sample collection for malaria molecular surveillance.. Malaria journal, 2022.
• Comparison of Serological and Molecular Assays for Bartonella Species in Dogs with Hemangiosarcoma.. Pathogens (Basel, Switzerland), 2021.
• Analytical performance evaluation and method comparison of the 1copy BCR::ABL1 qPCR assay for monitoring chronic myeloid leukemia.. Practical laboratory medicine, 2026.
• A Droplet Digital PCR Method for Simultaneous Detection and Quantification of &lt,i&gt,S. aureus&lt,/i&gt,, &lt,i&gt,L. monocytogenes&lt,/i&gt,, &lt,i&gt,C. sakazakii&lt,/i&gt,, and &lt,i&gt,M. bovis&lt,/i&gt, in Dairy Products.. 2026.
• Digital PCR for Absolute Quantification of Adenoviruses.. 2026.
• Validation of a Duplex Digital PCR Assay for the Quantification of the NK603 Maize Event Across Three dPCR Platforms.. 2026.
• TimeSight: Kinetics-aware detection for digital nucleic acid amplification via spatiotemporal convolutional learning.. 2026.
• Droplet digital PCR assays for Pvmdr1 and Pvcrt-o gene copy number variation determination in Plasmodium vivax.. 2026.
• Absolute quantification of prokaryotes in the microbiome by 16S rRNA qPCR or ddPCR. Nature Protocols, 2025.
• qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biology, 2007.
• Relative Quantification of Residue-Specific m6A RNA Methylation Using m6A-RT-QPCR.. Methods in molecular biology, 2021.
• Development and validation of a multiplex qPCR assay for detection and relative quantification of HPV16 and HPV18 E6 and E7 oncogenes. Scientific Reports, 2021.
• A novel RT-QPCR-based assay for the relative quantification of residue specific m6A RNA methylation. Scientific Reports, 2019.
• Fluorescence damping as primary interference mechanism of humic acids on qPCR quantification of SARS-CoV-2 in wastewater surveillance.. Journal of Hazardous Materials, 2025.
• qPCR-based relative quantification of the brown algal endophyte Laminarionema elsbetiae in Saccharina latissima: variation and dynamics of host-endophyte interactions. Journal of Applied Phycology, 2017.
• Accuracy of real-time PCR and digital PCR for the monitoring of total HIV DNA under prolonged antiretroviral therapy. Scientific Reports, 2022.
• Efficiency and sensitivity of the digital droplet PCR for the quantification of antibiotic resistance genes in soils and organic residues. Applied Microbiology and Biotechnology, 2016.
• Droplet digital PCR for routine analysis of genetically modified foods (GMO) - A comparison with real-time quantitative PCR. Food Control, 2016.

This article is educational and does not replace validated laboratory procedures, institutional biosafety review, manufacturer instructions, or professional interpretation.