Choosing Between NGS and qPCR

Next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR) are essential techniques for investigating the genome, transcriptome, and epigenome. In many cases, choosing the appropriate technique is straightforward, but in others, it can be more challenging to determine the most effective option. A simple distinction is that smaller, more focused projects are typically better suited for qPCR, while larger, more complex datasets benefit from NGS. However, there are other characteristics that differentiate the two techniques. To provide clearer guidance, we'll review NGS and qPCR and offer basic guidelines to help you choose the best method for your research.

Next-Generation Sequencing
The limitations of traditional sequencing methods, like Sanger sequencing, led to the creation of NGS. Its development has enabled comprehensive analysis of genetic material on an unprecedented scale. Over the years, NGS has seen continuous advancements, with significant expansions in methodologies, instruments, and tool providers. Sequencing platforms have also become smaller, more affordable, and increasingly accurate. Additionally, the introduction of long-read sequencing technologies has dramatically extended read lengths, allowing for more precise analysis of complex genomic regions. Most importantly, the overall cost of sequencing has dropped significantly, further expanding its accessibility and usage.

The main benefit of NGS is its ability to provide sequence information for vast amounts of genetic material in a massively parallel manner. For instance, NGS has become a core component in cancer research since it enables the discovery of rare mutations and variants that drive disease progression. It is also routinely used for whole genome analysis, quantifying transcriptome-wide gene expression, and identifying novel mutations often missed by other methods.

qPCR
Quantitative PCR and real-time PCR refer to the same technique. The key difference from regular PCR is that qPCR amplifies DNA, but the reaction includes fluorescent dyes or probes that are used to measure the accumulation of amplified DNA in real time. Related techniques, such as digital PCR (dPCR), are also widely used for similar applications and offer the advantage of absolute quantification of nucleic acids.

One notable feature of qPCR is its ability to provide relative quantification, making it especially useful when comparing gene expression levels across different samples. qPCR is also commonly used in clinical settings for rapid pathogen detection, such as identifying SARS-CoV-2 during the COVID-19 pandemic. In particular, this approach is fast, cost-effective, and ideal for applications where the target is already known.

Comparing NGS and qPCR
The most effective way to choose between these two techniques is to first consider the application and the number of targets. The number of targets for a qPCR assay depends on the system used, but it’s typically limited to a few (3-5) per reaction. In contrast, NGS can handle nearly limitless regions of interest, unless a targeted assay is employed, in which case it can still process hundreds within a single assay.

The potential downside to qPCR is that it requires prior knowledge of the target sequences for detection. This is also true for targeted NGS experiments; however, many NGS assays can be used to investigate regions without prior knowledge of the target sequences for detection. In addition, NGS has higher sensitivity, can detect rare mutations or low-abundance variants, and doesn’t require prior knowledge of targets, making it more suitable for discovery-based research. Conversely, qPCR is less sensitive for rare variants and is better suited for applications where the sequence is already defined, such as gene expression quantification or pathogen detection.

While NGS is often recommended for larger and more comprehensive projects, there are several instances where qPCR is the better option. This includes studies that involve a smaller number of targets that could be analyzed across a few runs of qPCR. Running these experiments with NGS would often be wasteful or cost-prohibitive. Additionally, applications that require a simple “yes” or “no” such as pathogen or mutation detection are often better suited for qPCR. Many pre-designed assays also facilitate ease of use for qPCR in certain applications.
Another key difference is in the complexity of data analysis. NGS generates large volumes of data and requires advanced bioinformatics tools and expertise for analysis. In contrast, qPCR generates smaller and simpler datasets that make the data analysis phase quicker and easier.

Final Thoughts
Both qPCR and NGS are beneficial for genomics research, but each has its advantages and disadvantages. In short, qPCR generally offers faster turnaround times, providing results within hours, and is more cost-effective for small-scale studies due to lower equipment and reagent costs. Meanwhile, NGS is more suitable for comprehensive and exploratory studies due to its higher resolution and ability to provide sequence-level information, but with the trade-off of higher cost, more complex workflows, and higher data analysis requirements. Advances in these technologies are also expanding their use cases. NGS now has smaller, more affordable instruments with shorter run times, while qPCR has increased in sensitivity, throughput, and cost-effectiveness. Ultimately, the choice between qPCR and NGS depends on the requirements of your research, and as both methods rapidly advance, they offer even more possibilities in genomics.


Overview of Methods:

• qPCR
  • Key Applications
    • Gene expression quantification
    • Rapid pathogen detection
    • Verifying sequencing results
    • Non-coding RNA analysis
    • Variation detection (SNPs, CNVs, etc.)
  • Benefits
    • Rapid results
    • Low cost
    • Lower equipment requirements
  • Disadvantages
    • Limited number of targets
    • Limited to identifying predefined sequences
    • Lower resolution
    • Doesn’t provide sequence information
    • Not useful for exploratory studies
• NGS
  • Key Applications
    • Detailed gene expression analysis
    • Large-scale analysis (WGS, WES, metagenomics, etc.)
    • Isoform diversity
    • Novel and low-level transcript discovery
    • Non-coding RNA analysis
    • Complex genomic analysis (structural variation and rare variants)
  • Benefits
    • More comprehensive/ higher throughput
    • Easier to explore unknown regions
    • Provides sequence information
    • Higher resolution and discovery power
  • Disadvantages
    • Higher costs
    • Not cost-effective/practical when analyzing a few targets
    • Longer, more complex workflows
    • Requires expertise for analysis