CNV Detection Using Amplicon Sequencing: A Scalable Approach for Targeted NGS Assays

CleanPlex CNV detection relies on normalized read depth comparisons across samples. The workflow begins with raw read count extraction for each amplicon, followed by intra-sample normalization to correct for differences in total sequencing depth.

Then, read counts are compared across samples to correct for amplicon-specific amplification bias.

Finally, normalized ratios are evaluated against an expected diploid baseline. A ratio of ~1.0 indicates normal copy number, ~0.5 indicates a heterozygous deletion, and ~1.5–2.0 indicates a duplication. Contiguous amplicon sequencing analysis and threshold-based classification are used to assign final copy number states, making it possible for laboratories to analyze genetic variation at the exon level within a single streamlined workflow.

Accurate CNV calling requires correcting for both sample-level and amplicon-level technical variability. CleanPlex workflows apply a two-stage normalization approach: first, read counts are normalized within each sample to account for differences in total sequencing depth; second, values are compared across samples to correct for amplicon-specific amplification bias.

In order to reduce technical noise during data analysis, amplicons can be grouped by properties such as length, GC content, and PCR pool. This minimizes category-specific biases that could otherwise be misinterpreted as true copy number changes rather than genomic variants.

CNV analysis is inherently quantitative (it depends on detecting meaningful differences in read depth across specific genomic regions). When amplification is uneven, technical variability can obscure true copy number signals or generate false positives.

Highly uniform coverage lowers background noise, strengthens the signal-to-noise ratio, and makes it possible to confidently identify mutations and structural changes at the single-exon level.

CleanPlex chemistry incorporates proprietary background-cleaning enzymatic steps that reduce non-specific amplification products and improve sequencing uniformity across highly multiplexed panels — directly supporting more reliable copy number interpretation.

Normalized read depth is the primary basis for CNV calling; CleanPlex workflows can provide orthogonal evidence to improve confidence in CNV calls. In targeted next generation sequencing NGS workflows, additional signals such as loss of heterozygosity (LOH), soft-clipped reads, gapped alignments, and super-amplicon evidence can be leveraged alongside depth metrics. These indicators help distinguish true structural variants from technical artifacts, particularly in complex or ambiguous regions where read depth alone may not be sufficient to resolve genetic variation with confidence.

Yes. The CleanPlex BRCA1 & BRCA2 Kit was specifically designed to support next-generation sequencing of BRCA1 and BRCA2 with highly consistent exon-level coverage. In a technical study using well-characterized Coriell reference samples with known CNVs, the workflow successfully identified a heterozygous BRCA1 exon 10 deletion, a BRCA1 exon 12 duplication, and BRCA1 exon 14–15 deletions — all confirmed with supporting orthogonal evidence.

Unlike whole exome sequencing, which covers the full coding portion of the human genome, CleanPlex focuses sequencing depth where it matters most. This makes it a strong option for hereditary cancer panels where BRCA1/2 exon level CNV detection is required alongside somatic mutations, SNV, and indel analysis.

There are many advantages of amplicon sequencing (high coverage uniformity, low DNA input requirements, and fast turnaround), which make it well-suited for any targeted workflow where copy number analysis needs to be integrated alongside SNV and indel detection.

Key applications include hereditary cancer testing, oncology biomarker profiling, rare disease testing, pharmacogenomics, and translational research. These workflows can support analysis of genomic DNA, DNA sequences, gene fusions, and microbial communities across a wide range of sample types. Because CleanPlex supports FFPE and low-input DNA samples, it is compatible with the specimens most commonly encountered in clinical and research settings.

Traditional CNV detection methods like MLPA, qPCR, and array technologies require separate workflows from sequencing-based variant detection and are often limited in their ability to simultaneously detect single nucleotide polymorphisms, indels, and structural variants.

Unlike these amplicon sequencing methods, which are actually optimized for specific variant classes, CleanPlex consolidates CNV detection into the same workflow used for comprehensive variant profiling. Compared to whole genome sequencing, targeted amplicon sequencing provides focused, cost-effective sequencing utilization with the high read depth needed for sensitive exon-level CNV analysis; without the informatic complexity or cost of genome-wide approaches.

Several factors are very important for reliable CNV detection in NGS amplicon sequencing. The PCR amplification chemistry must be carefully optimized. Poorly balanced multiplex PCR panels introduce variability that reduces sensitivity and increases false positive rates. DNA fragments should be consistently amplified across target regions with sufficient redundancy to support confident copy number interpretation. Primer design must account for potential interactions, GC bias, and overlapping genomic regions. Reproducibility across runs and operators is equally important. Lastly, robust bioinformatics normalization pipelines are needed to separate true biological signal from technical noise.

CleanPlex chemistry and Paragon Designer™ algorithms were developed as an integrated system using polymerase chain reaction-aware design principles, specifically to address these requirements across large, highly multiplexed panels.