High-quality copy number variant (CNV) analysis begins with a well-designed assay and optimized sequencing workflow. Foundational quality measures deployed in VarSeq include building an appropriate reference set for coverage normalization, ensuring sufficient sequencing depth, and maintaining consistency in both the sequencing platform and library preparation methods. These steps help ensure robust data and reliable CNV detection. However, additional post-project refinement can improve accuracy and sensitivity even when these parameters are carefully optimized.
Refining Target Regions for Accurate CNV Detection
One effective strategy is cleaning problematic targets within your target regions before the CNV detection step. A key approach to this refinement is identifying and excluding low-quality regions from the normalization process.
Specific targets may consistently underperform due to characteristics such as:
- GC content outside of optimal ranges, leading to biased coverage.
- Inadequate read span, which limits statistical confidence.
- Low average coverage, which reduces the ability to detect true copy number changes.
By removing targets that fail to meet desired thresholds for one or more of these parameters, you can prevent noisy or unreliable regions from skewing normalization metrics. This, in turn, helps reduce false positives and false negatives, resulting in more confident CNV calls.
While often overlooked, this clean-up step can be a best practice for any lab conducting CNV analysis, especially when aiming for high assay reproducibility and clinical or research-grade results. Whether working with an established panel or developing a new assay, implementing a post-project target evaluation process can improve data quality and interpretation.
If you would like to explore our strategies for implementing this type of quality filtering in your VarSeq CNV workflow, please reach out to [email protected].
