panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics

Area of Science:

• Genomics
• Bioinformatics
• Clinical Diagnostics

Background:

• Targeted next-generation sequencing (NGS) panels are standard in clinical diagnostics, detecting various genetic alterations.
• Existing computational methods for copy-number variation (CNV) detection in NGS data have limitations in accuracy, quality control, and user-friendliness.
• Incidental findings from comprehensive analyses can complicate clinical interpretation.

Purpose of the Study:

• To develop and evaluate panelcn.MOPS, a novel computational pipeline for accurate and user-friendly CNV detection in targeted NGS panel data.
• To address limitations of existing methods, including accuracy, quality control, and the management of incidental findings.
• To provide a robust tool suitable for routine clinical genetic diagnostics.

Main Methods:

• Development of the panelcn.MOPS pipeline for CNV detection from targeted NGS data.
• Comparative analysis of panelcn.MOPS against five state-of-the-art methods using data from 180 samples.
• Implementation of quality control (QC) criteria for both samples and individual regions of interest (ROIs).

Main Results:

• panelcn.MOPS demonstrated high accuracy in detecting CNVs, comparable to leading methods.
• The pipeline reliably identified CNVs of various sizes, from partial regions to entire genes.
• panelcn.MOPS effectively avoids incidental findings by allowing users to select specific genes for analysis.
• Novel QC metrics for samples and ROIs enhance confidence in CNV calls.

Conclusions:

• panelcn.MOPS is a highly accurate and sensitive tool for CNV detection in targeted NGS data.
• Its ability to avoid incidental findings and provide robust QC makes it suitable for clinical use.
• The pipeline's user-friendly interface, available as an R package and standalone software, facilitates adoption by clinical geneticists.