Enhancing clinical genomic accuracy with panelGC: a novel metric and tool for quantifying and monitoring GC biases in hybridization capture panel sequencing

Abstract Accurate assessment of fragment abundance within a genome is crucial in clinical genomics applications such as the analysis of copy number variation (CNV). However, this task is often hindered by biased coverage in regions with varying guanine–cytosine (GC) content. These biases are particularly exacerbated in hybridization capture sequencing due to GC effects on probe hybridization and polymerase chain reaction (PCR) amplification efficiency. Such GC content–associated variations can exert a negative impact on the fidelity of CNV calling within hybridization capture panels. In this report, we present panelGC, a novel metric, to quantify and monitor GC biases in hybridization capture sequencing data. We establish the efficacy of panelGC, demonstrating its proficiency in identifying and flagging potential procedural anomalies, even in situations where instrument and experimental monitoring data may not be readily accessible. Validation using real-world datasets demonstrates that panelGC enhances the quality control and reliability of hybridization capture panel sequencing.