MOTIVATION: Accurate detection of copy number variations (CNVs) from targeted panel sequencing remains challenging due to limited genomic coverage and pronounced sample-specific biases. Existing normalization strategies, including baseline-cohort, matched-control, and single-sample approaches, often struggle to balance noise suppression with adaptability, leading to inconsistent performance across heterogeneous samples. RESULTS: We present PScnv, a personalized self-normalizing framework for robust CNV detection from panel sequencing data. PScnv integrates a pre-built panel-of-normals (PoN) with sample-intrinsic stable chromosomes through ridge-regression normalization to generate individualized log2 ratio profiles with reduced systematic variation. CNVs are then identified using a hierarchical multi-phase segmentation pipeline incorporating z-score pre-partitioning, kernel-based correction, and circular binary segmentation. In 139 clinical tumor samples with orthogonal FISH validation at MET, ERBB2, and MTAP, PScnv showed improved accuracy and robustness over existing methods that do not require patient-matched normal samples, provided that a pre-built PoN cohort is available. AVAILABILITY: Source code is available for academic use at https://github.com/lvws/PScnv.

Copy Number Variation (CNV) is a hallmark of tumor genomes. The performance evaluation and optimization of CNV detection algorithms heavily rely on benchmark datasets with known variations and tumor purity. However, existing simulation tools often generate synthetic sequencing reads based on idealized statistical models, which exhibit significant discrepancies from real sequencing data in terms of error profiles, GC bias, and library complexity, thereby compromising the accuracy of algorithm evaluation. To address this gap, we have designed and developed CNV-Sculptor, a high-fidelity CNV simulation software. CNV -Sculptor innovatively employs a “remove-and-refill” strategy, utilizing real sequencing data from normal samples (BAM files) as building blocks. Through six core steps - base BAM construction, regional read pool extraction, read realignment and deduplication, target read quantification, pure tumor sample synthesis, and optional tumor purity simulation-it precisely introduces variations of specified copy numbers at any genomic location and can simulate varying proportions of tumor cell content. This software not only ensures the high authenticity of the simulated data but also provides users with great flexibility and customizability. CNV-Sculptor offers a powerful, reliable, and user-friendly tool for the validation and comparison of CNV detection algorithms, as well as for bioinformatics education.