ClinCNV

ClinCNV detects somatic and germline copy-number alterations (CNAs) in paired normal–tumor and multi-sample next-generation sequencing (NGS) datasets by integrating read-depth and B-allele frequency (BAF) metrics for whole-exome sequencing (WES) and targeted panel sequencing (TPS) data.

Key Features:

• High-Resolution Detection: Uses a statistical approach combining read-depth and B-allele frequency (BAF) metrics to identify copy-number changes in paired normal-tumor NGS datasets.
• Multi-Sample Capability: Supports simultaneous analysis of germline, trio, and somatic contexts across multiple samples.
• Compatibility with Sequencing Data: Processes whole-exome sequencing (WES) and targeted panel sequencing (TPS) datasets, addressing challenges of detecting larger structural variants with these technologies.
• Performance Validation: Methodology has been validated on large cohorts of WES and TPS sequenced samples.

Scientific Applications:

• Cancer Genomics: Detects CNAs in tumor samples to support identification of biomarkers for targeted therapies and prognostic factors in research and clinical studies.

Methodology:

Applies a statistical method that integrates read-depth and B-allele frequency (BAF) analysis to discern copy-number changes between normal and tumor samples in paired and multi-sample NGS datasets.