iCNV

iCNV detects copy number variations (CNVs) by integrating whole exome sequencing (WES), whole genome sequencing (WGS), and SNP-array data to improve CNV resolution and accuracy for genetic association studies.

Key Features:

• Cross-Platform Integration: Integrates WES, WGS, and SNP-array data to enhance CNV detection resolution and accuracy beyond single-platform or simple intersection/union approaches.
• Statistical Framework: Applies platform-specific normalization procedures to ensure data compatibility and reliability across different experimental setups.
• Allele-Specific Read Utilization: Leverages allele-specific reads from sequencing data to refine CNV detection and improve precision.
• Hidden Markov Model (HMM): Integrates matched next-generation sequencing (NGS) and SNP-array data using a Hidden Markov Model to model genomic states and transitions.
• Versatile Study Designs: Supports analysis of WES-only, WGS-only, SNP-array-only, or any combination of these platforms.

Scientific Applications:

• Genetic Association Studies: Enables high-resolution CNV detection across cohorts to support association analyses of disease-related structural variation.
• Genomics and Personalized Medicine: Provides refined CNV calls from integrated datasets to aid genomic research and applications in personalized medicine.

Methodology:

Platform-specific normalization of WES, WGS, and SNP-array data; integration of matched next-generation sequencing (NGS) and SNP-array data using a Hidden Markov Model (HMM); and incorporation of allele-specific reads to refine CNV calls.