TrioCNV2

TrioCNV2 detects and refines copy number variations (CNVs) from whole-genome sequencing (WGS) data of parent-offspring trios to improve CNV breakpoint precision and leverage Mendelian inheritance for more accurate detection.

Key Features:

• Integrated Approach: Uses read depth and discordant read pairs to identify approximate CNV locations from WGS data.
• Refinement Techniques: Employs split reads and local de novo assembly to refine CNV breakpoints.
• Mendelian Inheritance Utilization: Incorporates parent-offspring trio data and Mendelian inheritance patterns to improve detection accuracy.
• Performance Validation: Validated on real WGS data from two parent-offspring trios and reported superior accuracy compared to other CNV detection approaches.
• Software Implementation: Implemented in Java and R for processing large genomic datasets.

Scientific Applications:

• Rare and complex disease variant discovery: Identification of causal CNVs from trio WGS data in studies of rare and complex diseases.
• Breakpoint resolution and mapping: Precise refinement of CNV breakpoints to support detailed genomic studies and variant mapping.

Methodology:

Initial CNV detection using read depth and discordant read pairs, breakpoint refinement with split reads and local de novo assembly, incorporation of Mendelian inheritance from parent-offspring trios, and implementation in Java and R; validated on two real WGS trios.