CNVpytor

CNVpytor detects and analyzes copy number variations (CNVs) and copy number alterations (CNAs) from whole-genome sequencing (WGS) data using read depth and B-allele frequency information.

Key Features:

• Core Engine: Builds upon the CNVnator core engine for CNV/CNA detection.
• Read Depth (RD) Analysis: Performs CNV/CNA detection using read depth (RD) from whole-genome sequencing data.
• B-allele Frequency (BAF) Integration: Integrates B-allele frequency (BAF) likelihood information derived from single nucleotide polymorphism and small indel data.
• Performance Enhancements: Parses alignment files 2–20× faster than its predecessor and produces intermediate files 20–50× smaller.
• Modular Architecture: Provides a modular architecture enabling integration into analysis workflows.
• Visualization and Export: Provides advanced visualization capabilities and supports export of data to JBrowse.
• Filtering and Annotation: Supports filtering of CNV calls by multiple criteria and annotation of CNV calls.

Scientific Applications:

• Personalized Genomics and Treatment Planning: Supports CNV/CNA detection relevant to personalized genomics and treatment planning.
• Copy-Number-Neutral LOH Detection: Enables identification of copy-number-neutral loss of heterozygosity through combined RD and BAF analysis.
• Comprehensive WGS CNV/CNA Analysis: Facilitates comprehensive CNV/CNA profiling from whole-genome sequencing data.

Methodology:

Combines read depth (RD) and B-allele frequency (BAF) likelihoods derived from SNP and small indel data in whole-genome sequencing, extends the CNVnator core engine, parses alignment files, and generates intermediate files.