VegaMC

VegaMC detects significant recurrent copy number alterations and loss of heterozygosity (LOH) in large cancer genomic datasets to identify driver genetic alterations.

Key Features:

• Recurrent copy number alteration detection: Identifies significant recurrent copy number alterations across large datasets.
• Loss of heterozygosity (LOH) detection: Detects LOH events relevant to tumor genetics and potential driver mutations.
• Integration with allele-intensity formats: Accepts outputs represented as log R ratio and B allele frequency derived from allele signal intensities.
• PennCNV compatibility: Integrates with outputs from PennCNV for use with existing CNV calling workflows.
• Joint segmentation framework: Implements joint segmentation of datasets to help distinguish driver versus passenger alterations.
• Validation on real and synthetic data: Validated using synthetic data and TCGA datasets including colon adenocarcinoma and glioblastoma multiforme.
• R/Bioconductor implementation: Provided as an R/Bioconductor package for computational analysis within the R environment.

Scientific Applications:

• Driver alteration identification in cancer genomics: Identification of candidate driver genetic alterations in tumor cohorts.
• Multi-sample recurrent event analysis: Analysis of multiple samples simultaneously to detect recurrent copy number and LOH patterns across cancer types.
• TCGA dataset analysis: Applied to TCGA datasets such as colon adenocarcinoma and glioblastoma multiforme for validated discovery of aberrant genes.

Methodology:

VegaMC performs joint segmentation of datasets and analyzes allele signal intensities represented as log R ratio and B allele frequency, and accepts outputs from PennCNV.