cghMCR

cghMCR analyzes copy number abnormalities (CNAs) in cancer genomes to identify recurrent CNA regions and genes likely driving these alterations.

Key Features:

• Visualization of copy number datasets: Provides visualization of segmented copy number data across multiple samples to identify regions of genomic gain or loss.
• Statistical analysis of recurrent CNAs: Incorporates statistical methods to pinpoint recurrent CNA regions and assess associations with differential survival outcomes.
• Gene identification using cghMCR: Applies the cghMCR method to identify genes most likely responsible for driving CNA formation.
• Detection of recurrently broken genes: Detects recurrently broken genes that may be disrupted or fused, informing structural variation analysis.
• Input data support: Operates on segmented copy number data from multiple samples.

Scientific Applications:

• Cancer genomics research: Analysis of CNA landscapes to elucidate molecular mechanisms driving tumorigenesis.
• Prognostic marker identification: Identification of recurrent CNA regions and genes associated with differential survival outcomes.
• Structural variation studies: Detection of recurrently broken or fused genes to investigate structural alterations contributing to oncogenesis.

Methodology:

Analyzes segmented copy number data from multiple samples, integrates visualization and statistical analyses, and applies the cghMCR method to identify genes likely driving CNAs.