cghRA

cghRA performs comprehensive analysis of array Comparative Genomic Hybridization (aCGH) data to detect and prioritize copy-number alterations and polymorphisms.

Key Features:

• Algorithm integration: Integrates multiple algorithms into a cohesive framework for aCGH data analysis.
• Copy-number calling: Implements newly developed algorithms for copy-number calling.
• Polymorphism detection: Implements newly developed algorithms for polymorphism detection.
• Minimal common region prioritization: Implements newly developed algorithms for prioritizing minimal common regions.
• R package implementation: Distributed as an R package implementation for computational analysis workflows.

Scientific Applications:

• Diffuse Large B-Cell Lymphoma analysis: Applied to an original series of 107 Diffuse Large B-Cell Lymphomas to identify and prioritize recurrent copy-number alterations.
• Cancer genomics: Enables comprehensive copy-number and polymorphism analyses in oncology datasets derived from aCGH.

Methodology:

Integrates multiple algorithms and implements newly developed methods for copy-number calling, polymorphism detection, and minimal common region prioritization validated on aCGH data.