CGHcall

CGHcall detects copy-number aberrations in array comparative genomic hybridization (CGH) data using a six-state mixture model and integrating segmentation-derived breakpoint and chromosome arm information to improve aberration calling.

Key Features:

• Six-State Mixture Model: Employs a six-state mixture model to classify probe log2 ratios into detailed copy-number states.
• Breakpoint Information Utilization: Incorporates segmentation-derived breakpoint information to refine state assignments at segment boundaries.
• Inclusion of Biological Concepts: Accounts for biological distinctions such as single copy gains and amplifications to improve detection of complex genomic changes.
• Chromosome Arm Information: Uses chromosome arm information to contextualize aberrations across chromosomal segments.
• Validation: Validated on simulated data and verified real array CGH datasets to assess performance and robustness.
• Visualization Tools: Provides visualization of profile analysis to aid interpretation of copy-number calls.

Scientific Applications:

• Genomic Research: Precise detection of copy number variations (CNVs) in genomic studies.
• Cancer Genomics: Detection of single copy gains and amplifications to identify oncogenic alterations in cancer samples.
• Developmental Biology: Investigation of chromosomal abnormalities that may impact development.

Methodology:

Combines segmentation-derived breakpoint information with a six-state mixture model for aberration calling.