snapCGH

snapCGH performs segmentation, normalization, processing, and visualization of array Comparative Genomic Hybridization (aCGH) data to detect copy number variations and support genomic analyses.

Key Features:

• Segmentation: Identifies regions of genomic gain or loss in aCGH data to enable detection of copy number changes.
• Normalization: Applies normalization techniques to adjust raw aCGH signals for technical variability.
• Processing Capabilities: Provides functions to process and manage aCGH datasets for downstream analysis.
• Visualization: Plots raw and segmented aCGH data for individual arrays and multiple arrays simultaneously.

Scientific Applications:

• Copy Number Variation (CNV) detection: Enables identification of CNVs from aCGH experiments.
• Disease and cancer genomics: Supports analysis of genetic alterations associated with diseases such as cancer.
• Basic and clinical genomic research: Facilitates processing and interpretation of high-throughput aCGH datasets for research and clinical investigations.

Methodology:

snapCGH runs in R and integrates within the Bioconductor framework, leveraging R's statistical capabilities and interoperating with other Bioconductor packages.