MCGH

MCGH analyzes microarray-based comparative genomic hybridization (array CGH) experiments by assessing relative DNA copy-number variations between two genomes using competitive hybridisation data.

Key Features:

• Ratio Normalization Approaches: Provides multiple methodologies for ratio normalization of array CGH intensities.
• Copy Number Estimation: Estimates distributions of three distinct DNA copy-number classes—gains, normal, and losses—using a maximum likelihood method.
• Amplicon Boundary Computation: Computes amplicon boundaries using either the fuzzy K-nearest neighbour method or a wavelet approach.
• Integration with Genomic Databases: Links each genomic clone to corresponding entries in the Ensembl database (http://www.ensembl.org).

Scientific Applications:

• Cancer genomics: Detects chromosomal amplifications and deletions to support studies of oncogenesis and tumor progression.
• Genomic disorder and biomarker analysis: Aids identification of potential genetic markers associated with cancer and other genomic disorders through genome-wide copy-number profiling.

Methodology:

Competitive hybridisation of DNA samples to microarrays followed by computational analyses including ratio normalization, maximum likelihood estimation of three-class (gain/normal/loss) copy-number distributions, and amplicon boundary computation via fuzzy K-nearest neighbour or wavelet methods.