Bayesian Hidden Markov Modeling of Array CGH Data.

ABSTRACT: Genomic alterations have been linked to the development and progression of cancer. The technique of comparative genomic hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather

SUBMITTER: Guha S 

PROVIDER: S-EPMC3286622 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature