A versatile statistical analysis algorithm to detect genome copy number variation

Daruwala, R. S., Rudra, A., Ostrer, H., Lucito, R., Wigler, M. H., Mishra, B. (November 2004) A versatile statistical analysis algorithm to detect genome copy number variation. Proceedings of the National Academy of Sciences of the United States of America, 101 (46). pp. 16292-16297. ISSN 0027-8424

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URL: https://www.ncbi.nlm.nih.gov/pubmed/15534219
DOI: 10.1073/pnas.0407247101


We have developed a versatile statistical analysis algorithm for the detection of genomic aberrations in human cancer cell lines. The algorithm analyzes genomic data obtained from a variety of array technologies, such as oligonucleotide array, bacterial artificial chromosome array, or array-based comparative genomic hybridization, that operate by hybridizing with genomic material obtained from cancer and normal cells and allow detection of regions of the genome with altered copy number. The number of probes (i.e., resolution), the amount of uncharacterized noise per probe, and the severity of chromosomal aberrations per chromosomal region may vary with the underlying technology, biological sample, and sample preparation. Constrained by these uncertainties, our algorithm aims at robustness by using a priorless maximum a posteriori estimator and at efficiency by a dynamic programming implementation. We illustrate these characteristics of our algorithm by applying it to data obtained from representational oligonucleotide microarray analysis and array-based comparative genomic hybridization technology as well as to synthetic data obtained from an artificial model whose properties can be varied computationally. The algorithm can combine data from multiple sources and thus facilitate the discovery of genes and markers important in cancer, as well as the discovery of loci important in inherited genetic disease.

Item Type: Paper
Uncontrolled Keywords: array-based comparative genomic hybridization copy-number fluctuations maximum a posteriori estimator MINIMAX ESTIMATION minimax estimation ESTIMATORS estimators MODELS models CGH REPRESENTATIONS representations DISTRIBUTIONS distributions HYBRIDIZATION hybridization
Subjects: bioinformatics > computational biology
CSHL Authors:
Communities: CSHL labs > Lucito lab
CSHL labs > Wigler lab
Depositing User: CSHL Librarian
Date: November 2004
Date Deposited: 09 Feb 2012 16:31
Last Modified: 11 Jan 2018 20:46
PMCID: PMC528962
Related URLs:
URI: https://repository.cshl.edu/id/eprint/22354

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