Journal Article

MSMAD: a computationally efficient method for the analysis of noisy array CGH data

Eva Budinska, Eva Gelnarova and Michael G. Schimek

in Bioinformatics

Volume 25, issue 6, pages 703-713
Published in print March 2009 | ISSN: 1367-4803
Published online January 2009 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btp022
MSMAD: a computationally efficient method for the analysis of noisy array CGH data

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Motivation: Genome analysis has become one of the most important tools for understanding the complex process of cancerogenesis. With increasing resolution of CGH arrays, the demand for computationally efficient algorithms arises, which are effective in the detection of aberrations even in very noisy data.

Results: We developed a rather simple, non-parametric technique of high computational efficiency for CGH array analysis that adopts a median absolute deviation concept for breakpoint detection, comprising median smoothing for pre-processing. The resulting algorithm has the potential to outperform any single smoothing approach as well as several recently proposed segmentation techniques. We show its performance through the application of simulated and real datasets in comparison to three other methods for array CGH analysis.

Implementation: Our approach is implemented in the R-language and environment for statistical computing (version 2.6.1 for Windows, R-project, 2007). The code is available at: http://www.iba.muni.cz/~budinska/msmad.html

Contact: budinska@iba.muni.cz

Supplementary information:Supplementary data are available at Bioinformatics online.

Journal Article.  9317 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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