Skip Navigation


Bioinformatics Advance Access originally published online on January 15, 2009
Bioinformatics 2009 25(6):703-713; doi:10.1093/bioinformatics/btp022
This Article
Right arrow Full Text
Right arrow Full Text (Print PDF)
Right arrow Supplementary Data
Right arrow All Versions of this Article:
25/6/703    most recent
btp022v2
btp022v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (1)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Budinska, E.
Right arrow Articles by Schimek, M. G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Budinska, E.
Right arrow Articles by Schimek, M. G.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

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

Eva Budinska 1,*, Eva Gelnarova 1 and Michael G. Schimek 1,2

1Institute of Biostatistics and Analyses, Masaryk University, Kamenice 126/3, 625 00 Brno, Czech Republic and 2Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2, 8036 Graz, Austria

*To whom correspondence should be addressed.


  Abstract

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{at}iba.muni.cz

Supplementary information:Supplementary data are available at Bioinformatics online.

Associate Editor: Alex Bateman

Received on June 4, 2008; revised on November 5, 2008; accepted on January 8, 2009

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.