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Bioinformatics Advance Access published online on October 31, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn570
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Model-based analysis of non-specific binding for background correction of high-density oligonucleotide microarrays.

Chikara Furusawa 1,2,{dagger}, Naoaki Ono 1,{dagger}, Shingo Suzuki 1, Tomoharu Agata 1, Hiroshi Shimizu 1 and Tetsuya Yomo 1,2,3

1Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
2Complex Systems Biology Project, ERATO, Japan, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
3Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan

To whom correspondence should be addressed. Prof. Chikara Furusawa, E-mail: furusawa{at}ist.osaka-u.ac.jp


  Abstract

Motivation: High density DNA microarrays provide us with useful tools for analyzing DNA and RNA comprehensively. However, the background signal caused by the non-specific binding between probe and target makes it difficult to obtain accurate measurements. To remove the background signal, there is a set of background probes on Affymetrix Exon arrays to represent the amount of non-specific signals, and an accurate estimation of non-specific signals using these background probes is desirable for improvement of microarray analyses.

Results: We developed a thermodynamic model of non-specific binding on short nucleotide microarrays in which the non-specific bindings are modeled by duplex formation of probes and multiple hypothetical targets. We fitted the observed signal intensities of the background probes with those expected by the model to obtain the model parameters. As a result, we found that the presented model can improve the accuracy of prediction of non-specific signals in comparison with previously proposed methods. This result will provide a useful method to correct for the background signal in oligonucleotide microarray analysis.

Contact: furusawa{at}ist.osaka-u.ac.jp

Availability: The software is implemented in the R language andcan be downloaded from our website (http://www-shimizu.ist.osakau.ac.jp/shimizu_lab/MSNS/).

Associate Editor: Dr. Trey Ideker

{dagger}These authors contributed equally to this work.

Received on June 17, 2008; revised on September 16, 2008; accepted on October 30, 2008

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