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Bioinformatics Advance Access originally published online on September 15, 2005
Bioinformatics 2005 21(22):4169-4175; doi:10.1093/bioinformatics/bti680
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

RSIR: regularized sliced inverse regression for motif discovery

Wenxuan Zhong 1,{dagger}, Peng Zeng 2,{dagger}, Ping Ma 3,{dagger}, Jun S. Liu 1,* and Yu Zhu 4,*

1Department of Statistics, Harvard University Cambridge, MA 02138, USA
2Department of Mathematics and Statistics, Auburn University Auburn, AL 36849, USA
3Department of Statistics, University of Illinois at Urbana-Champaign Champaign, IL 61820, USA
4Department of Statistics, Purdue University West Lafayette, IN 47907, USA

*To whom correspondence should be addressed.

Motivation: Identification of transcription factor binding motifs (TFBMs) is a crucial first step towards the understanding of regulatory circuitries controlling the expression of genes. In this paper, we propose a novel procedure called regularized sliced inverse regression (RSIR) for identifying TFBMs. RSIR follows a recent trend to combine information contained in both gene expression measurements and genes' promoter sequences. Compared with existing methods, RSIR is efficient in computation, very stable for data with high dimensionality and high collinearity, and improves motif detection sensitivities and specificities by avoiding inappropriate model specification.

Results: We compare RSIR with SIR and stepwise regression based on simulated data and find that RSIR has a lower false positive rate. We also demonstrate an excellent performance of RSIR by applying it to the yeast amino acid starvation data and cell cycle data.

Availability: Matlab programs are available upon request from the authors.

Contact: jliu{at}stat.harvard.edu; yuzhu{at}stat.purdue.edu

Received on May 17, 2005; revised on September 3, 2005; accepted on September 13, 2005

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