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Bioinformatics Advance Access published online on September 15, 2005
Bioinformatics, 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@oxfordjournals.org
Received May 17, 2005
Revised September 3, 2005
Accepted September 13, 2005

Article

RSIR: regularized sliced inverse regression for motif discovery

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

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

* To whom correspondence should be addressed.
Jun S. Liu, E-mail: jliu{at}stat.harvard.edu


  Abstract

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.

1 These authors contributed equally to this work and should be regarded as joint First Authors
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