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Finding Regulatory Elements Using Joint Likelihoods for Sequence and Expression Profile Data

Ian Holmes$^{(\ast)}$ - William J. Bruno
[email protected]     [email protected]
Theoretical Biology & Biophysics, T-10, MS K-710, Los Alamos National Laboratory, NM 87545
$(\ast)$ Present address: Berkeley Drosophila Genome Project, LSA Rm 539
UC Berkeley, CA 94720-3200 Tel (510) 643-9944 Fax (510) 643-9947

Keywords: microarrays, clustering, promoters, Gibbs sampling, Gaussian processes

Abstract:

A recent, popular method of finding promoter sequences is to look for conserved motifs upstream of genes clustered on the basis of expression data. This method presupposes that the clustering is correct. Theoretically, one should be better able to find promoter sequences and create more relevant gene clusters by taking a unified approach to these two problems. We present a likelihood function for a ``sequence-expression'' model giving a joint likelihood for a promoter sequence and its corresponding expression levels. An algorithm to estimate sequence-expression model parameters using Gibbs sampling and Expectation/Maximization is described. A program, called kimono, that implements this algorithm has been developed: the source code is freely available on the Internet.



 
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Next: Introduction

2000-04-26