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The National Technical Information Service acquires, indexes, abstracts, and archives the largest collection of U.S. government-sponsored technical reports in existence. The NTRL offers online, free and open access to these authenticated government technical reports. Technical reports and documents in its repository may be available online for free either from the issuing federal agency, the U.S. Government Publishing Office’s Federal Digital System website, or through search engines.

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

DE2001752619

Publication Date	2000
Personal Author	Bruno, W. J.; Holmes, I.
Page Count	12
Abstract	A recent, popular method of finding promoter sequences is to look for conserved motifs up-stream 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 and the source code is freely available over the internet.
Keywords	Genes Algorithms Internet Sampling Expression data Clustering Joint likelihoods Promotor sequences
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	57F - Cytology, Genetics, & Molecular Biology
Corporate Authors	Los Alamos National Lab., NM.; Department of Energy, Washington, DC.
Document Type	Conference Proceedings
NTIS Issue Number	200123
Contract Number	W-7405-ENG-36

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

Finding Regulatory Elements Using Joint Likelihoods for Sequence and Expression Profile Data.
DE2001752619

Publication Date	2000
Personal Author	Bruno, W. J.; Holmes, I.
Page Count	12
Abstract	A recent, popular method of finding promoter sequences is to look for conserved motifs up-stream 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 and the source code is freely available over the internet.
Keywords	Genes Algorithms Internet Sampling Expression data Clustering Joint likelihoods Promotor sequences
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	57F - Cytology, Genetics, & Molecular Biology
Corporate Authors	Los Alamos National Lab., NM.; Department of Energy, Washington, DC.
Document Type	Conference Proceedings
NTIS Issue Number	200123
Contract Number	W-7405-ENG-36