Finding regulatory elements using joint likelihoods for sequence and expression profile data
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Updated: Oct 13, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
Published on: August 9, 2019
1Theoretical Biology & Biophysics, Los Alamos National Laboratory, NM 87545, USA. ihh@fruitfly.org
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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