Finding Regulatory Elements Using Joint Likelihoods for Sequence and ExpressionProfile Data

摘要

A recent, popular method of finding promoter sequences is to look for conservedmotifs 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.