Markov chain-based promoter structure modeling for tissue-specific expression pattern prediction.

ABSTRACT: Transcriptional regulation is the first level of regulation of gene expression and is therefore a major topic in computational biology. Genes with similar expression patterns can be assumed to be co-regulated at the transcriptional level by promoter sequences with a similar structure. Current approaches for modeling shared regulatory features tend to focus mainly on clustering of cis-regulatory sites. Here we introduce a Markov chain-based promoter structure model that uses both shared motifs and shared features from an input set of promoter sequences to predict candidate genes with similar expression. The model uses positional preference, order, and orientation of motifs. The trained model is used to score a genomic set of promoter sequences: high-scoring promoters are assumed to have a s

SUBMITTER: Vandenbon A 

PROVIDER: S-EPMC2650632 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature