Prediction of regulatory elements in mammalian genomes using chromatin signatures.

ABSTRACT:

Background

Recent genomic scale survey of epigenetic states in the mammalian genomes has shown that promoters and enhancers are correlated with distinct chromatin signatures, providing a pragmatic way for systematic mapping of these regulatory elements in the genome. With rapid accumulation of chromatin modification profiles in the genome of various organisms and cell types, this chromatin based approach promises to uncover many new regulatory elements, but computational methods to effectively extract information from these datasets are still limited.

Results

We present here a supervised learning method to predict promoters and enhancers based on their unique chromatin modification signatures. We trained Hidden Markov models (HMMs) on the histone modification data for known

SUBMITTER: Won KJ 

PROVIDER: S-EPMC2657164 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature