Prediction-based approaches to characterize bidirectional promoters in the mammalian genome.

ABSTRACT:

Background

Machine learning approaches are emerging as a way to discriminate various classes of functional elements. Previous attempts to create Regulatory Potential (RP) scores to discriminate functional DNA from nonfunctional DNA included using Markov models trained to identify sequences from promoters and enhancers from ancestral repeats. We proposed that knowledge gleaned from those methods could be further refined using a multiple class predictor to separate classes of promoter elements from enhancers or nonfunctional DNA.

Results

We extended our previous work, which identified over 5,000 candidate bidirectional promoters in the human genome, to map the orthologous promoter regions in the mouse genome. Our algorithm measured the robustness of evidence provided by the sp

SUBMITTER: Yang MQ 

PROVIDER: S-EPMC2386062 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature