A Random-Forest Based Algorithm for Prediction of Enhancers From Histone Modifications

ABSTRACT: Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts,

ORGANISM(S): Homo sapiens

SUBMITTER: Nisha Rajagopal 

PROVIDER: E-GEOD-37858 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress