Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression

Background: In the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a complex network that regulate the state of chromatin and processes that depend on it including transcription and DNA replication. Currently, a large number of these epigenetic modifications are being mapped in a variety of cell lines at different stages of development using high throughput sequencing by members of the ENCODE consortium, the NIH Roadmap Epigenomics Program and the Human Epigenome Project. An extremely promising and underexplored area of research is the application of machine learning methods, which are designed to construct predictive network models, to these large-scale epigenomic data sets. Results: Using a ChIP-Seq data set of 20 histone lysine and arginine methylations and histone variant H2A.Z in human CD4+ T-cells, we built predictive models of gene expression as a function of histone modifica...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Xiaojiang Xu, Stephen Hoang, Marty W. Mayo, Stefan Bekiranov
• Keywords: BMCBI 2010, Epigenetic Modifications, Gene Expression, Histones