Patterns of Chromatin-Modifications Discriminate Different Genomic Features in Arabidopsis

作     者：Anuj Srivastava Xiaoyu Zhang Sal LaMarca Liming Cai Russell L. Malmberg 

作者机构：Institute of Bioinformatics University of Georgia The Jackson Laboratory Department of Plant Biology University of Georgia Department of Computer Science University of Georgia 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2013年第18卷第5期

页      面：431-440页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 071007[理学-遗传学] 0901[农学-作物学] 090102[农学-作物遗传育种] 

基　　金：supported by the National Science Foundation of USA(No.IIS 0916250) The University of Georgia Franklin College of Arts&Sciences research fund 

主　　题：chromatin modification DNA methylation support vector machine machine learning Arabidopsis 

摘      要：Dynamic regulation and packaging of genetic information is achieved by the organization of DNA into chromatin. Nucleosomal core histones, which form the basic repeating unit of chromatin, are subject to various post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitinylation. These modifications have effects on chromatin structure and, along with DNA methylation, regulate gene *** goal of this study was to determine if patterns in modifications were related to different categories of genomic features, and, if so, if the patterns had predictive value. In this study, we used publically available data（ChIP-chip）for different types of histone modifications（methylation and acetylation） and for DNA methylation for Arabidopsis thaliana and then applied a machine learning based approach（a support vector machine） to demonstrate that patterns of these modifications are very different among different kinds of genomic feature categories（protein, RNA,pseudogene, and transposon elements）. These patterns can be used to distinguish the types of genomic *** methylation and H3K4me3 methylation emerged as features with most discriminative power. From our analysis on Arabidopsis, we were able to predict 33 novel genomic features, whose existence was also supported by analysis of RNA-seq experiments. In summary, we present a novel approach which can be used to discriminate/detect different categories of genomic features based upon their patterns of chromatin modification and DNA methylation.