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EMdeCODE:一种新型算法,能够读取表观遗传密码的单词,预测增强子和逆转录病毒整合位点,并确定 H3R2me1 作为编码与非编码基因的区别标志。

EMdeCODE: a novel algorithm capable of reading words of epigenetic code to predict enhancers and retroviral integration sites and to identify H3R2me1 as a distinctive mark of coding versus non-coding genes.

机构信息

Department of Genetic Medicine and Development, University of Geneva, 1211 Geneva 4, Switzerland.

出版信息

Nucleic Acids Res. 2013 Feb 1;41(3):e48. doi: 10.1093/nar/gks1214. Epub 2012 Dec 11.

Abstract

Existence of some extra-genetic (epigenetic) codes has been postulated since the discovery of the primary genetic code. Evident effects of histone post-translational modifications or DNA methylation over the efficiency and the regulation of DNA processes are supporting this postulation. EMdeCODE is an original algorithm that approximate the genomic distribution of given DNA features (e.g. promoter, enhancer, viral integration) by identifying relevant ChIPSeq profiles of post-translational histone marks or DNA binding proteins and combining them in a supermark. EMdeCODE kernel is essentially a two-step procedure: (i) an expectation-maximization process calculates the mixture of epigenetic factors that maximize the Sensitivity (recall) of the association with the feature under study; (ii) the approximated density is then recursively trimmed with respect to a control dataset to increase the precision by reducing the number of false positives. EMdeCODE densities improve significantly the prediction of enhancer loci and retroviral integration sites with respect to previous methods. Importantly, it can also be used to extract distinctive factors between two arbitrary conditions. Indeed EMdeCODE identifies unexpected epigenetic profiles specific for coding versus non-coding RNA, pointing towards a new role for H3R2me1 in coding regions.

摘要

自从发现主要遗传密码以来,人们就假设存在一些额外的遗传(表观遗传)密码。组蛋白翻译后修饰或 DNA 甲基化对 DNA 过程的效率和调控的明显影响支持了这一假设。EMdeCODE 是一种原始算法,通过识别相关的 ChIPSeq 后翻译组蛋白标记或 DNA 结合蛋白的图谱,并将它们组合在一个超级标记中,来近似给定 DNA 特征(例如启动子、增强子、病毒整合)的基因组分布。EMdeCODE 核是一个两步过程:(i)期望最大化过程计算出最大化与研究特征关联的敏感性(召回率)的表观遗传因素的混合物;(ii)然后递归地根据控制数据集修剪近似密度,以减少假阳性来提高精度。与以前的方法相比,EMdeCODE 密度显著提高了增强子位置和逆转录病毒整合位点的预测。重要的是,它还可以用于提取两个任意条件之间的独特因素。事实上,EMdeCODE 确定了 H3R2me1 在编码区域中具有编码与非编码 RNA 之间的新作用的意想不到的表观遗传特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef53/3561958/0e8170ff8d4d/gks1214f1p.jpg

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