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首页> 外文期刊>Epigenetics: official journal of the DNA Methylation Society >Conflicts of CpG density and DNA methylation are proximally and distally involved in gene regulation in human and mouse tissues
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Conflicts of CpG density and DNA methylation are proximally and distally involved in gene regulation in human and mouse tissues

机译:CPG密度和DNA甲基化的冲突近侧和远端参与人和小鼠组织的基因调节

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摘要

The relationship between CpG content and DNA methylation has attracted considerable interest in recent years. Direct or indirect methods have been developed to investigate their regulatory functions based on various hypotheses, large cohort studies, and meta-analyses. However, all of these analyses were performed at units of CpG blocks and, thus, the influence of finer genome structure has been neglected. Herein, we present a novel algorithm of base-pair resolution to systematically investigate the relationship between CpG contents and DNA methylation. By introducing the concept of 'complementary index' we examined the methylomes of 34 adult and 7 embryonic tissues and successfully fitted the relationship of DNA methylation and CpG density into a nonlinear mathematical model. A further algorithm was developed to locate the regions where CpG density does not match expectations from the model, termed 'conflict of gap' (COG) regions. Interestingly, COGs are highly concordant in human and mouse and their distributions display a tissue-specific pattern. Based on COG methylation patterns we correctly classified tissues according to their function or origin. We demonstrate that COGs based on our method can reveal more and deeper information than traditional differential methylation region (DMR) approaches. We also found that when COGs are located near to transcription start site (TSS), these regions can determine which promoters will be utilized for initiating gene transcription. Furthermore, COGs located far from the TSS perform as enhancers in terms of histone modification, sequence conservation, transcription factor binding, and DNase I-hypersensitivity.
机译:近年来,CPG含量和DNA甲基化之间的关系吸引了相当大的兴趣。已经开发了直接或间接方法来研究基于各种假设,大队列研究和荟萃分析的监管职能。然而,所有这些分析都以CPG块为单位进行,因此,较细基因组结构的影响被忽略了。这里,我们提出了一种新的基对分辨率算法,以系统地研究CPG含量与DNA甲基化之间的关系。通过介绍“互补指数”的概念,我们检查了34名成人和7个胚胎组织的甲基元素,并成功地拟合了DNA甲基化和CPG密度的关系进入非线性数学模型。开发了一种进一步的算法来定位CPG密度与模型的期望不匹配的区域,称为“差距冲突”(COG)区域。有趣的是,COGS在人和小鼠中高度协调,其分布显示组织特异性图案。基于COG甲基化模式,我们根据其功能或原点正确分类组织。我们证明基于我们方法的齿轮可以揭示比传统差分甲基化区域(DMR)方法更深入的信息。我们还发现,当COGS位于转录开始部位(TSS)附近时,这些区域可以确定哪些启动子将用于启动基因转录。此外,根据组蛋白修饰,序列保护,转录因子结合和DNase I-超敏感性,齿轮位于远离TSS的齿轮作为增强剂。

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  • 作者单位

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Dalian Med Univ Hosp 2 467th Zhongshan Rd Dalian 116023 Liaoning Peoples R China;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Dalian Med Univ Dept Pharm Dalian Peoples R China;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

    Pontificia Univ Catolica Chile Fac Biol Sci Santiago Chile;

    Dalian Med Univ Hosp 2 467th Zhongshan Rd Dalian 116023 Liaoning Peoples R China;

    Dalian Med Univ Inst Canc Stem Cell Ctr Genome &

    Personalized Med Dalian 116044 Liaoning;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 植物学;
  • 关键词

    Epigenome; DNA methylation; genome function; Next-generation sequencing; data mining;

    机译:表观蛋白酶;DNA甲基化;基因组功能;下一代测序;数据挖掘;

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