Using methylation data to improve transcription factor binding prediction.

IF 2.9 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Epigenetics Pub Date : 2024-12-01 Epub Date: 2024-02-01 DOI:10.1080/15592294.2024.2309826
Daniel Morgan, Dawn L DeMeo, Kimberly Glass
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引用次数: 0

Abstract

Modelling the regulatory mechanisms that determine cell fate, response to external perturbation, and disease state depends on measuring many factors, a task made more difficult by the plasticity of the epigenome. Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) can be used to estimate transcription factor (TF) binding locations. However, this approach does not incorporate information regarding the epigenetic context necessary for TF binding. CpG methylation is an epigenetic mark influenced by environmental factors that is commonly assayed in human cohort studies. We developed a framework to score inferred TF binding locations using methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole-genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that for most TFs, binding prediction improves using methylation-based scoring compared to standard PWM-scores. We also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context-specific TF binding using methylation data. Importantly, the availability of DNA methylation data in existing patient populations provides an opportunity to use our approach to understand the impact of methylation on gene regulatory processes in the context of human disease.

利用甲基化数据改进转录因子结合预测。
对决定细胞命运、对外部扰动的反应和疾病状态的调控机制建模取决于对许多因素的测量,而表观基因组的可塑性使这项任务变得更加困难。扫描基因组中由位置权重矩阵(PWM)定义的序列模式可用于估计转录因子(TF)的结合位置。然而,这种方法并不包含 TF 结合所需的表观遗传背景信息。CpG 甲基化是一种受环境因素影响的表观遗传标记,通常在人类队列研究中进行检测。我们开发了一个框架,利用甲基化数据对推断的 TF 结合位置进行评分。我们将利用 PWMs 确定的主题位置与全基因组亚硫酸氢盐测序和 Illumina EPIC 阵列数据中捕获的甲基化信息相交叉,根据这些数据对主题位置进行评分,并与表征 TF 结合的实验数据(ChIP-seq)进行比较。我们发现,与标准的 PWM 评分相比,基于甲基化的评分能改进大多数 TF 的结合预测。我们还说明,当甲基化信息只有近端可用时,我们的方法可以推广到推断 TF 的结合,即测量与主题位置不直接重叠的附近 CpGs。总之,我们的方法为利用甲基化数据推断特异性 TF 结合提供了一个框架。重要的是,现有患者群体中 DNA 甲基化数据的可用性为利用我们的方法了解甲基化在人类疾病背景下对基因调控过程的影响提供了机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Epigenetics
Epigenetics 生物-生化与分子生物学
CiteScore
6.80
自引率
2.70%
发文量
82
审稿时长
3-8 weeks
期刊介绍: Epigenetics publishes peer-reviewed original research and review articles that provide an unprecedented forum where epigenetic mechanisms and their role in diverse biological processes can be revealed, shared, and discussed. Epigenetics research studies heritable changes in gene expression caused by mechanisms others than the modification of the DNA sequence. Epigenetics therefore plays critical roles in a variety of biological systems, diseases, and disciplines. Topics of interest include (but are not limited to): DNA methylation Nucleosome positioning and modification Gene silencing Imprinting Nuclear reprogramming Chromatin remodeling Non-coding RNA Non-histone chromosomal elements Dosage compensation Nuclear organization Epigenetic therapy and diagnostics Nutrition and environmental epigenetics Cancer epigenetics Neuroepigenetics
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