TREEOME：一个整合表观遗传学和转录组学数据以探索控制转录的调控相互作用的框架
TREEOME: A framework for epigenetic and transcriptomic data integration to explore regulatory interactions controlling transcription
摘要：
动机：基因表达的预测建模是通过整合高通量组学数据进行转录调控相互作用的计算机探索的有力框架。以前的方法的一个主要限制是他们无法处理条件和协同作用时出现的集体分析基因受到不同的监管机制。这种限制降低了整体预测能力，从而降低了下游生物推断的可靠性。结果如下：我们引入了一个分析建模框架（TREEOME：表达模型树），它通过将基因分离成假定的调控类来整合表观遗传和转录组数据。目前的预测建模方法已经发现DNA甲基化和组蛋白修饰表观遗传数据在预测转录本丰度的准确性方面提供很少或没有改善，尽管例如mRNA水平和启动子定位的DNA甲基化之间存在明显的反相关性。为了改善这一点，在TREEOME中，我们评估了四种可能的方法来制定基因水平的DNA甲基化指标，这为识别基因水平的甲基化事件和随后的差异分析提供了基础，而大多数以前的技术都是在单个CpG二核苷酸的水平上操作的。我们通过整合基因水平的DNA甲基化（亚硫酸氢盐-seq）和组蛋白修饰（ChIP-seq）数据来准确预测H1-hESC和GM 12878细胞系的全基因组mRNA转录丰度（RNA-seq），从而证明了TREEOME。可用性：TREEOME使用开源软件实现，并作为预配置的可引导参考环境提供。本研究中提供的所有脚本和数据均可在http://sourceforge.net/projects/budden2015treeome/上获得。
英文摘要：
Motivation: Predictive modelling of gene expression is a powerful framework for the in silico exploration of transcriptional regulatory interactions through the integration of high-throughput -omics data. A major limitation of previous approaches is their inability to handle conditional and synergistic interactions that emerge when collectively analysing genes subject to different regulatory mechanisms. This limitation reduces overall predictive power and thus the reliability of downstream biological inference. Results: We introduce an analytical modelling framework (TREEOME: tree of models of expression) that integrates epigenetic and transcriptomic data by separating genes into putative regulatory classes. Current predictive modelling approaches have found both DNA methylation and histone modification epigenetic data to provide little or no improvement in accuracy of prediction of transcript abundance despite, for example, distinct anti-correlation between mRNA levels and promoter-localised DNA methylation. To improve on this, in TREEOME we evaluate four possible methods of formulating gene-level DNA methylation metrics, which provide a foundation for identifying gene-level methylation events and subsequent differential analysis, whereas most previous techniques operate at the level of individual CpG dinucleotides. We demonstrate TREEOME by integrating gene-level DNA methylation (bisulfite-seq) and histone modification (ChIP-seq) data to accurately predict genome-wide mRNA transcript abundance (RNA-seq) for H1-hESC and GM12878 cell lines. Availability: TREEOME is implemented using open-source software and made available as a pre-configured bootable reference environment. All scripts and data presented in this study are available online at http://sourceforge.net/projects/budden2015treeome/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
