With the help of high-throughput NGS (next-generation sequencing) technologies, ancestral transcriptome reconstruction is helpful to understand the complexity of transcriptional regulatory systems that underlies the evolution of multiple cellular metazoans with sophisticated functions and distinctive morphologies. To this end, we report a new method of ancestral state inference. The new method used Ornstein-Uhlenbeck (OU) model, which is more biologically realistic, to replace the Brownian motion (BM) model and is suitable for multi-transcriptome data. Implemented in the free R package, AnceTran is specially designed for RNA-seq and ChIP-seq data, which is feasible. It should be noticed that our work will be integrated to a unified, statistically-sound phylogenetic framework to study the evolution of many other molecular phenomes such as proteomics, chromatin accessibility, methylation status, and metabolomics. We exemplify our method by a case study, using the ChIP-seq binding data of three liver-specific transcription factors and the RNA-seq liver expression data in four closely related mice species, and some technical issues are discussed.

中文翻译：

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基于 RNA-Seq 和 ChIP-seq 数据的祖先转录组推断

在高通量 NGS（下一代测序）技术的帮助下，祖先转录组重建有助于理解转录调控系统的复杂性，而转录调控系统是多种具有复杂功能和独特形态的细胞后生动物进化的基础。为此，我们报告了一种新的祖先状态推断方法。新方法使用更具生物学真实性的 Ornstein-Uhlenbeck (OU) 模型代替布朗运动 (BM) 模型，适用于多转录组数据。在免费的 R 包中实现，AnceTran 是专门为 RNA-seq 和 ChIP-seq 数据设计的，这是可行的。需要注意的是，我们的工作将整合到一个统一的、统计健全的系统发育框架，用于研究许多其他分子表型的进化，例如蛋白质组学、染色质可及性、甲基化状态和代谢组学。我们通过案例研究举例说明了我们的方法，使用三个肝脏特异性转录因子的 ChIP-seq 结合数据和四个密切相关的小鼠物种的 RNA-seq 肝脏表达数据，并讨论了一些技术问题。