Ancestral transcriptome inference based on RNA-Seq and ChIP-seq data.

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.