Thanks to the microarray technology, our understanding of transcriptome evolution at the genome level has been considerably advanced in the past decade. Yet, further investigation was challenged by several technical limitations of this technology. Recent innovation of next-generation sequencing, particularly the invention of RNA-seq technology, has shed insightful lights on resolving this problem. Though a number of statistical and computational methods have been developed to analyze RNA-seq data, the analytical framework specifically designed for evolutionary genomics remains an open question. In this article we develop a new method for estimating the genome expression distance from the RNA-seq data, which has explicit interpretations under the model of gene expression evolution. Moreover, this distance measure takes the data overdispersion, gene length variation, and sequencing depth variation into account so that it can be applied to multiple genomes from different species. Using mammalian RNA-seq data as example, we demonstrated that this expression distance is useful in phylogenomic analysis.
Keywords: RNA-seq; genome expression distance; transcriptome evolution.