As the cost of sequencing continues to drop, RNAseq is expected to replace microarrays for many applications that involve determining the structure and dynamics of the transcriptome . While likely to revolutionize our understanding of transcriptome evolution, especially in non-model systems, a possible caveat is that sources of bias in coverage and therefore gene expression estimates are not yet fully understood. For example, it appears that base composition, library preparation and transcript length may all impact gene expression estimates . As reviewed by , many challenges remain when dealing with next generation sequence data. In addition, more work is necessary to generalise the relationship between the evolution of promoter sequences, protein coding regions and gene expression in plants . Our study provides a significant advance in our understanding of molecular evolution. It reveals several general trends that appear to be robust to variables such as timing of divergence or experimental growth conditions, given that they have been reported across a broad range of taxa. At the same time, a meta-analysis of studies conducted under different environmental conditions, developmental stages, and species may reveal novel insights into plant molecular evolution that are not apparent when studying only a single condition. With the increased availability of high-throughput sequencing datasets, such large-scale studies are becoming increasingly feasible.