Transcriptomic profiling with RNA-seq is increasingly common. Often, RNA-seq is used a first step to discover genes, followed by manipulation of said genes to show a causal relationship with some phenotype. That’s often the case, but sometimes, just the transcriptomic data are reported without any follow-up perturbations. The volume of data one gets from transcriptomic studies is huge, and reporting the findings in a clear and insightful way can be a challenging.

I’m struggling with this issue right now. But thankfully, there are some good papers to use as models for my own writing. Here are a few that I’ve found helpful.

• Henry, F. E. et al. Cell type-specific transcriptomics of hypothalamic energy-sensing neuron responses to weight-loss. eLife 4, e09800 (2015).

• Chiu, I. M. et al. Transcriptional profiling at whole population and single cell levels reveals somatosensory neuron molecular diversity. eLife 3, (2014).
• Zhang, Y. et al. An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex. Journal of Neuroscience 34, 11929–11947 (2014).