Circadian rhythms are daily endogenous oscillations of behavior, metabolism, and physiology. At a molecular level, these oscillations are generated by transcriptional-translational feedback loops composed of core clock genes. In turn, core clock genes drive the rhythmic accumulation of downstream outputs-termed clock-controlled genes (CCGs)-whose rhythmic translation and function ultimately underlie daily oscillations at a cellular and organismal level. Given the circadian clock’s profound influence on human health and behavior, considerable efforts have been made to systematically identify CCGs.
The recent development of next-generation sequencing has dramatically expanded our ability to study the expression, processing, and stability of rhythmically expressed mRNAs. Nevertheless, like any new technology, there are many technical issues to be addressed.
Here, researchers from the University of Missouri-St. Louis discuss considerations for studying circadian rhythms using genome scale transcriptional profiling, with a particular emphasis on RNA sequencing. They make a number of practical recommendations-including the choice of sampling density, read depth, alignment algorithms, read-depth normalization, and cycling detection algorithms-based on computational simulations and our experience from previous studies.