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Methods Enzymol. 2015;551:349-67. doi: 10.1016/bs.mie.2014.10.020. Epub 2014 Dec 26.

Considerations for RNA-seq analysis of circadian rhythms.

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Department of Biology, University of Missouri-St. Louis, St. Louis, Missouri, USA.
Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Department of Pharmacology, Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.
Department of Biology, University of Missouri-St. Louis, St. Louis, Missouri, USA. Electronic address:


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, we discuss considerations for studying circadian rhythms using genome scale transcriptional profiling, with a particular emphasis on RNA sequencing. We 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. We believe that these results will be of interest to the circadian field and help investigators design experiments to derive most values from these large and complex data sets.


Circadian rhythms; Clock-controlled genes; Computational biology; Gene expression; Genomics; Next-generation sequencing; RNA sequencing; Read depth

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