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Transcriptome profiling at different times of day is powerful for studying circadian regulation in model organisms and humans. To date, 24 h profiles from many tissue types suggest that about half of all genes are circadian-expressed somewhere in the body. However, few of these studies focused on the brain. Thus, despite known links between circadian disruption and neurological disease, we have virtually no mechanistic understanding. In the coming decade, we expect more genome-wide studies of time of day in different brain diseases, regions, and cell types. We expect just as many different approaches to the design and analysis of these studies. This review considers key principles of circadian tran scriptomics, with the goal of maximizing utility and reproducibility of future studies in the nervous system.


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Genome-wide studies of time of day in the brain: Design and analysis

Show Author's information Gang Wu1Marc D. Ruben1Yinyeng Lee1Jiajia Li2Michael E. Hughes2John B. Hogenesch1( )
 Divisions of Human Genetics and Immunobiology, Center for Chronobiology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way, Cincinnati, OH 45229, U.S.A.
 Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine, St. Louis, MO 63310, U.S.A.

Abstract

Transcriptome profiling at different times of day is powerful for studying circadian regulation in model organisms and humans. To date, 24 h profiles from many tissue types suggest that about half of all genes are circadian-expressed somewhere in the body. However, few of these studies focused on the brain. Thus, despite known links between circadian disruption and neurological disease, we have virtually no mechanistic understanding. In the coming decade, we expect more genome-wide studies of time of day in different brain diseases, regions, and cell types. We expect just as many different approaches to the design and analysis of these studies. This review considers key principles of circadian tran scriptomics, with the goal of maximizing utility and reproducibility of future studies in the nervous system.

Keywords:

circadian, transcriptome, rhythmic analysis, brain
Received: 13 January 2020 Accepted: 27 February 2020 Published: 31 August 2020 Issue date: June 2020
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Publication history

Received: 13 January 2020
Accepted: 27 February 2020
Published: 31 August 2020
Issue date: June 2020

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© The authors 2020.

Acknowledgements

This review was inspired by the "Statistical Methods for Time Series Analysis of Rhythms" (SMTSAR) workshop on Society for Research on Biological Rhythms (SRBR) meeting held in 2016 (https://github.com/gangwug/SRBR_SMTSARworkshop2016) and 2018 (https://github.com/ gangwug/SRBR_SMTSARworkshop2018). We thank Tanya Leise for reading through the manuscript and supporting the SMTSAR workshop. We thank Tiago de Andrade, Robert E. Schmidt, Lauren J. Francey, David F. Smith, and organizers of SRBR meetings for supporting the SMTSAR workshop. We also thank all SMTSAR workshops attendees for valuable discussion. This work is supported by the National Institute of Neurological Disorders and Stroke (5R01NS054794-13 to JBH and Andrew Liu), the National Heart, Lung, and Blood Institute (5R01HL138551-02 to Eric Bittman and JBH), and the National Cancer Institute (1R01CA227485-01A1 to Ron Anafi and JBH).

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