Guidelines for Genome-Scale Analysis of Biological Rhythms

Michael E. Hughes*, Katherine C. Abruzzi, Ravi Allada, Ron Anafi, Alaaddin Bulak Arpat, Gad Asher, Pierre Baldi, Charissa de Bekker, Deborah Bell-Pedersen, Justin Blau, Steve Brown, M. Fernanda Ceriani, Zheng Chen, Joanna C. Chiu, Juergen Cox, Alexander M. Crowell, Jason P. DeBruyne, Derk Jan Dijk, Luciano DiTacchio, Francis J. DoyleGiles E. Duffield, Jay C. Dunlap, Kristin Eckel-Mahan, Karyn A. Esser, Garret A. FitzGerald, Daniel B. Forger, Lauren J. Francey, Ying Hui Fu, Frédéric Gachon, David Gatfield, Paul de Goede, Susan S. Golden, Carla Green, John Harer, Stacey Harmer, Jeff Haspel, Michael H. Hastings, Hanspeter Herzel, Erik D. Herzog, Christy Hoffmann, Christian Hong, Jacob J. Hughey, Jennifer M. Hurley, Horacio O. de la Iglesia, Carl Johnson, Steve A. Kay, Nobuya Koike, Karl Kornacker, Achim Kramer, Katja Lamia, Tanya Leise, Scott A. Lewis, Jiajia Li, Xiaodong Li, Andrew C. Liu, Jennifer J. Loros, Tami A. Martino, Jerome S. Menet, Martha Merrow, Andrew J. Millar, Todd Mockler, Felix Naef, Emi Nagoshi, Michael N. Nitabach, Maria Olmedo, Dmitri A. Nusinow, Louis J. Ptáček, David Rand, Akhilesh B. Reddy, Maria S. Robles, Till Roenneberg, Michael Rosbash, Marc D. Ruben, Samuel S.C. Rund, Aziz Sancar, Paolo Sassone-Corsi, Amita Sehgal, Scott Sherrill-Mix, Debra J. Skene, Kai Florian Storch, Joseph S. Takahashi, Hiroki R. Ueda, Han Wang, Charles Weitz, Pål O. Westermark, Herman Wijnen, Ying Xu, Gang Wu, Seung Hee Yoo, Michael Young, Eric Erquan Zhang, Tomasz Zielinski, John B. Hogenesch

*Corresponding author for this work

Research output: Contribution to journalArticle

59 Scopus citations

Abstract

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them.

Original languageEnglish (US)
Pages (from-to)380-393
Number of pages14
JournalJournal of biological rhythms
Volume32
Issue number5
DOIs
StatePublished - Oct 1 2017

Keywords

  • ChIP-seq
  • RNA-seq
  • biostatistics
  • circadian rhythms
  • computational biology
  • diurnal rhythms
  • functional genomics
  • guidelines
  • metabolomics
  • proteomics
  • systems biology

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)

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  • Cite this

    Hughes, M. E., Abruzzi, K. C., Allada, R., Anafi, R., Arpat, A. B., Asher, G., Baldi, P., de Bekker, C., Bell-Pedersen, D., Blau, J., Brown, S., Ceriani, M. F., Chen, Z., Chiu, J. C., Cox, J., Crowell, A. M., DeBruyne, J. P., Dijk, D. J., DiTacchio, L., ... Hogenesch, J. B. (2017). Guidelines for Genome-Scale Analysis of Biological Rhythms. Journal of biological rhythms, 32(5), 380-393. https://doi.org/10.1177/0748730417728663