Guidelines for Genome-Scale Analysis of Biological Rhythms.

  • publication
  • 04-11-2017

Hughes ME, Abruzzi KC, Allada R, Anafi R, Arpat AB, Asher G, Baldi P, de Bekker C, Bell-Pedersen D, Blau J, Brown S, Ceriani MF, Chen Z, Chiu JC, Cox J, Crowell AM, DeBruyne JP, Dijk DJ, DiTacchio L, Doyle FJ, Duffield GE, Dunlap JC, Eckel-Mahan K, Esser KA, FitzGerald GA, Forger DB, Francey LJ, Fu YH, Gachon F, Gatfield D, de Goede P, Golden SS, Green C, Harer J, Harmer S, Haspel J, Hastings MH, Herzel H, Herzog ED, Hoffmann C, Hong C, Hughey JJ, Hurley JM, de la Iglesia HO, Johnson C, Kay SA, Koike N, Kornacker K, Kramer A, Lamia K, Leise T, Lewis SA, Li J, Li X, Liu AC, Loros JJ, Martino TA, Menet JS, Merrow M, Millar AJ, Mockler T, Naef F, Nagoshi E, Nitabach MN, Olmedo M, Nusinow DA, Ptáček LJ, Rand D, Reddy AB, Robles MS, Roenneberg T, Rosbash M, Ruben MD, Rund SSC, Sancar A, Sassone-Corsi P, Sehgal A, Sherrill-Mix S, Skene DJ, Storch KF, Takahashi JS, Ueda HR, Wang H, Weitz C, Westermark PO, Wijnen H, Xu Y, Wu G, Yoo SH, Young M, Zhang EE, Zielinski T, Hogenesch JB. J. Biol. Rhythms 2017 Nov;():748730417728663. 10.1177/0748730417728663.

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.

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