TY - JOUR
T1 - Guidelines for Genome-Scale Analysis of Biological Rhythms.
JF - J Biol Rhythms
Y1 - 2017
A1 - Hughes, Michael E
A1 - Abruzzi, Katherine C
A1 - Allada, Ravi
A1 - Anafi, Ron
A1 - Arpat, Alaaddin Bulak
A1 - Asher, Gad
A1 - Baldi, Pierre
A1 - de Bekker, Charissa
A1 - Bell-Pedersen, Deborah
A1 - Blau, Justin
A1 - Brown, Steve
A1 - Ceriani, M Fernanda
A1 - Chen, Zheng
A1 - Chiu, Joanna C
A1 - Cox, Juergen
A1 - Crowell, Alexander M
A1 - DeBruyne, Jason P
A1 - Dijk, Derk-Jan
A1 - DiTacchio, Luciano
A1 - Doyle, Francis J
A1 - Duffield, Giles E
A1 - Dunlap, Jay C
A1 - Eckel-Mahan, Kristin
A1 - Esser, Karyn A
A1 - FitzGerald, Garret A
A1 - Forger, Daniel B
A1 - Francey, Lauren J
A1 - Fu, Ying-Hui
A1 - Gachon, Frédéric
A1 - Gatfield, David
A1 - de Goede, Paul
A1 - Golden, Susan S
A1 - Green, Carla
A1 - Harer, John
A1 - Harmer, Stacey
A1 - Haspel, Jeff
A1 - Hastings, Michael H
A1 - Herzel, Hanspeter
A1 - Herzog, Erik D
A1 - Hoffmann, Christy
A1 - Hong, Christian
A1 - Hughey, Jacob J
A1 - Hurley, Jennifer M
A1 - de la Iglesia, Horacio O
A1 - Johnson, Carl
A1 - Kay, Steve A
A1 - Koike, Nobuya
A1 - Kornacker, Karl
A1 - Kramer, Achim
A1 - Lamia, Katja
A1 - Leise, Tanya
A1 - Lewis, Scott A
A1 - Li, Jiajia
A1 - Li, Xiaodong
A1 - Liu, Andrew C
A1 - Loros, Jennifer J
A1 - Martino, Tami A
A1 - Menet, Jerome S
A1 - Merrow, Martha
A1 - Millar, Andrew J
A1 - Mockler, Todd
A1 - Naef, Felix
A1 - Nagoshi, Emi
A1 - Nitabach, Michael N
A1 - Olmedo, Maria
A1 - Nusinow, Dmitri A
A1 - Ptáček, Louis J
A1 - Rand, David
A1 - Reddy, Akhilesh B
A1 - Robles, Maria S
A1 - Roenneberg, Till
A1 - Rosbash, Michael
A1 - Ruben, Marc D
A1 - Rund, Samuel S C
A1 - Sancar, Aziz
A1 - Sassone-Corsi, Paolo
A1 - Sehgal, Amita
A1 - Sherrill-Mix, Scott
A1 - Skene, Debra J
A1 - Storch, Kai-Florian
A1 - Takahashi, Joseph S
A1 - Ueda, Hiroki R
A1 - Wang, Han
A1 - Weitz, Charles
A1 - Westermark, Pål O
A1 - Wijnen, Herman
A1 - Xu, Ying
A1 - Wu, Gang
A1 - Yoo, Seung-Hee
A1 - Young, Michael
A1 - Zhang, Eric Erquan
A1 - Zielinski, Tomasz
A1 - Hogenesch, John B
AB - 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.

VL - 32
IS - 5
U1 - http://www.ncbi.nlm.nih.gov/pubmed/29098954?dopt=Abstract
ER -