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 language | English (US) |
|---|---|
| Pages (from-to) | 380-393 |
| Number of pages | 14 |
| Journal | Journal of biological rhythms |
| Volume | 32 |
| Issue number | 5 |
| DOIs | |
| State | Published - 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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Guidelines for Genome-Scale Analysis of Biological Rhythms. / Hughes, Michael E.; Abruzzi, Katherine C.; Allada, Ravi; Anafi, Ron; Arpat, Alaaddin Bulak; Asher, Gad; Baldi, Pierre; de Bekker, Charissa; Bell-Pedersen, Deborah; Blau, Justin; Brown, Steve; Ceriani, M. Fernanda; Chen, Zheng; Chiu, Joanna C.; Cox, Juergen; Crowell, Alexander M.; DeBruyne, Jason P.; Dijk, Derk Jan; DiTacchio, Luciano; Doyle, Francis J.; Duffield, Giles E.; Dunlap, Jay C.; Eckel-Mahan, Kristin; Esser, Karyn A.; FitzGerald, Garret A.; Forger, Daniel B.; Francey, Lauren J.; Fu, Ying Hui; Gachon, Frédéric; Gatfield, David; de Goede, Paul; Golden, Susan S.; Green, Carla; Harer, John; Harmer, Stacey; Haspel, Jeff; Hastings, Michael H.; Herzel, Hanspeter; Herzog, Erik D.; Hoffmann, Christy; Hong, Christian; Hughey, Jacob J.; Hurley, Jennifer M.; de la Iglesia, Horacio O.; Johnson, Carl; Kay, Steve A.; Koike, Nobuya; Kornacker, Karl; Kramer, Achim; Lamia, Katja; Leise, Tanya; Lewis, Scott A.; Li, Jiajia; Li, Xiaodong; Liu, Andrew C.; Loros, Jennifer J.; Martino, Tami A.; Menet, Jerome S.; Merrow, Martha; Millar, Andrew J.; Mockler, Todd; Naef, Felix; Nagoshi, Emi; Nitabach, Michael N.; Olmedo, Maria; Nusinow, Dmitri A.; Ptáček, Louis J.; Rand, David; Reddy, Akhilesh B.; Robles, Maria S.; Roenneberg, Till; Rosbash, Michael; Ruben, Marc D.; Rund, Samuel S.C.; Sancar, Aziz; Sassone-Corsi, Paolo; Sehgal, Amita; Sherrill-Mix, Scott; Skene, Debra J.; Storch, Kai Florian; Takahashi, Joseph S.; Ueda, Hiroki R.; Wang, Han; Weitz, Charles; Westermark, Pål O.; Wijnen, Herman; Xu, Ying; Wu, Gang; Yoo, Seung Hee; Young, Michael; Zhang, Eric Erquan; Zielinski, Tomasz; Hogenesch, John B.
In: Journal of biological rhythms, Vol. 32, No. 5, 01.10.2017, p. 380-393.Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Guidelines for Genome-Scale Analysis of Biological Rhythms
AU - Hughes, Michael E.
AU - Abruzzi, Katherine C.
AU - Allada, Ravi
AU - Anafi, Ron
AU - Arpat, Alaaddin Bulak
AU - Asher, Gad
AU - Baldi, Pierre
AU - de Bekker, Charissa
AU - Bell-Pedersen, Deborah
AU - Blau, Justin
AU - Brown, Steve
AU - Ceriani, M. Fernanda
AU - Chen, Zheng
AU - Chiu, Joanna C.
AU - Cox, Juergen
AU - Crowell, Alexander M.
AU - DeBruyne, Jason P.
AU - Dijk, Derk Jan
AU - DiTacchio, Luciano
AU - Doyle, Francis J.
AU - Duffield, Giles E.
AU - Dunlap, Jay C.
AU - Eckel-Mahan, Kristin
AU - Esser, Karyn A.
AU - FitzGerald, Garret A.
AU - Forger, Daniel B.
AU - Francey, Lauren J.
AU - Fu, Ying Hui
AU - Gachon, Frédéric
AU - Gatfield, David
AU - de Goede, Paul
AU - Golden, Susan S.
AU - Green, Carla
AU - Harer, John
AU - Harmer, Stacey
AU - Haspel, Jeff
AU - Hastings, Michael H.
AU - Herzel, Hanspeter
AU - Herzog, Erik D.
AU - Hoffmann, Christy
AU - Hong, Christian
AU - Hughey, Jacob J.
AU - Hurley, Jennifer M.
AU - de la Iglesia, Horacio O.
AU - Johnson, Carl
AU - Kay, Steve A.
AU - Koike, Nobuya
AU - Kornacker, Karl
AU - Kramer, Achim
AU - Lamia, Katja
AU - Leise, Tanya
AU - Lewis, Scott A.
AU - Li, Jiajia
AU - Li, Xiaodong
AU - Liu, Andrew C.
AU - Loros, Jennifer J.
AU - Martino, Tami A.
AU - Menet, Jerome S.
AU - Merrow, Martha
AU - Millar, Andrew J.
AU - Mockler, Todd
AU - Naef, Felix
AU - Nagoshi, Emi
AU - Nitabach, Michael N.
AU - Olmedo, Maria
AU - Nusinow, Dmitri A.
AU - Ptáček, Louis J.
AU - Rand, David
AU - Reddy, Akhilesh B.
AU - Robles, Maria S.
AU - Roenneberg, Till
AU - Rosbash, Michael
AU - Ruben, Marc D.
AU - Rund, Samuel S.C.
AU - Sancar, Aziz
AU - Sassone-Corsi, Paolo
AU - Sehgal, Amita
AU - Sherrill-Mix, Scott
AU - Skene, Debra J.
AU - Storch, Kai Florian
AU - Takahashi, Joseph S.
AU - Ueda, Hiroki R.
AU - Wang, Han
AU - Weitz, Charles
AU - Westermark, Pål O.
AU - Wijnen, Herman
AU - Xu, Ying
AU - Wu, Gang
AU - Yoo, Seung Hee
AU - Young, Michael
AU - Zhang, Eric Erquan
AU - Zielinski, Tomasz
AU - Hogenesch, John B.
N1 - Funding Information: We thank members of the Hughes and Hogenesch labs for useful comments during the drafting of this article. We thank the organizers of the “Big Data” workshop during the 2016 meeting of the Society for Research on Biological Rhythms for providing the initial impetus for exploring the issues discussed herein. Work in the Hughes Lab is supported by an award from NIAMS (1R21AR069266) and start-up funds from the Department of Medicine at Washington University in St. Louis. The work of Pierre Baldi is supported in part by DARPA grant D17AP00002. Charissa de Bekker is supported by start-up funds from the Department of Biology at the University of Central Florida in Orlando, Florida. Justin Blau’s laboratory is supported by National Institutes of Health (NIH) grant GM063911. Zheng Chen’s lab is supported by the Robert A. Welch Foundation (AU-1731) and NIH/National Institute on Aging (R01AG045828). Work in Joanna Chiu’s laboratory is supported by NIH R01 GM102225 and National Science Foundation (NSF) IOS 1456297. Alexander M. Crowell and Jay C. Dunlap are supported by R35GM118021 and by U01EB022546. Jason DeBruyne’s lab is supported by National Institute of Neurological Disorders and Stroke (NINDS) U54 NS083932 and National Institute of General Medical Sciences (NIGMS) SC1 GM109861. Derk-Jan Dijk is supported by the Biotechnology and Biological Sciences Research Council and a Royal Society Wolfson Research Merit Award. Work in Giles Duffield’s lab is supported by NIGMS (R01-GM087508) and the Eck Institute for Global Health. Work in Susan S. Golden’s laboratory is supported by NIH award R35GM118290. Work in Carla Green’s laboratory is supported by NIH grants R01GM112991, R01GM111387, and R01AG045795. Work in Stacey Harmer’s laboratory is supported by NIH award R01GM069418 and NSF award IOS1238040. Work in Michael Hastings’s lab is supported by the UK Medical Research Council (MC_ U105170643). Erik D. Herzog’s lab is supported by NIH grants U01EB021956, R01NS095367, and R01GM104991. Work in the Hong laboratory is supported by the National Institute of Allergy and Infectious Diseases (U19AI116491). Work in the Hurley lab is supported by an award from National Institute of Biomedical Imaging and Bioengineering (1U01EB022546) and start-up funds from the Department of Biological Sciences at Rensselaer Polytechnic Institute. Horacio de la Iglesia is supported by NIH award R01 NS094211. Nobuya Koike is supported by JSPS KAKENHI grant JP26293048. Work in Achim Kramer’s laboratory is supported by the Deutsche Forschungsgemeinschaft (SFB740/D2 and TRR186/A17). Related work in the Lamia lab is supported by an award from the National Institute of Diabetes and Digestive and Kidney Diseases (DK097164). Andrew C. Liu is supported by NIH grant NINDS R01NS054794. Jennifer J. Loros is supported by R35GM118022. Tami A. Martino’s laboratory is supported by the Canadian Institutes of Health Research and the Heart and Stroke Foundation of Canada. Work in the Merrow lab is supported by a grant from the STW (Dutch Foundation for Technology and Science), the Volkswagen Foundation, and funds from the Ludwig-Maximilians University Munich. Work in the laboratory of Michael N. Nitabach is supported in part by NINDS, NIH (R01NS091070) and NIGMS, NIH (R01GM098931). Work in the Nusinow lab is supported by NSF grant IOS-1456796. Maria Olmedo is supported by the Ramón y Cajal program of the Spanish Ministerio de Economía y Competitividad (RYC-2014-15551). Akhilesh B. Reddy is supported by the Wellcome Trust (100333/Z/12/Z) and the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001534), the U.K. Medical Research Council (FC001534), and the Wellcome Trust (FC001534). Maria Robles’s lab is supported by start-up funds from the Ludwig-Maximiliam University, Munich, Germany. Samuel S. C. Rund is funded by the Royal Society (NF140517). Han Wang is funded by the grants from National Basic Research Program of China (973 Program; 2012CB947600) and the National Natural Science Foundation of China (31030062, 81570171, 81070455). Pål O. Westermark is funded by the Leibniz Institute for Farm Animal Biology. Herman Wijnen is funded by a Biotechnology and Biological Science Research Council grant BB/L023067/1 and EU Marie Sklodowska Curie Career Integration grant 618563. Seung-Hee Yoo’s lab is supported by NIH/NIGMS (R01GM114424). John Hogenesch is supported by the NINDS (5R01NS05479). Publisher Copyright: © 2017, © 2017 The Author(s).
PY - 2017/10/1
Y1 - 2017/10/1
N2 - 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.
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.
KW - ChIP-seq
KW - RNA-seq
KW - biostatistics
KW - circadian rhythms
KW - computational biology
KW - diurnal rhythms
KW - functional genomics
KW - guidelines
KW - metabolomics
KW - proteomics
KW - systems biology
UR - http://www.scopus.com/inward/record.url?scp=85034445356&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85034445356&partnerID=8YFLogxK
U2 - 10.1177/0748730417728663
DO - 10.1177/0748730417728663
M3 - Article
C2 - 29098954
AN - SCOPUS:85034445356
VL - 32
SP - 380
EP - 393
JO - Journal of Biological Rhythms
JF - Journal of Biological Rhythms
SN - 0748-7304
IS - 5
ER -