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. Doyle; Giles 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

doi:10.1177/0748730417728663

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

Research output: Contribution to journal › Article › peer-review

135 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 language	English
Pages (from-to)	380-393
Number of pages	14
Journal	Journal of Biological Rhythms
Volume	32
Issue number	5
DOIs	https://doi.org/10.1177/0748730417728663
State	Published - Oct 1 2017

Keywords

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

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10.1177/0748730417728663

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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

@article{5a39aef4d8974293a18decefb405cb5d,

title = "Guidelines for Genome-Scale Analysis of Biological Rhythms",

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.",

keywords = "ChIP-seq, RNA-seq, biostatistics, circadian rhythms, computational biology, diurnal rhythms, functional genomics, guidelines, metabolomics, proteomics, systems biology",

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

note = "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{\textquoteright}s laboratory is supported by National Institutes of Health (NIH) grant GM063911. Zheng Chen{\textquoteright}s lab is supported by the Robert A. Welch Foundation (AU-1731) and NIH/National Institute on Aging (R01AG045828). Work in Joanna Chiu{\textquoteright}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{\textquoteright}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{\textquoteright}s lab is supported by NIGMS (R01-GM087508) and the Eck Institute for Global Health. Work in Susan S. Golden{\textquoteright}s laboratory is supported by NIH award R35GM118290. Work in Carla Green{\textquoteright}s laboratory is supported by NIH grants R01GM112991, R01GM111387, and R01AG045795. Work in Stacey Harmer{\textquoteright}s laboratory is supported by NIH award R01GM069418 and NSF award IOS1238040. Work in Michael Hastings{\textquoteright}s lab is supported by the UK Medical Research Council (MC_ U105170643). Erik D. Herzog{\textquoteright}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{\textquoteright}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{\textquoteright}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 Ram{\'o}n y Cajal program of the Spanish Ministerio de Econom{\'i}a 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{\textquoteright}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). P{\aa}l 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{\textquoteright}s lab is supported by NIH/NIGMS (R01GM114424). John Hogenesch is supported by the NINDS (5R01NS05479). Publisher Copyright: {\textcopyright} 2017, {\textcopyright} 2017 The Author(s).",

year = "2017",

month = oct,

day = "1",

doi = "10.1177/0748730417728663",

language = "English",

volume = "32",

pages = "380--393",

journal = "Journal of Biological Rhythms",

issn = "0748-7304",

number = "5",

}

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 2017, 'Guidelines for Genome-Scale Analysis of Biological Rhythms', Journal of Biological Rhythms, vol. 32, no. 5, pp. 380-393. https://doi.org/10.1177/0748730417728663

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

U2 - 10.1177/0748730417728663

DO - 10.1177/0748730417728663

M3 - Article

C2 - 29098954

AN - SCOPUS:85034445356

SN - 0748-7304

VL - 32

SP - 380

EP - 393

JO - Journal of Biological Rhythms

JF - Journal of Biological Rhythms

IS - 5

ER -

Guidelines for Genome-Scale Analysis of Biological Rhythms

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Keywords

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