Guidelines for Genome-Scale Analysis of Biological Rhythms

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 discove...

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Detalles Bibliográficos
Autores: Hughes, Michael E., Abruzzi, Katharine Compton, Allada, Ravi, Anafi, Ron C., Arpat, Alaaddin Bulak, Asher, Gad, Olmedo López, María, Gachon, Frédéric
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/102284
Acceso en línea:https://hdl.handle.net/11441/102284
https://doi.org/10.1177%2F0748730417728663
Access Level:acceso abierto
Palabra clave:Biostatistics
ChIP-seq
Circadian rhythms
Computational biology
Diurnal rhythms
Functional genomics
Guidelines
Metabolomics
Proteomics
RNA-seq
Systems biology
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spelling Guidelines for Genome-Scale Analysis of Biological RhythmsHughes, Michael E.Abruzzi, Katharine ComptonAllada, RaviAnafi, Ron C.Arpat, Alaaddin BulakAsher, GadOlmedo López, MaríaGachon, FrédéricBiostatisticsChIP-seqCircadian rhythmsComputational biologyDiurnal rhythmsFunctional genomicsGuidelinesMetabolomicsProteomicsRNA-seqSystems biologyGenome 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.SAGEGenética2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/102284https://doi.org/10.1177%2F0748730417728663reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésJournal of Biological Rhythms, 32 (5), 380-393.https://doi.org/10.1177%2F0748730417728663info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1022842026-06-17T12:51:07Z
dc.title.none.fl_str_mv Guidelines for Genome-Scale Analysis of Biological Rhythms
title Guidelines for Genome-Scale Analysis of Biological Rhythms
spellingShingle Guidelines for Genome-Scale Analysis of Biological Rhythms
Hughes, Michael E.
Biostatistics
ChIP-seq
Circadian rhythms
Computational biology
Diurnal rhythms
Functional genomics
Guidelines
Metabolomics
Proteomics
RNA-seq
Systems biology
title_short Guidelines for Genome-Scale Analysis of Biological Rhythms
title_full Guidelines for Genome-Scale Analysis of Biological Rhythms
title_fullStr Guidelines for Genome-Scale Analysis of Biological Rhythms
title_full_unstemmed Guidelines for Genome-Scale Analysis of Biological Rhythms
title_sort Guidelines for Genome-Scale Analysis of Biological Rhythms
dc.creator.none.fl_str_mv Hughes, Michael E.
Abruzzi, Katharine Compton
Allada, Ravi
Anafi, Ron C.
Arpat, Alaaddin Bulak
Asher, Gad
Olmedo López, María
Gachon, Frédéric
author Hughes, Michael E.
author_facet Hughes, Michael E.
Abruzzi, Katharine Compton
Allada, Ravi
Anafi, Ron C.
Arpat, Alaaddin Bulak
Asher, Gad
Olmedo López, María
Gachon, Frédéric
author_role author
author2 Abruzzi, Katharine Compton
Allada, Ravi
Anafi, Ron C.
Arpat, Alaaddin Bulak
Asher, Gad
Olmedo López, María
Gachon, Frédéric
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Genética
dc.subject.none.fl_str_mv Biostatistics
ChIP-seq
Circadian rhythms
Computational biology
Diurnal rhythms
Functional genomics
Guidelines
Metabolomics
Proteomics
RNA-seq
Systems biology
topic Biostatistics
ChIP-seq
Circadian rhythms
Computational biology
Diurnal rhythms
Functional genomics
Guidelines
Metabolomics
Proteomics
RNA-seq
Systems biology
description 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.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/102284
https://doi.org/10.1177%2F0748730417728663
url https://hdl.handle.net/11441/102284
https://doi.org/10.1177%2F0748730417728663
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Biological Rhythms, 32 (5), 380-393.
https://doi.org/10.1177%2F0748730417728663
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv SAGE
publisher.none.fl_str_mv SAGE
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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