{"article_title":"Guidelines for Genome-Scale Analysis of Biological Rhythms.","author":"Hughes ME","journal_title":"Journal of biological rhythms","issn":"1552-4531","isbn":"","publication_date":"2020 Aug","volume":"32","issue":"5","first_page":"380","page_count":"","accession_number":"29098954","doi":"10.1177\/0748730417728663","publisher":"Sage Publishers","doctype":"Guideline","subjects":"Circadian Rhythm genetics; Genome; Genomics statistics & numerical data; Statistics as Topic methods; Biostatistics; Computational Biology methods; Humans; Metabolomics; Proteomics; Software; Systems Biology","interest_area":["Internal Medicine"],"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.","url":"https:\/\/search.ebscohost.com\/login.aspx?direct=true&db=mdl&AN=29098954","isPdfLink":false,"isSAML":true,"additionalInfo":{"Authored_By":"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, Pt\u00e1\u010dek 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","Journal_Info":"Publisher: Sage Publishers Country of Publication: United States NLM ID: 8700115 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-4531 (Electronic) Linking ISSN: 07487304 NLM ISO Abbreviation: J Biol Rhythms Subsets: MEDLINE","Publication_Type":"Guideline; Journal Article","Published_Date":"2017-10-01","Source":"Journal of biological rhythms [J Biol Rhythms] 2017 Oct; Vol. 32 (5), pp. 380-393. Date of Electronic Publication: 2017 Nov 03.","Languages":"English","Electronic_ISSN":"1552-4531","MeSH_Terms":"Genome* , Genomics*\/statistics & numerical data, Circadian Rhythm\/*genetics , Statistics as Topic\/*methods, Biostatistics ; Computational Biology\/methods ; Humans ; Metabolomics ; Proteomics ; Software ; Systems Biology","Subjects":"Biostatistics, Computational Biology methods, Humans, Metabolomics, Proteomics, Software, Systems Biology, Circadian Rhythm genetics, Genome, Genomics statistics & numerical data, Statistics as Topic methods","Title_Abbreviations":"Journal of biological rhythms","Volume":"32"},"header":{"DbId":"mdl","DbLabel":"MEDLINE Ultimate","An":"29098954","RelevancyScore":"748","PubType":"Academic Journal","PubTypeId":"academicJournal","PreciseRelevancyScore":"748.331665039063"},"plink":"https:\/\/search.ebscohost.com\/login.aspx?direct=true&site=eds-live&db=mdl&AN=29098954&authtype=shib&custid=ns346513&group=main&profile=eds","upload_link":"https:\/\/dx.doi.org\/doi:10.1177\/0748730417728663"}
["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, Pt\u00e1\u010dek 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","Publisher: Sage Publishers Country of Publication: United States NLM ID: 8700115 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-4531 (Electronic) Linking ISSN: 07487304 NLM ISO Abbreviation: J Biol Rhythms Subsets: MEDLINE","Guideline; Journal Article","2017-10-01","Journal of biological rhythms [J Biol Rhythms] 2017 Oct; Vol. 32 (5), pp. 380-393. Date of Electronic Publication: 2017 Nov 03.","English","1552-4531","Genome* , Genomics*\/statistics & numerical data, Circadian Rhythm\/*genetics , Statistics as Topic\/*methods, Biostatistics ; Computational Biology\/methods ; Humans ; Metabolomics ; Proteomics ; Software ; Systems Biology","Biostatistics, Computational Biology methods, Humans, Metabolomics, Proteomics, Software, Systems Biology, Circadian Rhythm genetics, Genome, Genomics statistics & numerical data, Statistics as Topic methods","Journal of biological rhythms","32"]
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.
