The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Detecting Rhythms in Time Series with RAIN
Open accessAbstract
A fundamental problem in research on biological rhythms is that of detecting and assessing the significance of rhythms in large sets of data. Classic methods based on Fourier theory are often hampered by the complex and unpredictable characteristics of experimental and biological noise. Robust nonparametric methods are available but are limited to specific wave forms. We present RAIN, a robust nonparametric method for the detection of rhythms of prespecified periods in biological data that can detect arbitrary wave forms. When applied to measurements of the circadian transcriptome and proteome of mouse liver, the sets of transcripts and proteins with rhythmic abundances were significantly expanded due to the increased detection power, when we controlled for false discovery. Validation against independent data confirmed the quality of these results. The large expansion of the circadian mouse liver transcriptomes and proteomes reflected the prevalence of nonsymmetric wave forms and led to new conclusions about function. RAIN was implemented as a freely available software package for R/Bioconductor and is presently also available as a web interface.
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Circadian alignment of early onset caloric restriction promotes longevity in male C57BL/6J mice
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AI-based structure prediction empowers integrative structural analysis of human nuclear pores
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A cell-autonomous mammalian 12 hr clock coordinates metabolic and stress rhythms
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Metadata
- Authors
- Thaben, P. F., & Westermark, P. O.
- Venue
- Journal of Biological Rhythms
- Publisher
- SAGE Publications
- Published
- 2014-10-17
- Volume
- 29
- Issue
- 6
- Pages
- 391-400
- ISSN
- 0748-7304 (ISSN-L)
- 1552-4531
- OpenAlex
- W2156003764
- Cited by
- 706
- References
- 20
- FWCI
- 4.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
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