Differential rhythmicity: detecting altered rhythmicity in biological data.

MOTIVATION: Biological rhythms, such as rhythms in gene expression controlled by the cell cycle or the circadian clock, are important in cell physiology. A common type of experiment compares rhythmicity in tissues or cells either kept under different conditions or having different genotypes. Such investigations provide insights into underlying mechanisms as well as functions of rhythms. RESULTS: We present and benchmark a set of statistical and computational methods for this type of analysis, here termed differential rhythmicity analysis. The methods detect alterations in rhythm amplitude, phase and signal to noise ratio in one set of measurements compared to another. Using these methods, we compared circadian rhythms in liver mRNA expression in mice held under two different lighting conditions: constant darkness and light-dark cycles, respectively. This analysis revealed widespread and reproducible amplitude increases in mice kept in light-dark cycles. Further analysis of the subset of differentially rhythmic transcripts implied the immune system in mediating ambient light-dark cycles to rhythmic transcriptional activities. The methods are suitable for genome- or proteome-wide studies, and provide rigorous P values against well-defined null hypotheses. AVAILABILITY AND IMPLEMENTATION: The methods were implemented as the accompanying R software package DODR, available on CRAN. CONTACT: pal-olof.westermark@charite.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rhythmic degradation explains and unifies circadian transcriptome and proteome data.

The rich mammalian cellular circadian output affects thousands of genes in many cell types and has been the subject of genome-wide transcriptome and proteome studies. The results have been enigmatic because transcript peak abundances do not always follow the peaks of gene-expression activity in time. We posited that circadian degradation of mRNAs and proteins plays a pivotal role in setting their peak times. To establish guiding principles, we derived a theoretical framework that fully describes the amplitudes and phases of biomolecules with circadian half-lives. We were able to explain the circadian transcriptome and proteome studies with the same unifying theory, including cases in which transcripts or proteins appeared before the onset of increased production rates. Furthermore, we estimate that 30% of the circadian transcripts in mouse liver and Drosophila heads are affected by rhythmic posttranscriptional regulation.

Detecting rhythms in time series with RAIN.

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.