Summary: Detecting periodicity in large scale data remains a challenge. While efforts have been made to identify best of breed algorithms, relatively little research has gone into integrating these methods in a generalizable method. Here, we present MetaCycle, an R package that incorporates ARSER, JTK_CYCLE and Lomb-Scargle to conveniently evaluate periodicity in time-series data. MetaCycle has two functions, meta2d and meta3d, designed to analyze two-dimensional and three-dimensional time-series datasets, respectively. Meta2d implements N-version programming concepts using a suite of algorithms and integrating their results. Availability and implementation: MetaCycle package is available on the CRAN repository (https:// cran.r-project

The discovery that half of the mammalian protein-coding genome is regulated by the circadian clock has clear implications for medicine. Recent studies demonstrated that the circadian clock influences therapeutic outcomes in human heart disease and cancer. However, biological time is rarely given clinical consideration. A key barrier is the absence of information on tissue-specific molecular rhythms in the human body. We have applied the cyclic ordering by periodic structure (CYCLOPS) algorithm, designed to reconstruct sample temporal order in the absence of time-of-day information, to the gene expression collection of 13 tissues from 632 human donors. We identified rhythms in gene expression across the body; nearly half of protein-coding genes were shown to be cycling in at least 1 of the 13 tissues analyzed. One thousand of these cycling genes encode proteins that either transport or metabolize drugs or are themselves drug targets. These results provide a useful resource for studying the role of circadian rhythms in medicine and support the idea that biological time might play a role in determining drug response.

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.

Summary The intricate connection between the circadian clock and metabolism remains poorly understood. We used high temporal resolution metabolite profiling to explore clock regulation of mouse liver and cell autonomous metabolism. In liver, ~50% of metabolites were circadian, with enrichment of nucleotide, amino acid, and methylation pathways. In U2 OS cells, 28% were circadian, including amino acids and NAD biosynthesis metabolites. Eighteen metabolites oscillated in both systems and a subset of these in primary hepatocytes. These 18 metabolites were enriched in methylation and amino acid pathways. To assess clock-dependence of these rhythms, we used genetic perturbation. BMAL1 knockdown diminished metabolite rhythms, while CRY1 or CRY2 perturbation generally shortened/lengthened rhythms, respectively. Surprisingly, CRY1 knockdown induced 8 h rhythms in amino acid, methylation, and vitamin metabolites, decoupling metabolite from transcriptional rhythms, with potential impact on nutrient sensing in vivo. These results provide the first comprehensive views of circadian liver and cell autonomous metabolism.