MOTIVATION: Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken. RESULTS: We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO_CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO_CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO_CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO_CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO_CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO_CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp. AVAILABILITY AND IMPLEMENTATION: All data and software are publicly available on the CircadiOmics web portal: circadiomics.igb.uci.edu/ CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2016) 32(12), i8–i17 doi: 10.1093/bioinformatics/btw243 The authors wish to correct the following errors in the above article: In Section 2.1.2, Arabdiposis should read Arabidopsis, in Sections 2.1.2 and 4.4.1, Cyr1 should read Cry1, and in Section 3.1.5, V ;> ;V(i) should read V(i) ;> ;V. The authors apologize for these errors.

MOTIVATION: Circadian oscillations have been observed in animals, plants, fungi and cyanobacteria and play a fundamental role in coordinating the homeostasis and behavior of biological systems. Genetically encoded molecular clocks found in nearly every cell, based on negative transcription/translation feedback loops and involving only a dozen genes, play a central role in maintaining these oscillations. However, high-throughput gene expression experiments reveal that in a typical tissue, a much larger fraction ([Formula: see text]) of all transcripts oscillate with the day-night cycle and the oscillating species vary with tissue type suggesting that perhaps a much larger fraction of all transcripts, and perhaps also other molecular species, may bear the potential for circadian oscillations. RESULTS: To better quantify the pervasiveness and plasticity of circadian oscillations, we conduct the first large-scale analysis aggregating the results of 18 circadian transcriptomic studies and 10 circadian metabolomic studies conducted in mice using different tissues and under different conditions. We find that over half of protein coding genes in the cell can produce transcripts that are circadian in at least one set of conditions and similarly for measured metabolites. Genetic or environmental perturbations can disrupt existing oscillations by changing their amplitudes and phases, suppressing them or giving rise to novel circadian oscillations. The oscillating species and their oscillations provide a characteristic signature of the physiological state of the corresponding cell/tissue. Molecular networks comprise many oscillator loops that have been sculpted by evolution over two trillion day-night cycles to have intrinsic circadian frequency. These oscillating loops are coupled by shared nodes in a large network of coupled circadian oscillators where the clock genes form a major hub. Cells can program and re-program their circadian repertoire through epigenetic and other mechanisms. AVAILABILITY AND IMPLEMENTATION: High-resolution and tissue/condition specific circadian data and networks available at http://circadiomics.igb.uci.edu. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.