Circadian rhythms are fundamental to biology and medicine and today these can be studied at the molecular level in high-throughput fashion using various omic technologies. We briefly present two resources for the study of circadian omic (e.g. transcriptomic, metabolomic, proteomic) time series. First, BIO_CYCLE is a deep-learning-based program and web server that can analyze omic time series and statistically assess their periodic nature and, when periodic, accurately infer the corresponding period, amplitude, and phase. Second, CircadiOmics is the larges annotated repository of circadian omic time series, containing over 260 experiments and 90 million individual measurements, across multiple organs and tissues, and across 9 different species. In combination, these tools enable powerful bioinformatics and systems biology analyses. The are currently being deployed in a host of different projects where they are enabling significant discoveries: both tools are publicly available over the web at: http://circadiomics.ics.uci.edu/ .
Keywords: Amplitude; Bioinformatics; Circadian; Omic; Period; Phase; Rhythms; Transcriptomic.
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