Related Experiment Video
Updated: Sep 29, 2026

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
Published on: October 21, 2022
CircadiOmics: bioinformatics platform for circadian rhythms
Kousha Changizi Ashtiani1,2, Pierre Baldi1,2
1Artificial Intelligence in Science Institute, University of California, Irvine, CA 92697, United States.
Motivation:
Circadian rhythms are fundamental to biology: they occur at the molecular level across all species and are critical to homeostasis and biological functions. They are also essential for health and medicine, as disruption of circadian regulation has been linked to numerous health problems, and many drug targets exhibit circadian oscillations, highlighting the importance of molecular-level circadian insight for precision medicine. Despite the rapid growth of circadian omic time series datasets, informatics tools for processing and analyzing this growing wealth of data remain limited.
Results:
CircadiOmics provides, to the best of our knowledge, the largest repository and analytical platform for circadian omic data, consisting primarily of transcriptomic data, along with metabolomic, proteomic, and acetylomic datasets. CircadiOmics contains 704 omic datasets across 18 species, 30 tissue/organ categories, and 21 experiment types, including genetic manipulations, dietary interventions, and disease-associated conditions. By leveraging this repository, users can access an environment for comparing and visualizing molecular-level circadian oscillations. Periodicity statistics computed using BIO_CYCLE, including period, amplitude, phase, p-values, and q-values, are available as interactive plots and downloadable tables. This updated version of CircadiOmics introduces new features and substantially expanded data coverage, establishing it as a comprehensive resource for the exploration and analysis of circadian omic data.
Availability And Implementation:
CircadiOmics is freely accessible at: https://circadiomics.igb.uci.edu. The source code for BIO_CYCLE is available on GitHub: https://github.com/BaldiLab/BIO_CYCLE and archived on Zenodo: https://doi.org/10.5281/zenodo.22108329.
