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. 2018 Jul 2;46(W1):W157-W162.
doi: 10.1093/nar/gky441.

CircadiOmics: circadian omic web portal

Nicholas Ceglia  1   2 , Yu Liu  1   2 , Siwei Chen  1   2 , Forest Agostinelli  1   2 , Kristin Eckel-Mahan  3 , Paolo Sassone-Corsi  2   4   5 , Pierre Baldi  1   2   4   5
Affiliations

CircadiOmics: circadian omic web portal

Nicholas Ceglia et al. Nucleic Acids Res. .

Abstract

Circadian rhythms play a fundamental role at all levels of biological organization. Understanding the mechanisms and implications of circadian oscillations continues to be the focus of intense research. However, there has been no comprehensive and integrated way for accessing and mining all circadian omic datasets. The latest release of CircadiOmics (http://circadiomics.ics.uci.edu) fills this gap for providing the most comprehensive web server for studying circadian data. The newly updated version contains high-throughput 227 omic datasets corresponding to over 74 million measurements sampled over 24 h cycles. Users can visualize and compare oscillatory trajectories across species, tissues and conditions. Periodicity statistics (e.g. period, amplitude, phase, P-value, q-value etc.) obtained from BIO_CYCLE and other methods are provided for all samples in the repository and can easily be downloaded in the form of publication-ready figures and tables. New features and substantial improvements in performance and data volume make CircadiOmics a powerful web portal for integrated analysis of circadian omic data.

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Figures

Figure 1.
Figure 1.
Dataset collection by species, tissues, experimental conditions and omic categories.
Figure 2.
Figure 2.
Three-tier Model-View-Controller architecture of the CircadiOmics web portal. Intelligent data discovery supplies candidate datasets for inclusion in the repository using a machine learning filter applied to key word features derived from web crawling published abstracts. BIO_CYCLE results are obtained and stored for all datasets. The user interface sends requests and displays results from the web server allowing for interactive hypothesis generation and scientific discovery.
Figure 3.
Figure 3.
Visualization of queries for ARNTL, PER1 and CRY1 in a control mouse dataset. Any number of queries, across any number of datasets, can be displayed simultaneously.
Figure 4.
Figure 4.
Selected examples of the impact of CircadiOmics. (A) CircadiOmics was used to link a multitude of circadian metabolites with functionally related circadian transcripts. Figure taken from Figure 5A of (17). (B) CircadiOmics was used to discover reprogrammed circadian transcripts and metabolites related to inflammatory and energy pathways. Figure taken from Figures 2E, 4B and 5D of (27). (C) Exogenous MYOD1, during MEF myogenic reprogramming, entrains oscillation in MYOG and related targets in absence of oscillation of the core clock (https://www.biorxiv.org/content/early/2017/06/18/151555). (D) Bar heights show the ordered number of oscillating protein coding transcripts with a P ≤ 0.05 in each mouse transcriptomic experiment in the repository. The trend is the cumulative union of oscillating transcripts. Over 93% of possible protein coding transcripts are found to oscillate in at least one tissue or condition across all mouse datasets.
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