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. 2019 Nov 8;36(6):1952-1954.
doi: 10.1093/bioinformatics/btz834. Online ahead of print.

DiscoRhythm: an easy-to-use web application and R package for discovering rhythmicity

Matthew Carlucci  1 , Algimantas Kriščiūnas  1   2 , Haohan Li  1 , Povilas Gibas  1   2 , Karolis Koncevičius  1   2 , Art Petronis  1   2 , Gabriel Oh  1
Affiliations

DiscoRhythm: an easy-to-use web application and R package for discovering rhythmicity

Matthew Carlucci et al. Bioinformatics. .

Erratum in

Abstract

Motivation: Biological rhythmicity is fundamental to almost all organisms on Earth and plays a key role in health and disease. Identification of oscillating signals could lead to novel biological insights, yet its investigation is impeded by the extensive computational and statistical knowledge required to perform such analysis.

Results: To address this issue, we present DiscoRhythm (Discovering Rhythmicity), a user-friendly application for characterizing rhythmicity in temporal biological data. DiscoRhythm is available as a web application or an R/Bioconductor package for estimating phase, amplitude, and statistical significance using four popular approaches to rhythm detection (Cosinor, JTK Cycle, ARSER, and Lomb-Scargle). We optimized these algorithms for speed, improving their execution times up to 30-fold to enable rapid analysis of -omic-scale datasets in real-time. Informative visualizations, interactive modules for quality control, dimensionality reduction, periodicity profiling, and incorporation of experimental replicates make DiscoRhythm a thorough toolkit for analyzing rhythmicity.

Availability and implementation: The DiscoRhythm R package is available on Bioconductor (https://bioconductor.org/packages/DiscoRhythm), with source code available on GitHub (https://github.com/matthewcarlucci/DiscoRhythm) under a GPL-3 license. The web application is securely deployed over HTTPS (https://disco.camh.ca) and is freely available for use worldwide. Local instances of the DiscoRhythm web application can be created using the R package or by deploying the publicly available Docker container (https://hub.docker.com/r/mcarlucci/discorhythm).

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Overview of the analysis procedures performed by DiscoRhythm. The illustration shows the step-wise operations being performed on the input circadian data matrix. All procedures are performed on a matrix with the biological features represented by rows and temporal samples by columns. Columns with the same color (e.g. purple and yellow) represent technical replicates, while stripped boxes indicate samples or features removed for downstream analysis. Red and gray oscillating lines show significant and non-significant rhythms, respectively. hr, hours
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