Research tools

The SRBR Education Committee has compiled a list of resources that allow students and researchers to seek answers to new questions related to biological rhythms. This includes links to open access datasets that may support student-directed research as well as data analysis tools that may help in the analysis of these data sets.

Open Access data sets

These data sets were found by searching on Google data set search, Open science framework, Datadryad.org. If you know of any others, please email us at info@srbr.org.

The Costs of Sleeping In

Effects of Light at Night on Reproduction

Natural Light Exposure, Sleep and Depression

Social jetlag, food consumption, and meal times

Effects of circadian clock disruption on SCN and peripheral tissues

Effects of feeding rhythms on mouse liver

Open data on circadian studies of plants

BioDare2

CircaDB – Circadian Gene Expression Data Base

 

Analysis tools

This list contains links to publicly available tools that can be used for the analysis of biological time-series data. If you know of any others, please email us at info@srbr.org.

  • The PAICE Suite: A suite of packages that allow for the identification and analysis of rhythms with changing amplitudes:
    • ECHO, an easy to use, high-throughput, application that identifies rhythmic elements, including those with changing amplitudes (e.g. damping) in multiple data types at omics scale, while also estimating their period length and phase, developed by De los Santos et al. (2020).
    • ENCORE, an easy to use, high-throughput, application that allows users to navigate gene ontologic categories for circadian rhythms using ECHO output, developed by De los Santos et al. (2019).
    • MOSAIC, an easy to use, high-throughput, application that allows users to find and visualize circadian and non-circadian trends by comparing multi-omics time course data using model selection and joint modeling, developed by De los Santos et al. (2020).
  • CATkit, this website documents core techniques for analyzing circadian rhythms and describes the CAT analysis toolkit that can be used to perform these analyses. The CAT package was developed by Gierke and Cornelissen  (2016).
  • JTK_Cycle, an algorithm to identify rhythmic components in large, genome-scale data sets and estimate their period length, phase, and amplitude, developed by Hughes et al. (2010).
  • SW1PerS is cycling detection method that uses sliding window persistence homology to identify cycling genes, developed by Perea et al. (2015).
  • TimeTrial is a user friendly R shiny application for optimizing the design and analysis of transcriptomic time-series experiments, developed by Ness-Cohn et al. (2020).
  • BooteJTK is cycling detection method based on a bootstrapped empirical bayes procedure to identify cycling genes, developed by Hutchison et al. (2018).
  • ARSER (PythonR) is cycling detection method that uses autoregressive spectral estimation to identify cycling genes, developed by Yang and Su (2010).
  • CIRCADA,a pair of apps that provide educational tools for learning about circadian data visualization and analysis using the discrete wavelet transform, sine-fitting, the Lomb-Scargle periodogram, autocorrelation, and maximum entropy spectral analysis, as described in Cenek et al. (2020).
  • per2py is a python-based high-throughput circadian Per2 bioluminescence analysis toolkit. per2py was developed for automated analysis of bioluminescence rhythms from all individual neurons within SCN slices in Shan et al. 2020. Instructions for use, a demonstration dataset, and a detailed description of the analytical steps are provided.

JBR Perspectives on Data Analysis

Elan Ness-Cohn, et al. TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Time-Series Data in Circadian Biology Research Journal of Biological Rhythms, First Published 2 Jul 2020.

Evie van der Spoel, et al. Comparing Methods for Measurement Error Detection in Serial 24-h Hormonal Data Journal of Biological Rhythms, vol. 34, 4: pp. 347-363. , First Published June 12, 2019

Jacob J. Hughey, et al. LimoRhyde: A Flexible Approach for Differential Analysis of Rhythmic Transcriptome Data Journal of Biological Rhythms, vol. 34, 1: pp. 5-18. , First Published November 25, 2018.

Alan L. Hutchison, et al. Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data Journal of Biological Rhythms, vol. 33, 4: pp. 339-349. , First Published August 13, 2018.

Michael C. Tackenberg, et al. Tau-independent Phase Analysis: A Novel Method for Accurately Determining Phase Shifts Journal of Biological Rhythms, vol. 33, 3: pp. 223-232. , First Published April 11, 2018.

Michael E. Hughes, et al. Guidelines for Genome-Scale Analysis of Biological Rhythms Journal of Biological Rhythms, vol. 32, 5: pp. 380-393. , First Published November 3, 2017.

Tanya L. Leise Analysis of Nonstationary Time Series for Biological Rhythms Research Journal of Biological Rhythms, vol. 32, 3: pp. 187-194. , First Published June 1, 2017.

Matt T. Bianchi, et al. Statistics for Sleep and Biological Rhythms Research: From Distributions and Displays to Correlation and Causation Journal of Biological Rhythms, vol. 32, 1: pp. 7-17. , First Published October 24, 2016.

Elizabeth B. Klerman, et al. Statistics for Sleep and Biological Rhythms Research: Longitudinal Analysis of Biological Rhythms Data Journal of Biological Rhythms, vol. 32, 1: pp. 18-25. , First Published October 24, 2016.