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

ClockProfile: Rhythmic gene expression control by circadian and feeding rhythms in Bmal1 and Cry1/Cry2 KO. Weger et al. (2021)

• 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).

• Rethomics, a package for high-throughput behavioral analysis in R that was developed by Geissmann et al. (2019).

• LimoRhyde, an R package for analyzing transcriptome data from circadian systems, developed by Singer and Hughey (2019).
  

• RhythmicAlly, a user-friendly application for the analysis of biological time-series data, developed by Abhilash and Sheeba (2019).

• MetaCycle, an R package for detecting rhythmic signals from large scale time-series data, developed by Wu et al. (2016).

• 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).

• RAIN, an R package to detect rhythms in time series, developed by Thaben and Westermark (2014)
• ActogramJ is a package based on ImageJ for analysis of chronobiological data, developed by Schmid, Helfrich-Foster, and Yoshii (2011)
• 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 (Python, R) 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 on curated experimental data  and synthetic data. 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.
• CircaCompare An approach based on non-linear cosinor regression that allows independent statistical testing of amplitude, phase and MESOR changes between conditions. Parsons et al. (2019)
• DryR: Differential RhythmicitY analysis in R is an R package that provides a model selection approach to assess differential rhythmicity and gene expression level of a time series of RNA-Seq or any other OMICS data with two and more conditions. Weger et al. (2021)
• compareRhythms A simple to use R package for differential rhythmicity analysis across two conditions using hypothesis or model selection for a variety of -omics data types. Pelikan et al. (2021)
• cosinoRmixedeffects A tool to perform cosinor analysis within the mixed-effects modeling framework. Hou et al. (2021)
• luox, a user-friendly web platform for calculating physiologically relevant quantities related to light and lighting.
• pyActigraphy, an open-source Python package for analyzing actigraphy data. Hammad et al. (2021)
• Nitecap, a web-based app to run analyses of high-throughput data to detect, quantify, and compare rhythms and circadian behavior, developed by Brooks et al. (2022).
• VANESSA, a web-based app to visualize and analyze circadian rhythms and sleep for data from Drosphila Activity Monitors, developed by Ghosh and Sheeba (2022).
• PHASE, an open-source MATLAB-based program for the analysis of Drosophila phase, activity, and sleep under entrainment. Persons et al. (2022).
• MODA, a MATLAB-based app aimed at deriving important properties about the dynamical system underlying time series data, including bivariate time series. Clemson et al. (2016).