TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Times-Series Data in CircadianBiology Research

BioRxiv : the Preprint Server for Biology
E. Ness-CohnRosemary Braun


The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues, coordinating physiological processes. The effect of this rhythm on health has generated increasing interest in discovering genes under circadian control by searching for periodic patterns in transcriptomic time-series experiments. While algorithms for detecting cycling transcripts have advanced, there remains little guidance quantifying the effect of experimental design and analysis choices on cycling detection accuracy. We present TimeTrial (https://github.com/nesscoder/TimeTrial): a user-friendly benchmarking framework using both real and synthetic data to investigate cycle detection algorithms' performance and improve circadian experimental design. Results show that the optimal choice of analysis method depends on the sampling scheme, noise level, and shape of the waveform of interest; and provides guidance on the impact of sampling frequency and duration on cycling detection accuracy. By supplying a tool to vary and optimize these considerations, TimeTrial will enhance circadian transcriptomics studies.

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