JTK_CYCLE: An Efficient Nonparametric Algorithm for Detecting Rhythmic Components in Genome-Scale Data Sets

Abstract

Circadian rhythms are oscillations of physiology, behavior, and metabolism that have period lengths near 24 hours. In several model organisms and humans, circadian clock genes have been characterized and found to be transcription factors. Because of this, researchers have used microarrays to characterize global regulation of gene expression and algorithmic approaches to detect cycling. This article presents a new algorithm, JTK_CYCLE, designed to efficiently identify and characterize cycling variables in large data sets. Compared with COSOPT and the Fisher’s G test, two commonly used methods for detecting cycling transcripts, JTK_CYCLE distinguishes between rhythmic and nonrhythmic transcripts more reliably and efficiently. JTK_CYCLE’s increased resistance to outliers results in considerably greater sensitivity and specificity. Moreover, JTK_CYCLE accurately measures the period, phase, and amplitude of cycling transcripts, facilitating downstream analyses. Finally, JTK_CYCLE is several orders of magnitude faster than COSOPT, making it ideal for large-scale data sets. JTK_CYCLE was used to analyze legacy data sets including NIH3T3 cells, which have comparatively low amplitude oscillations. JTK_CYCLE’s improved power led to the identification of a novel cluster of RNA-interacting genes whose abundance is under clear circadian regulation. These data suggest that JTK_CYCLE is an ideal tool for identifying and characterizing oscillations in genome-scale data sets.