JTK_CYCLE: an efficient non-parametric algorithm for detecting rhythmic components in genome-scale datasets

摘要

Circadian rhythms are oscillations of physiology, behavior, and metabolism that have period lengths of 24 hours. In several model organisms and man, 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. Here we present a new algorithm, JTK_CYCLE, designed to efficiently identify and characterize cycling variables in large datasets. Compared to COSOPT and the Fisher’s G test, two commonly used methods for detecting cycling transcripts, JTK_CYCLE distinguishes between rhythmic and non-rhythmic transcripts more reliably and efficiently. We also show that 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, it is several orders of magnitude faster than COSOPT, making it ideal for large scale data sets. We used JTK_CYCLE to analyze legacy data sets including NIH3T3 cells, which have comparatively low amplitude. 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 datasets.