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 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.

Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock

“…The circadian behavior in the fecal microbiota of wild-type mice was assessed by sampling fecal pellets from each mouse every 4 h for 48 h. Circadian rhythmicity of the fecal microbiota was tested by the JTK_CYCLE algorithm (21). The fecal bacterial load, as measured by the total 16S rRNA gene copy numbers, oscillated diurnally during the light-dark cycle both in the group as a whole (Fig.…”

Coordination of the transcriptome and metabolome by the circadian clock

“…Of 309 named metabolites (detectable compounds of known identity), 100 showed a time main effect and 73 showed a genotype main effect. A nonparametric algorithm, JTK_CYCLE, was used to detect metabolites that displayed 24-h rhythmicity (Datasets S2, S3, and S4) (30). By JTK_CYCLE, 60 metabolites were detected as oscillating in a diurnal fashion in WT livers (P < 0.05).…”

“…Mann-Whitney u tests were used to determine significance between two independent samples. For analysis of rhythmic metabolites, the nonparametric test, JTK_CYCLE, was used incorporating a window of 20-28 h for the determination of circadian periodicity (30). Venn diagrams were made with Venny (http://bioinfogp.…”

Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis

“…The time of expression change for a cluster was defined as the point at which the absolute average value for the genes in the cluster was $0.5. Genes that exhibited rhythmic expression were identified using JTK_CYCLE and default parameters (Hughes et al, 2010). Up-or downregulation for genes differentially expressed in response to B. cinerea infection was determined by calculating the difference between infected and mock-inoculated expression values.…”