JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets.
JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets.
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DOI：
10.1177/0748730410379711
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发表时间：
2010-10
影响因子：
3.5
通讯作者：
Kornacker K
中科院分区：
文献类型：
--
作者：
Hughes ME;Hogenesch JB;Kornacker K
昼夜节律是具有24小时周期长度的生理、行为和新陈代谢的振荡。在一些模式生物和人类中，生物钟基因已经被表征并被发现是转录因子。正因为如此，研究人员使用微阵列来表征基因表达的全球调控，并使用算法方法来检测循环。在这里，我们提出了一个新的算法，JTK_Cycle，旨在有效地识别和表征大数据集中的循环变量。与COSOPT和Fisher‘s G检验这两种常用的检测周期转录的方法相比，JTK_Cycle更可靠、更有效地区分节律性和非节律性转录。我们还表明，JTK_Cycle对异常值的抵抗力增强，导致更高的灵敏度和特异度。此外，JTK_Cycle精确测量循环转录的周期、相位和幅度，便于下游分析。最后，它比COSOPT快几个数量级，这使它成为大型数据集的理想选择。我们使用JTK_Cycle分析了包括NIH3T3细胞在内的遗留数据集，这些数据集具有相对较低的幅度。JTK_Cycle功率的提高导致了一组新的RNA相互作用基因的鉴定，其丰度受到明显的昼夜节律调节。这些数据表明，JTK_Cycle是识别和表征基因组规模数据集中振荡的理想工具。
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.
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