<2> Computational Algorithms and Tools
last updated: June 25, 2016
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1. CircaDB: a database of circadian transcriptional profiles from time course expression experiments from mice and humans (Pizarro, et al., 2013).
2. SCNseq: an chronobiological resource of the mammalian SCN transcriptome over a 24-hour light / dark cycle by combining laser capture microdissection (LCM) and RNA-seq (Pembroke, et al., 2015).
3. Bioclock: a searchable repository of diel and circadian microarray expression data Anopheles gambiae and Aedes aegypti (Rund, et al., 2011; Rund, et al., 2013; Leming, et al., 2014).
4. CircadiOmics: a computational resource that provides a common repository for circadian cycle–related metabolic data with other omic data and produces high-resolution biological networks displaying, for instance, metabolites, enzymes, transcription factors and their interactions, and concentration changes over time throughout the circadian cycle. (Patel, et al., 2012).
5. Diurnal 2.0: a resource providing a graphical interface for mining and viewing diurnal and circadian microarray data for Arabidopsis thaliana, poplar, and rice (Mockler, et al., 2007).
6. EUCLIS: EU Clock Information System, integrates a database for circadian systems biology, containing modules for experimental data (Clock Experiments) and models (Clock Models) with a digital library (Clock KnowledgeBase) for the research community (Batista, et al., 2007).
7. dbCRY: a Web-based comparative genomics platform for genes encoding cryptochromes (Kim, et al., 2014).
<2> Computational Algorithms and Tools
1. JTK_Cycle: a tool for identifying and characterizing oscillations in genome-scale data sets (Hughes, et al., 2010).
2. MetaCycle: an integrated R package to evaluate periodicity in large scale data (Wu, et al., 2016).
3. ZeitZeiger: a method to predict a periodic variable (e.g. time of day) from a high-dimensional observation (Hughey, et al., 2016).
4. HAYSTACK (HAY): a model-based pattern-matching algorithm for identifying genes that are coexpressed and potentially coregulated (Mockler, et al., 2007).
5. Phaser: a web-based tool that identifies if a set of loci are overrepresented with a particular phase of expression under some diurnal or circadian time course (Mockler, et al., 2007).
6. Fisher’s G test (FIS): an approach to calculate the p-value(s) according to Fisher's exact g test for one or more time series. This test is useful to detect hidden periodicities of unknown frequency in a data set (Sofia, et al., 2003).
7. COSOPT (COS): a method for detection of periodic patterns in gene expression data (Straume, et al., 2004).
8. Lomb-Scargle periodograms: an approach for detection and quantification of periodic patterns in any unevenly spaced gene expression time-series data (Glynn, et al., 2006).
9. BioClock: a platform for analysis of biological clock behaviour in living organisms (Yang, et al., 2010; Yang, et al., 2011).
10. BIO_CYCLE and BIO_CLOCK: Deep learning approaches to detect Circadian Rhythm and predict Experiment Time from Gene Expression (Agostinelli, et al., 2016).