ArticleBioinformatics (Oxford, England)2009
Bayesian detection of non-sinusoidal periodic patterns in circadian expression data.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling.Cancers · 2020Review
- Methods detecting rhythmic gene expression are biologically relevant only for strong signal.PLoS computational biology · 2020Article
- MICOP: Maximal information coefficient-based oscillation prediction to detect biological rhythms in proteomics data.BMC bioinformatics · 2018Article
- Finding Clocks in Genes: A Bayesian Approach to Estimate Periodicity.BioMed research international · 2016Article
- Circadian systems biology: When time matters.Computational and structural biotechnology journal · 2015Review
- The Antarctic krill Euphausia superba shows diurnal cycles of transcription under natural conditions.PloS one · 2013Article
- Microarray analysis of natural socially regulated plasticity in circadian rhythms of honey bees.Journal of biological rhythms · 2012Article
- Article
- Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation.Bioinformatics (Oxford, England) · 2010Article
- Circadian control of global gene expression patterns.Annual review of genetics · 2010Review
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Authors and funding
5 authors.
Funding
Abstract
motivationCyclical biological processes such as cell division and circadian regulation produce coordinated periodic expression of thousands of genes. Identification of such genes and their expression patterns is a crucial step in discovering underlying regulatory mechanisms. Existing computational methods are biased toward discovering genes that follow sine-wave patterns.
resultsWe present an analysis of variance (ANOVA) periodicity detector and its Bayesian extension that can be used to discover periodic transcripts of arbitrary shapes from replicated gene expression profiles. The models are applicable when the profiles are collected at comparable time points for at least two cycles. We provide an empirical Bayes procedure for estimating parameters of the prior distributions and derive closed-form expressions for the posterior probability of periodicity, enabling efficient computation. The model is applied to two datasets profiling circadian regulation in murine liver and skeletal muscle, revealing a substantial number of previously undetected non-sinusoidal periodic transcripts in each. We also apply quantitative real-time PCR to several highly ranked non-sinusoidal transcripts in liver tissue found by the model, providing independent evidence of circadian regulation of these genes. AVAILABILITY: Matlab software for estimating prior distributions and performing inference is available for download from http://www.datalab.uci.edu/resources/periodicity/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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