Bayesian detection of non-sinusoidal periodic patterns in circadian expression data.
BACKGROUND
Cyclical 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.


RESULTS
We 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.


BACKGROUND
Matlab software for estimating prior distributions and performing inference is available for download from http://www.datalab.uci.edu/resources/periodicity/.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Chudova
Darya
D
Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com
Ihler
Alexander
A
Lin
Kevin K
KK
Andersen
Bogi
B
Smyth
Padhraic
P
D001499:Bayes Theorem; D002453:Cell Cycle / Q000502:physiology; D018797:Cell Cycle Proteins / Q000737:chemistry / Q000378:metabolism; D002940:Circadian Rhythm / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*
D018797:Cell Cycle Proteins