Bayesian detection of non-sinusoidal periodic patterns in circadian expression data.
Bioinformatics
; 25(23): 3114-20, 2009 Dec 01.
Article
en En
| MEDLINE
| ID: mdl-19773336
MOTIVATION: 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. 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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Ritmo Circadiano
/
Biología Computacional
/
Perfilación de la Expresión Génica
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2009
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Reino Unido