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A Computational Method to Quantify Fly Circadian Activity
13:05

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Published on: October 28, 2017

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

Darya Chudova1, Alexander Ihler, Kevin K Lin

  • 1Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com

Bioinformatics (Oxford, England)
|September 24, 2009
PubMed
Summary

This study introduces a new computational method to identify genes with cyclical expression patterns, including non-sinusoidal shapes. The novel approach enhances the discovery of periodic gene regulation in biological processes.

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Cyclical biological processes, like cell division and circadian rhythms, involve coordinated gene expression.
  • Identifying genes with periodic expression is key to understanding regulatory mechanisms.
  • Current computational methods often overlook genes with non-sinusoidal expression patterns.

Purpose of the Study:

  • To develop a computational method for discovering periodic transcripts with arbitrary shapes.
  • To overcome the limitations of existing methods biased towards sinusoidal patterns.

Main Methods:

  • Development of an analysis of variance (ANOVA) periodicity detector with a Bayesian extension.
  • Application of the models to replicated gene expression profiles from at least two cycles.
  • Empirical Bayes procedure for parameter estimation and derivation of closed-form expressions for posterior probability of periodicity.

Main Results:

  • The method successfully identified a substantial number of previously undetected non-sinusoidal periodic transcripts in murine liver and skeletal muscle circadian regulation datasets.
  • Quantitative real-time PCR validated the circadian regulation of several identified non-sinusoidal transcripts in liver tissue.

Conclusions:

  • The developed ANOVA periodicity detector and its Bayesian extension are effective in discovering periodic transcripts of arbitrary shapes.
  • This approach significantly advances the identification of genes involved in cyclical biological processes, particularly those with complex expression patterns.