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Circadian Rhythms and Gene Regulation

Circadian Rhythms and Gene Regulation

The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent...

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A novel Bayesian hierarchical model for detecting differential circadian pattern in transcriptomic applications.

Yutao Zhang¹, Haocheng Ding², Zhiguang Huo¹

¹Department of Biostatistics, University of Florida, Gainesville, FL, 32603, United States.

Briefings in Bioinformatics

|April 15, 2025

Summary

BayesDCirc, a new Bayesian model, identifies differential circadian (DC) patterns by analyzing amplitude, phase, and MESOR. It improves disease biomarker discovery by incorporating external knowledge and outperforming existing methods.

Keywords:

Bayesian method differential circadian pattern transcriptome

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Last Updated: May 13, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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Published on: December 10, 2012

A Computational Method to Quantify Fly Circadian Activity

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Published on: October 28, 2017

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Published on: November 11, 2016

Area of Science:

Chronobiology
Systems Biology
Statistical Bioinformatics

Background:

Circadian rhythms regulate physiological processes; disruptions are linked to diseases.
Identifying molecular biomarkers with differential circadian (DC) patterns aids disease prevention and treatment.
Existing DC analysis methods struggle with amplitude, phase, and MESOR, and lack external knowledge integration.

Purpose of the Study:

To introduce BayesDCirc, a novel Bayesian hierarchical model for detecting differential circadian patterns.
To address limitations of existing methods by enabling MESOR difference testing and external knowledge incorporation.
To improve the accuracy and robustness of differential circadian biomarker identification.

Main Methods:

Developed a Bayesian hierarchical model, BayesDCirc, for two-group differential circadian pattern analysis.
Incorporated the ability to test for differences in MESOR (Midpoint Estimated Sleep and Activity Rhythm) alongside amplitude and phase.
Enabled the integration of external differential circadian information from related biological contexts.

Main Results:

BayesDCirc demonstrated superior False Discovery Rate (FDR) control compared to existing methods.
The model showed improved performance when leveraging external knowledge of DC genes.
Applied to real datasets, BayesDCirc successfully identified key circadian genes, especially with integrated external data.

Conclusions:

BayesDCirc offers a flexible and powerful framework for differential circadian pattern analysis.
The model's ability to test MESOR differences and incorporate external knowledge enhances biomarker discovery.
The BayesDCirc R package is available for public use, facilitating further research in chronobiology and disease biomarker identification.