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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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Updated: Jun 7, 2025

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

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Published on: November 11, 2016

A Bayesian Framework for Genome-wide Circadian Rhythmicity Biomarker Detection.

Haocheng Ding, Lingsong Meng, Yutao Zhang

Biorxiv : the Preprint Server for Biology

|November 18, 2024

Summary

This summary is machine-generated.

This study introduces BayesCircRhy, a novel Bayesian framework for detecting circadian rhythms genome-wide. It accurately identifies circadian genes, improving upon existing methods for analyzing complex biological data.

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Last Updated: Jun 7, 2025

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Area of Science:

Genomics
Chronobiology
Statistical Bioinformatics

Background:

Circadian rhythms, governed by gene feedback loops, impact health.
Omics data analysis requires precise methods for circadian biomarker detection.

Purpose of the Study:

Develop a novel Bayesian framework for genome-wide circadian rhythm detection.
Incorporate prior biological knowledge and control for multiple testing.

Main Methods:

Utilized a Bayesian hierarchical model.
Employed reverse jump Markov chain Monte Carlo (rjMCMC) for model selection.
Developed the BayesCircRhy method and R package 'BayesianCircadian'.

Main Results:

BayesCircRhy demonstrated superior false discovery rate control and robustness.
The method outperformed existing approaches in simulations.
Successfully identified known and novel circadian genes in human and mouse RNA-Seq data.

Conclusions:

The novel Bayesian framework provides accurate genome-wide detection of circadian rhythms.
BayesCircRhy offers improved performance and reliability for circadian gene identification.
The publicly available R package facilitates broader application in chronobiology research.