Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework
Abstract Background The central biological clock governs numerous facets of mammalian physiology, including sleep, metabolism, and immune system regulation. Understanding gene regulatory relationships is crucial for unravelling the mechanisms that underlie various cellular biological processes. Whil...
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Published in | BMC bioinformatics Vol. 24; no. 1; pp. 1 - 362 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
BioMed Central
26.09.2023
BMC |
Subjects | |
Online Access | Get full text |
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Summary: | Abstract
Background
The central biological clock governs numerous facets of mammalian physiology, including sleep, metabolism, and immune system regulation. Understanding gene regulatory relationships is crucial for unravelling the mechanisms that underlie various cellular biological processes. While it is possible to infer circadian gene regulatory relationships from time-series gene expression data, relying solely on correlation-based inference may not provide sufficient information about causation. Moreover, gene expression data often have high dimensions but a limited number of observations, posing challenges in their analysis.
Methods
In this paper, we introduce a new hybrid framework, referred to as Circadian Gene Regulatory Framework (CGRF), to infer circadian gene regulatory relationships from gene expression data of rats. The framework addresses the challenges of high-dimensional data by combining the fuzzy C-means clustering algorithm with dynamic time warping distance. Through this approach, we efficiently identify the clusters of genes related to the target gene. To determine the significance of genes within a specific cluster, we employ the Wilcoxon signed-rank test. Subsequently, we use a dynamic vector autoregressive method to analyze the selected significant gene expression profiles and reveal directed causal regulatory relationships based on partial correlation.
Conclusion
The proposed CGRF framework offers a comprehensive and efficient solution for understanding circadian gene regulation. Circadian gene regulatory relationships are inferred from the gene expression data of rats based on the
Aanat
target gene. The results show that genes
Pde10a, Atp7b, Prok2, Per1, Rhobtb3
and
Dclk1
stand out, which have been known to be essential for the regulation of circadian activity. The potential relationships between genes
Tspan15, Eprs, Eml5
and
Fsbp
with a circadian rhythm need further experimental research. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1471-2105 1471-2105 |
DOI: | 10.1186/s12859-023-05458-y |