Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs...

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Gene expression data analysis involves preprocessing and downstream tasks like differential expression identification, co-expression clustering, subtype classification, and genetic regulatory network detection.
• Temporal dynamic gene expression data exhibits inherent correlations between adjacent time points, unlike static data where samples are independent.
• Understanding these temporal dependencies is crucial for accurate biological interpretation.

Purpose of the Study:

• To demonstrate how Hidden Markov Models (HMMs) and hierarchical Bayesian modeling can capture temporal dependencies in time series gene expression profiles.
• To focus on the identification of differential expression within these dynamic profiles.
• To show how identified differential expression genes and transcript variants can be used for genetic regulatory network detection.

Main Methods:

• Utilizing Hidden Markov Models (HMMs) to model the temporal dynamics of gene expression.
• Employing hierarchical Bayesian modeling to capture time dependencies in gene expression data.
• Focusing on the identification of differentially expressed genes in time series data.

Main Results:

• The study demonstrates the capability of HMMs and hierarchical Bayesian methods in capturing horizontal time dependency structures.
• These methods facilitate the identification of differential expression in temporal gene expression profiles.
• The identified differentially expressed genes and transcript variants serve as a foundation for detecting genetic regulatory networks.

Conclusions:

• HMMs and hierarchical Bayesian modeling are effective for analyzing temporal gene expression data by accounting for time dependencies.
• These approaches enable robust identification of differential expression and support the construction of dynamic genetic regulatory networks.
• The coupled framework provides a comprehensive approach to uncovering dynamic biological repertoires in systems biology.