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Published on: June 28, 2018
A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data
Yang Xie1, Wei Pan, Kyeong S Jeong
1Division of Biostatistics, Department of Clinical Sciences, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA. yang.xie@utsouthwestern.edu
Statistics in Medicine
|January 6, 2010
Summary
This study introduces a new statistical model combining DNA-protein binding, gene expression, and DNA sequence data. This integrated approach enhances the accuracy of identifying target genes in transcriptional regulation.
Area of Science:
- Genomics
- Bioinformatics
- Systems Biology
Background:
- Transcriptional regulatory circuits are complex, involving interactions between DNA, proteins, and gene expression.
- Genome-wide DNA-protein binding, DNA sequence, and gene expression data offer complementary insights into gene regulation.
- Integrating diverse data types can improve the statistical power and comprehensiveness of regulatory circuit analysis.
Purpose of the Study:
- To develop a novel statistical model for integrating multiple genomic data types.
- To augment protein-DNA binding data with gene expression and DNA sequence information.
- To enhance the identification of target genes in transcriptional regulation.
Main Methods:
- A hierarchical Bayes model was specified for joint data modeling.
- Markov chain Monte Carlo (MCMC) simulations were employed for statistical inference.
- The model was evaluated using both simulation studies and experimental data analysis.
Main Results:
- The proposed joint modeling method significantly improved specificity and sensitivity in identifying target genes.
- Compared to single-data-source approaches, the integrated model provided a more accurate picture of gene regulation.
- The model demonstrated robust performance in both simulated and real-world biological data.
Conclusions:
- Integrating diverse genomic data sources through a joint statistical model offers substantial benefits for understanding gene regulation.
- The hierarchical Bayes approach effectively combines protein-DNA binding, gene expression, and DNA sequence data.
- This novel method represents a significant advancement in identifying target genes and deciphering regulatory circuits.
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