A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data.
The genome-wide DNA-protein-binding data, DNA sequence data and gene expression data represent complementary means to deciphering global and local transcriptional regulatory circuits. Combining these different types of data can not only improve the statistical power, but also provide a more comprehensive picture of gene regulation. In this paper, we propose a novel statistical model to augment protein-DNA-binding data with gene expression and DNA sequence data when available. We specify a hierarchical Bayes model and use Markov chain Monte Carlo simulations to draw inferences. Both simulation studies and an analysis of an experimental data set show that the proposed joint modeling method can significantly improve the specificity and sensitivity of identifying target genes as compared with conventional approaches relying on a single data source.
Xie
Yang
Y
Division of Biostatistics, Department of Clinical Sciences, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA. yang.xie@utsouthwestern.edu
Pan
Wei
W
Jeong
Kyeong S
KS
Xiao
Guanghua
G
Khodursky
Arkady B
AB
D001499:Bayes Theorem; D003198:Computer Simulation; D004247:DNA / Q000235:genetics* / Q000378:metabolism; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism; D015870:Gene Expression; D015964:Gene Expression Regulation, Bacterial; D051756:Leucine-Responsive Regulatory Protein / Q000235:genetics / Q000378:metabolism*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D011485:Protein Binding; D018085:Regulon; D017422:Sequence Analysis, DNA / Q000706:statistics & numerical data*
D051756:Leucine-Responsive Regulatory Protein; D004247:DNA