Integrating epigenetic prior in dynamic Bayesian network for gene regulatory network inference

Gene regulatory network (GRN) inference from high throughput biological data has drawn a lot of research interest in the last decade. However, due to the complexity of gene regulation and lack of sufficient data, GRN inference still has much space to improve. One way to improve the inference of GRN is by developing methods to accurately combine various types of data. Here we apply dynamic Bayesian network (DBN) to infer GRN from time-series gene expression data where the Bayesian prior is derived from epigenetic data of histone modifications. We propose several kinds of prior from histone modification data, and use both real and synthetic data to compare their performance. Parameters of prior integration are also studied to achieve better results. Experiments on gene expression data of yeast cell cycle show that our methods increase the accuracy of GRN inference significantly.

[1]  Ruijie Zhang,et al.  Revealing epigenetic patterns in gene regulation through integrative analysis of epigenetic interaction network , 2012, Molecular Biology Reports.

[2]  Michael Hecker,et al.  Gene regulatory network inference: Data integration in dynamic models - A review , 2009, Biosyst..

[3]  Dirk Husmeier,et al.  Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[4]  J. Han,et al.  Inferring causal relationships among different histone modifications and gene expression. , 2008, Genome research.

[5]  Stefan Bornholdt,et al.  Boolean network models of cellular regulation: prospects and limitations , 2008, Journal of The Royal Society Interface.

[6]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[7]  João Ricardo Sato,et al.  Modeling gene expression regulatory networks with the sparse vector autoregressive model , 2007, BMC Systems Biology.

[8]  S. Chib,et al.  Understanding the Metropolis-Hastings Algorithm , 1995 .

[9]  Megan F. Cole,et al.  Genome-wide Map of Nucleosome Acetylation and Methylation in Yeast , 2005, Cell.

[10]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1998, Learning in Graphical Models.

[11]  Joshua M. Stuart,et al.  Conserved Genetic Modules 5 / 29 / 2003 1 A gene co-expression network for global discovery of conserved genetic modules in H . sapiens , D . melanogaster , C . elegans , and S . cerevisiae , 2003 .

[12]  Jagath C. Rajapakse,et al.  Integration of Epigenetic Data in Bayesian Network Modeling of Gene Regulatory Network , 2011, PRIB.

[13]  Kevin Murphy,et al.  Modelling Gene Expression Data using Dynamic Bayesian Networks , 2006 .

[14]  J. Collins,et al.  Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling , 2003, Science.

[15]  Jagath C. Rajapakse,et al.  Stability of building gene regulatory networks with sparse autoregressive models , 2011, BMC Bioinformatics.

[16]  Andrew J. Bulpitt,et al.  A Primer on Learning in Bayesian Networks for Computational Biology , 2007, PLoS Comput. Biol..

[17]  L. Aravind,et al.  Comprehensive analysis of combinatorial regulation using the transcriptional regulatory network of yeast. , 2006, Journal of molecular biology.

[18]  Kevin Y. Yip,et al.  A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets , 2011, Genome Biology.

[19]  Xiaojiang Xu,et al.  Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression , 2010, BMC Bioinformatics.

[20]  Dirk Husmeier,et al.  Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks. , 2007, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.

[21]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[22]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..