TY - JOUR T1 - Predicting Spatial and Temporal Gene Expression Using an Integrative Model of Transcription Factor Occupancy and Chromatin State A1 - Wilczynski, Bartek A1 - Liu, Ya-Hsin A1 - Yeo, Zhen Xuan A1 - Furlong, Eileen E. M. Y1 - 2012/12/06 N2 - Author Summary Development is a complex process in which a single cell gives rise to a multi-cellular organism comprised of diverse cell types and well-organized tissues. This transformation requires tightly coordinated expression, both spatially and temporally, of hundreds to thousands of genes specific to any given tissue. To orchestrate these patterns, gene expression is regulated at multiple steps, from TF binding to cis-regulatory modules, general transcription factor and RNA polymerase II recruitment to promoters, chromatin remodeling, and three-dimensional looping interactions. Despite this level of complexity, the regulation of gene expression is typically modeled in the context of transcription factor binding and a single enhancer's activity as this is where the majority of experimental data is available. Recent advances in the measurement of chromatin modifications and insulator binding during embryogenesis provide new datasets that can be used for modeling gene expression. Here we use a Bayesian approach to integrate all three levels of information to combine the activity of multiple regulatory elements into a single model of a gene's expression, implementing an expectation maximization strategy to overcome the problem of missing data. Importantly, while the data for histone modifications and insulator binding represents merged signals from all cells in the embryo, the model can extract cell type specific and stage-specific predictions on gene expression for hundreds of genes of unknown expression. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 8 IS - 12 UR - https://doi.org/10.1371/journal.pcbi.1002798 SP - e1002798 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002798 ER -