Computational Methods for Analysis of Dynamic Transcriptome and Its Regulation Through Chromatin Remodeling and Intracellular Signaling

Transcription is the first step in gene expression in which genetic information is transferred from DNA to RNA. Gene expression is highly controlled through transcriptional regulation at many steps. Transcriptional regulation in eukaryotes occurs, e.g., through binding of transcription factors and chromatin remodeling via various epigenetic pathways. Additionally, dysregulated transcription has been reported in various diseases. Thus, transcription and transcriptional regulation are of great interest for research. In this work, we study the transcriptome and its regulation using bioinformatic and computational biology approaches. We propose computational methods, LIGAP and DyNB, for analysis of temporal gene expression profiles measured using microarrays and RNA-seq, respectively. LIGAP is a methodology based on Gaussian processes for simultaneous differential expression analysis between an arbitratory number of time series microarray data sets. DyNB, is an extension of the Gaussian-Cox process in which the Poisson distribution is replaced by the negative binomial distribution. Additionally, DyNB enables the study of systematic differences, such as differential differentiation efficiencies, between conditions. Sorad, is a modeling framework based on differential equations and Gaussian processes for analysis of intracellular signaling transduction through phosphoprotein activities. We also propose and demonstrate how the in silico models inferred using Sorad can be used in estimating modulation strategies to obtain desired signaling response. Finally, we study the determinants of nucleosome positioning and subsequent effects on gene expression. All the proposed methods are benchmarked against existing methods and, in addition, they are applied to real-life problems. The comparison studies validate the applicability of the presented methods and demonstrate their improved performance relative to existing methods. Our transcriptome studies led to increased knowledge on the early differentiation of human T cells, and provided a valuable resource of candidate genes for future functional studies of the differentiation process. Our nucleosome study revealed that within loci important for T cell differentiation only 6% of the nucleosomes are differentially remodelled between T helper 1 and 2 cells and cytotoxic T lymphocytes. The remodelled nucleosomes correlated with the known differentiation program, chromatin accessibility, transcription factor binding, and gene expression. Finally, our data supports the hypothesis that transcription factors and nucleosomes compete for DNA occupancy.