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Abstract:
Although virtually all cells in an organism share the same genome, regulatory mechanisms give rise to hundreds of different, highly specialized cell types. Understanding these mechanisms has been in the limelight of epigenomic research. It is now evident that cellular identity is inscribed in the epigenome of each individual cell. Nonetheless, the precise mechanisms by which different epigenomic marks are involved in regulating gene expression are just beginning to be unraveled. Furthermore, epigenomic patterns are highly dynamic and subject to environmental influences. Any given cell type is defined by cell populations exhibiting epigenetic heterogeneity at different levels. Characterizing this heterogeneity is paramount in understanding the regulatory role of the epigenome. Different epigenomic marks can be profiled using high-throughput sequencing, and global initiatives have started to provide a comprehensive picture of the human epigenome by assaying a multitude of marks across a broad panel of cell types and conditions. In particular, DNA methylation has been extensively studied for its gene-regulatory role in health and disease. This thesis describes computational methods and pipelines for the analysis of DNA methylation data. It provides concepts for addressing bioinformatic challenges such as the processing of large, epigenome-wide datasets and integrating multiple levels of information in an interpretable manner. We developed RnBeads, an R package that facilitates comprehensive, interpretable analysis of large-scale DNA methylation datasets at the level of single CpGs or genomic regions of interest. With the epiRepeatR pipeline, we introduced additional tools for studying global patterns of epigenomic marks in transposons and other repetitive regions of the genome. Blood-cell differentiation represents a useful model for studying trajectories of cellular differentiation. We developed and applied bioinformatic methods to dissect the DNA methylation landscape of the hematopoietic system. Here, we provide a broad outline of cell-type-specific DNA methylation signatures and phenotypic diversity reflected in the epigenomes of human mature blood cells. We also describe the DNA methylation dynamics in the process of immune memory formation in T helper cells. Moreover, we portrayed epigenetic fingerprints of defined progenitor cell types and derived computational models that were capable of accurately inferring cell identity. We used these models in order to characterize heterogeneity in progenitor cell populations, to identify DNA methylation signatures of hematopoietic differentiation and to infer the epigenomic similarities of blood cell types. Finally, by interpreting DNA methylation patterns in leukemia and derived pluripotent cells, we started to discern how epigenomic patterns are altered in disease and explored how reprogramming of these patterns could potentially be used to restore a non-malignant state. In summary, this work showcases novel methods and computational tools for the identification and interpretation of epigenetic signatures of cell identity. It provides a detailed view on the epigenomic landscape spanned by DNA methylation patterns in hematopoietic cells that enhances our understanding of epigenetic regulation in cell differentiation and disease.
