The Dynamic Epigenome

Analysis of the Distribution of Histone Modifications

Lydia Steiner


The last decade revealed that the epigenome state and its regulation are important for differentiation and development. Correlations with aging are shown leading to the hypothesis that misregulation of the epigenome causes aging. Furthermore, diseases are identified which are caused by errors in the epigenome state and its regulation.

Identification of erroneous epigenome states and misregulation requires the prior knowledge of the normal state. Several studies aiming at measuring epigenome states in different organisms and cell types and thus, provide huge amount of data.

In this dissertation, methods are developed to analyze and characterize histone modifications with respect to different cell types. Application of this method is shown for a published data set of mouse consisting of data for H3K4me3, H3K27me3, and H3K9me3 measured in embryonic stem cells, embryonic fibroblasts and neuronal progenitors.

Furthermore, method for the detection of the epigenetic patterns are presented in this dissertation. Therefore, a segmentation methods is developed to segment the genome guided by the data sets. Based on this segmentation, the epigenome states as well as epigenetic variation can be studied. Different visualization methods are developed to highlight the epigenetic patterns in the segmentation data. It was shown that new insights can be gained in to epigenetic patterns and their regulation applying the segmentation and the visualization methods to the mouse data set. Not only the epigenome state but also the the epigenetic variation are studied in the mouse and show the power of the developed methods.

Although the studied data set in this dissertation contains only normal tissue cells, the methods are not restricted to study the reference epigenome state. Comparison of normal and disease cells as well as comparison with aged cells are possible with all of the methods.

Finally, the methods are compared based on the obtained results. It shows that all methods highlight different aspects of the data. Thus, applying all methods to the same data sets, deep insights into the epigenome in murine embryonic stem cells, embryonic fibroblasts and neuronal progenitor cells are gained.

Dissertation (PDF via Qucosa)

Supplemental Material