Integrative epigenome analysis
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AbstractHigh throughput technologies, like next generation sequencing, in combination with assays for probing epigenetic marks, have made it possible to map the epigenetic landscapes of cell types. With the new information the relation between gene expression and the epigenomic configuration of associated genomic regions can be studied. Cancer develops as a consequence of gene deregulation, which in turn is driven by genetic and epigenetic changes. A main theme of my PhD project was to test existing software and develop novel scripts for integrative analysis of such changes. Their interactive influence on gene expression in osteosarcoma is for instance analyzed. The information is in addition used to identify pathways contributing to osteosarcoma. A software called The Genomic HyperBrowser for manipulation and statistical analyze of genomic data has been developed at the University of Oslo. The Genomic HyperBrowser has, together with scripts developed in the statistical programming language R, been used to study the dependencies, and select genes, based on genomic, epigenomic and transcriptomic alterations in samples from osteosarcoma and immune cells. The gene expression, promoter methylation and DNA copy number data was acquired by oligonucleotide microarray technology, and the histone modification data was acquired by technology based on chromatin immune precipitation and next generation sequencing (ChIP-seq). For the analysis of osteosarcoma a method was used that was based on selection of genes that were deregulated, and in addition were annotated with genomic and/or epigenomic alterations, in a minimum number of analyzed samples. For the analysis of immune cells, a statistical test was developed and used to identify association between the number of histone modifications (of a given type) in a gene promoter and the transcriptional activity of the gene. A tool, integrated in the HyperBrowser environment that allows for the application of software for clustering of gene expression data to epigenomic data was also developed.
List of papers
|Paper I: Kresse SH, Rydbeck H, Skårn M, et al. Integrative Analysis Reveals Relationships of Genetic and Epigenetic Alterations in Osteosarcoma. PloS one 2012;7:e48262. The published version of this paper is available at: https://doi.org/10.1371/journal.pone.0048262|
|Paper II: Kuijjer ML, Rydbeck H, Kresse SH, et al. Identification of osteosarcoma driver genes by integrative analysis of copy number and gene expression data. Genes, chromosomes & cancer 2012;51:696–706. The paper is removed from the thesis in DUO due to publisher restrictions. The published version is available at: https://doi.org/10.1002/gcc.21956|
|Paper III: Sandve GK, Gundersen S, Rydbeck H, et al. The Genomic Hyper-Browser: inferential genomics at the sequence level. Genome biology 2010;11:R121. The published version of this paper is available at: https://doi.org/10.1186/gb-2010-11-12-r121|
|Paper IV: Rydbeck H, Sandve GK, Rye M, and Hovig E. ClusTrack: Defining distance and clustering for genomic element tracks to compare landscapes of occupancy. Submitted. 2012:1–19. The paper is removed from the thesis in DUO due to publisher restrictions.|