The fundamental subject of this thesis is the development of tools for the analysis of DNA methylation data as well as their application on bisulfite sequencing data comprising a large number of samples. DNA methylation is one of the major epigenetic modifications. It affects the cytosines of the DNA and is essential for the normal development of cells and tissues. Unusual alterations are associated with a variety of diseases and, specially, in cancergeneous tissues global changes in the DNA methylation level have been detected. To sequence DNA methylation on single nucleotide resolution, the sequences are treated with sodium bisulfite before sequencing, whereby unmethylated cytosines are represented as thymines. Thus, specialized techniques are required to process and analyze these kind of data. Here, the bisulfite analysis toolkit BAT is introduced, that is designed to facilitate an quick analysis of bisulfite treated DNA methylation sequencing data. It covers all steps of processing raw sequencing data up to calling of differential DNA methylation. At the begin of analysis, sodium bisulfite treated sequence data are aligned and DNA methylation rates for each covered cytosine in the reference genome are called. Subsequently, BAT integrates annotation data and performs basic analysis, i. e., methylation rate distribution plots and hierarchical clustering of the samples. In addition, calling of differentially methylated regions is performed and statistics of called regions are automatically created. Finally, DNA methylation and gene expression data integration is covered by the calculation of correlating regions. Secondly, a novel algorithm, metilene, for the calculation of differentially methylated regions (DMRs) between two groups of samples is introduced. Existing methods are limited in terms of detection sensitivity as well as time and memory consumption. Our approach is based on a circular binary segmentation, using a scoring function to detect sub-regions that show a stronger difference between the mean methylation levels of two groups than the surrounding background. These sub-regions are tested using a two-dimensional Kolmogorov Smirnov test (2D-KS test) [Fasano 1987] for significant differences taking all samples of each group into account. The use of the non-parametric 2D-KS test allows to avoid assumptions about a background distribution. Furthermore, the two dimensions of the problem, i. e., (i) the detection of a region, such that (ii) the methylation rates of the samples in the groups are significantly different, are taken into account in a single test. The algorithm calls DMRs in sufficiently short time on single sample comparisons as well as on about 50 samples per group. Furthermore, it works on whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) data and is able so estimate missing data points from the methylation rates of other samples in the group. Benchmarks on simulated and real data sets show that metilene outperforms other existing methods and is especially suitable for noisy datasets often found for example in cancer analysis. In the framework of this thesis, the previously introduced methods and algorithms are used to analyze a WGBS dataset of two different subtypes of germinal-center derived B-cell lymphomas and healthy controls. In both lymphoma subgroups genome-wide hypomethylation was found, with an exception for a specific type of promoter regions, i. e., poised promoters, that were frequently found to be hypermethylated. Using the previously presented algorithm, DMRs were called between the three entities. A strong enrichment of DMRs immediately downstream of the transcription start site was observed, indicating the regulatory relevance of this regions. The integration of gene expression data of the same samples, revealed that a considerable amount of the DMRs showed significant correlation between gene expression and DNA methylation. Finally, transcription factor binding sites and mutation data were combined with the methylation and expression data analysis. This identified strongly altered signaling pathways and cancer subtype specific genes. Furthermore, the data integration indicates that mutations and DNA methylation changes may act complementary to another. Finally, findings from the lymphoma study regarding the hypermethylation of poised promoters in cancer were extended to a huge data set comprising a variety of cancers. We could show that the relation of DNA methylation at a small set of frequently poised regions with respect to the background methylation level is sufficient to classify almost all samples based on DNA methylation data from 450k BeadChips into cancer or non-cancer probes. In addition, we found that the increase in methylation co-occurs with upregulated gene expression of several poised promoter regulated genes in almost all fresh cancer samples, implying a de-poising of poised regions. This upregulated gene expression is in contrast to the silencing of those genes in cancer cell lines, indicating that the upregulated gene expression might be a temporary status and possibly contributes to cancerogenesis.

The fundamental subject of this thesis is the development of tools for the analysis of DNA methylation data as well as their application on bisulfite sequencing data comprising a large number of samples. DNA methylation is one of the major epigenetic modifications. It affects the cytosines of the DNA and is essential for the normal development of cells and tissues. Unusual alterations are associated with a variety of diseases and, specially, in cancergeneous tissues global changes in the DNA methylation level have been detected. To sequence DNA methylation on single nucleotide resolution, the sequences are treated with sodium bisulfite before sequencing, whereby unmethylated cytosines are represented as thymines. Thus, specialized techniques are required to process and analyze these kind of data. Here, the bisulfite analysis toolkit BAT is introduced, that is designed to facilitate an quick analysis of bisulfite treated DNA methylation sequencing data. It covers all steps of processing raw sequencing data up to calling of differential DNA methylation. At the begin of analysis, sodium bisulfite treated sequence data are aligned and DNA methylation rates for each covered cytosine in the reference genome are called. Subsequently, BAT integrates annotation data and performs basic analysis, i. e., methylation rate distribution plots and hierarchical clustering of the samples. In addition, calling of differentially methylated regions is performed and statistics of called regions are automatically created. Finally, DNA methylation and gene expression data integration is covered by the calculation of correlating regions. Secondly, a novel algorithm, metilene, for the calculation of differentially methylated regions (DMRs) between two groups of samples is introduced. Existing methods are limited in terms of detection sensitivity as well as time and memory consumption. Our approach is based on a circular binary segmentation, using a scoring function to detect sub-regions that show a stronger difference between the mean methylation levels of two groups than the surrounding background. These sub-regions are tested using a two-dimensional Kolmogorov Smirnov test (2D-KS test) [Fasano 1987] for significant differences taking all samples of each group into account. The use of the non-parametric 2D-KS test allows to avoid assumptions about a background distribution. Furthermore, the two dimensions of the problem, i. e., (i) the detection of a region, such that (ii) the methylation rates of the samples in the groups are significantly different, are taken into account in a single test. The algorithm calls DMRs in sufficiently short time on single sample comparisons as well as on about 50 samples per group. Furthermore, it works on whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) data and is able so estimate missing data points from the methylation rates of other samples in the group. Benchmarks on simulated and real data sets show that metilene outperforms other existing methods and is especially suitable for noisy datasets often found for example in cancer analysis. In the framework of this thesis, the previously introduced methods and algorithms are used to analyze a WGBS dataset of two different subtypes of germinal-center derived B-cell lymphomas and healthy controls. In both lymphoma subgroups genome-wide hypomethylation was found, with an exception for a specific type of promoter regions, i. e., poised promoters, that were frequently found to be hypermethylated. Using the previously presented algorithm, DMRs were called between the three entities. A strong enrichment of DMRs immediately downstream of the transcription start site was observed, indicating the regulatory relevance of this regions. The integration of gene expression data of the same samples, revealed that a considerable amount of the DMRs showed significant correlation between gene expression and DNA methylation. Finally, transcription factor binding sites and mutation data were combined with the methylation and expression data analysis. This identified strongly altered signaling pathways and cancer subtype specific genes. Furthermore, the data integration indicates that mutations and DNA methylation changes may act complementary to another. Finally, findings from the lymphoma study regarding the hypermethylation of poised promoters in cancer were extended to a huge data set comprising a variety of cancers. We could show that the relation of DNA methylation at a small set of frequently poised regions with respect to the background methylation level is sufficient to classify almost all samples based on DNA methylation data from 450k BeadChips into cancer or non-cancer probes. In addition, we found that the increase in methylation co-occurs with upregulated gene expression of several poised promoter regulated genes in almost all fresh cancer samples, implying a de-poising of poised regions. This upregulated gene expression is in contrast to the silencing of those genes in cancer cell lines, indicating that the upregulated gene expression might be a temporary status and possibly contributes to cancerogenesis.

DNA methylation is an epigenetic mechanism that regulates the gene transcription. Folate is used in cellular synthesis of methyl groups, nucleic acids and amino acids. In complex diseases like cancer and neural tube defects (NTD), various genetic and epigenetic alterations can be found that disrupt the normal cell function. The main goals of this thesis were to analyze whether the genes responsible for the folate transport (FOLR1, PCFT, and RFC1) could be regulated by DNA methylation in placenta, blood leukocytes and colorectal cancer. We also addressed the genome-wide DNA methylation changes in colorectal cancer andcervical cancer.We found that changes in the methylated fraction of the RFC1 gene were dependent on the RFC1 80G>A polymorphism in placental specimens with NTDs and blood leukocytes from subjects with high homocysteine (Paper I). In colorectal cancer, the greatest difference in DNA methylation was observed in the RFC1 gene and was related to a lower protein expression (Paper II).In Paper III and IV we studied the DNA methylated fraction using a high-density array. Paper III was focused on genes in the DNA repair pathway and frequently mutated in colorectal cancer. We found that aberrant methylation in the DNA mismatch repair genes was not a frequent event in colorectal cancer and we identified five candidate biomarker genes in colorectal cancer, among them the GPC6 and DCLRE1C genes. In Paper IV, we found hypomethylation of genes involved in the immune system in cervical cancer specimens compared to healthy cervical scrapes and we identified twenty four candidate genes for further evaluation ofclinical value.In conclusion, the work of this thesis filled a relevant knowledge gap regarding the role of differential methylation of the folate transport genes in NTD and colorectal cancer. This thesis work also provided insights into the functional role of DNA methylation in cancer specific pathways and identified potential novel biomarker genes.

No description available.

Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) are two efficient technologies used to achieve higher data rate, lowlatency, robustness against fading used in 5G New Radio (NR). At the receiver end,the data arrives distorted due to disturbance during transfer over the wireless channel.Channel estimation is the applied technique at the receiver end to overcome this problemand mitigate the effect of the disturbance over the wireless channel. The main objective of this thesis is to port an existing channel estimator from a prototypesimulator for 5G to a complete Link-Level simulator that currently has support for 4Gtraffic. Two channel estimation algorithms have been investigated and implemented inthe Link-Level simulator based on MIMO-OFDM system. The channel estimators arethe Least Square (LS) and the Linear Minimum Mean Square Error (LMMSE). Theperformance of the channel estimators is evaluated in terms of Bit Error Rate vs Signalto Noise Ratio. The effectiveness of those implemented algorithms is evaluated using a simulation,where the results show that each channel estimation algorithm is suitable for a specificuse case and depends on channel properties and different scenarios but regardless thetime complexity, the LMMSE has better performance than the LS.

No description available.