Advanced Visual Analytics Approaches for the Integrative Study of Genomic and Transcriptomic Data

The advances in next-generation sequencing (NGS) technology enabled rapid and cost-e ective whole genome analyses. Nowadays, it is known that individual organisms have unique genome sequences and that di erences between these sequences are the reason for genetic diversity. Furthermore, the biomolecular processes of living organisms are steered by genes and the interplay of their products. Perturbations in these systems often lead to disease. Thus, one of the major question in biomedical research is how genetic variations in uence gene function, and how these a ect underlying biological pathways and gene interaction networks. One of the most common sources of genetic diversity are single nucleotide variations (SNVs). So-called Genome Wide Association Studies (GWAS) as well as expression Quantitative Trait Locus (eQTL) studies intend to associate SNVs with e.g. disease related binary or quantitative traits. However, available methods are usually limited to statistical analyses and previous approaches to improve the interpretation of the respective results are often insu cient. The goal of this dissertation was the development of new visual analytical approaches to assist purely statistical methods in the identi cation, characterization and interpretation of SNVs. For this purpose, Mayday, an expression analysis workbench, has been extended with innovative visual analytical methods to allow for integrative analyses with respect to variation and the combination of variation and gene expression data. For GWAS based analyses, inPHAP has been developed, which allows for the visual assessment of genotype and phased haplotype data between populations or subgroups of populations. It o ers an interactive matrix-like visualization and advanced methods for SNV pattern identi cation, in particular aggregation. In addition, Reveal was developed for the analysis of eQTL data with a focus on the integrative analysis of SNV and gene expression data. It o ers interactive network, matrix and table visualizations to study SNV gene associations, which can be linked to each other on the SNV and gene level. Finally, the analysis and visualization of SNVs as well as structural variations is possible in the context of whole genome alignments with the tool GenomeRing. In this work, enhancement and optimization strategies have been developed to improve visualization of structural similarities and dissimilarities in GenomeRing, as well as to reduce visual clutter in general. Genomic variations, especially SNVs, also play an important role in the immensely growing eld of paleogenetics, where DNA of ancient origin is compared to modern DNA with the intention to gain insights into evolutionary history. In this dissertation, a computational pipeline for comparative NGS analyses of ancient and modern DNA samples has been described. Special attention was given to the read merging step, which is required to cope with the quality limitations inherent to ancient DNA (aDNA), in particular DNA fragmentation and nucleotide misincorporation. In addition, aDNA is usually only retrievable in low amounts and it is often contaminated with DNA of modern microorganisms. To solve this issue, a highly economical microarray-based DNA capturing strategy has been developed for the parallel detection and enrichment of aDNA from up to 100 di erent human pathogens. With the costs for sequencing in steady decline and with robust statistical methods available, the time spent on and the e ciency of the integration and interpretation of results by biologists, clinicians, researchers and bioinformaticians has become the limiting factor in the eld. Therefore, the tools described in this thesis make an invaluable contribution to the success of current and future studies on genetic variations.