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Plants have evolved many mechanisms to defend against herbivores and pathogens. In many cases, these mechanisms took other duties. One example of such a neofunction- alisation would be carnivory. Carnivory evolved from the defence against herbivores. Instead of repelling the predator with a bitter taste, the plant kills it and absorbs its nutrients. A second example can be found in the pollination process. Many of the genes involved here were originally part of defence mechanisms against pathogens. In this thesis, I study these two examples on a genomic and transcriptomic level. The first project, Genomics of carnivorous Droseraceae, aims at obtaining annotated genome sequences of three carnivorous plants. I assembled the genome of Aldrovanda vesiculosa, annotated those of A. vesiculosa, Drosera spatulata and Dionaea muscipula and com- pared their genomic contents. Because of the high repetitiveness of the D. muscipula genome, I also developed reper, an assembly free method for detection, classification and quantification of repeats. With that method, we were able to study the repeats without the need of incorporating them into a genome assembly. The second large project investigates the role of DEFL (defensin-like) genes in pollen tube guidance in tobacco flowers. We sequenced the transcriptome of the SR1 strain in different stages of the pollination process. I assembled and annotated the transcriptome and searched for differentially expressed genes. We also used a method based on Hidden- Markov-Models (HMM) to find DEFLs, which I then analysed regarding their expression during the different stages of fertilisation. In total, this thesis results in annotated genome assemblies of three carnivorous Droser- aceae, which are used as a foundation for various analyses investigating the roots of car- nivory, insights into the role of DEFLs on a transcriptomic level in tobacco pollination and a new method for repeat identification in complex genomes.
New experimental methods have drastically accelerated the pace and quantity at which biological data is generated. High-throughput DNA sequencing is one of the pivotal new technologies. It offers a number of novel applications in various fields of biology, including ecology, evolution, and genomics. However, together with those opportunities many new challenges arise. Specialized algorithms and software are required to cope with the amount of data, often requiring substantial training in bioinformatic methods. Another way to make those data accessible to non-bioinformaticians is the development of programs with intuitive user interfaces.
In my thesis I developed analyses and programs to tackle current problems with high-throughput data in biology. In the field of ecology this covers the establishment of the bioinformatic workflow for pollen DNA meta-barcoding. Furthermore, I developed an application that facilitates the analysis of ecological communities in the context of their traits. Information from multiple public databases have been aggregated and can now be mapped automatically to existing community tables for interactive inspection. In evolution the new data are used to reconstruct phylogenetic trees from multiple genes. I developed the tool bcgTree to automate this process for bacteria. Many plant genomes have been sequenced in current years. Sequencing reads of those projects also contain data from the chloroplasts. The tool chloroExtractor supports the targeted extraction and analysis of the chloroplast genome. To compare the structure of multiple genomes specialized software is required for calculation and visualization of the relationships. I developed AliTV to address this. In contrast to existing programs for this task it allows interactive adjustments of produced graphics. Thus, facilitating the discovery of biologically relevant information. Another application I developed helps to analyze transcriptomes even if no reference genome is present. This is achieved by aggregating the different pieces of information, like functional annotation and expression level, for each transcript in a web platform. Scientists can then search, filter, subset, and visualize the transcriptome.
Together the methods and tools expedite insights into biological systems that were not possible before.