by Joachim W. Bargsten

Bioinformatics has become a key discipline in modern biology. Major factors in this development have been various technological advances, allowing to create vast amounts of biological data, sometimes referred to as »big data«. Such data should be stored, interpreted and integrated to answer biological research questions appropriately and to generate new ideas and new research leads. As a result of these advances, biology has become a more quantitative and a much more data-driven science. Bioinformatics is at the interface of data, computer science and biological research. In recent years, the development and application of bioinformatics methods has led to many applications in different branches of biology, such as medicine or plant breeding. Originally defined as and aimed at the study of informatic processes in biotic systems, it has developed into computational methods for (comparative) analysis of genome and other »omics« data. Bioinformatics has a dual nature, not only in the combination of biology and computer science, but also in serving as a tool for biologists on the one hand and as a separate research field, sometimes referred to as »computational biology« on the other hand. Like in statistics, most bioinformatics approaches can be applied in multiple settings and are independent of particular species or biological models. This inherent flexibility of the tools of bioinformatics has contributed to their wide-spread use. Despite such flexibility, methods generally need to be adapted due to the particular aspects of the biological research topic under study, as well as the nature and quality of data available. Unlike human research with one organism as the central focus of attention, plant bioinformatics generally deals with different species that each present their own data, challenges and issues.

Joachim Bargsten recently defended his PhD thesis, which presents a showcase of plant bioinformatics, with examples of genome annotation, comparative genomics, gene function prediction and the analysis of network topology for gene function prediction, for which core methods are used and developed.

To download the full copy of his thesis, click on the link below (7.5 MB).