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Biomedical subjects

Jacob Koehler

Publications and source records attributed to Jacob Koehler.

2 recordsLinked to original sources

The pathogen-host interactions database (PHI-base) provides insights into generic and novel themes of pathogenicity.

Fungal and oomycete pathogens of plants and animals are a major global problem. In the last 15 years, many genes required for pathogenesis have been determined for over 50 different species. Other studies have characterized effector genes (previously termed avirulence genes) required to activate host responses. By studying these types of pathogen genes, novel targets for control can be revealed. In this report, we describe the Pathogen-Host Interactions database (PHI-base), which systematically compiles such pathogenicity genes involved in pathogen-host interactions. Here, we focus on the biology that underlies this computational resource: the nature of pathogen-host interactions, the experimental methods that exist for the characterization of such pathogen-host interactions as well as the available computational resources. Based on the data, we review and analyze the specific functions of pathogenicity genes, the host-specific nature of pathogenicity and virulence genes, and the generic mechanisms of effectors that trigger plant responses. We further discuss the utilization of PHI-base for the computational identification of pathogenicity genes through comparative genomics. In this context, the importance of standardizing pathogenicity assays as well as integrating databases to aid comparative genomics is discussed.

Computational Biology↗

Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures.

The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/

Algorithms↗