There are many thousand of hereditary diseases in humans, each od which has a specific combination of phenotypic features, but computational analysis of phenotypic data has been hampered by adequate computational data structures. We have therefore d eveloped an ontology to describe the phenotypic features seen in hereditary and other forms of human disease. This manually curated program can be used to study phenotypic features with bioinformatic tools and other forms of computational analysis. Following its publication in November 2008, the Human Phenotype Ontology (HPO) was featured as a research highlight in Nature Reviews Genetics and is already being adopted by international research groups for phenotyping, including most prominently the DECIPHER group at the European Bioinformatics Institute/Sanger Center. We have more recently used the HPO to develop a clinical diagnostics algorithm for human genetics that utilizes a novel statistical model of semantic similarities in ontologies to provide a ranking of the candidate differential diagnoses and have developed a novel graph algorithm that accelerates semantic searches in ontologies by many orders of magnitude. In addition, our bioinformatics group is active in a number of other areas including algorithms and support for ChIP-seq and other next-generation sequencing applications as well as analysis of microRNA and mRNA microarray hybridizations and promoter analysis.

Selected publications:

Köhler S, Schulz MH, Krawitz P, Bauer S, Dölken S, Ott CE, Mundlos C, Horn D, Mundlos S, Robinson PN.
Clinical diagnostics in human genetics with semantic similarity searches in ontologies.
Am J Hum Genet. 2009 Oct;85(4):457-64.

Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet. 2008 Nov;83(5):610-5.