Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Wissensmanagement in der Bioinformatik

Forschungsseminar Sommersemester 2005

"Neue Entwicklungen in der Bioinformatik und Informationsintegration"

Philip Groth

Phenotypes in Research - Data Resources and Tools

All known diseases are defined by a phenotype and most may be traced back to a genetic origin. As changes in genotype can contribute to the development of diseases, knowledge about such genotype-phenotype relationships is valuable to find appropriate treatments. Generating this knowledge, however, is tedious and costly experimental work.

One method to direcly relate a given phenotype with a genetic modification is to functionally annotate large numbers of genes with the help of so-called "forward genetics" studies in model organisms such as D. melanogaster or yeast. In higher organisms, the lack of sophisticated genetic methods is being replaced through examinations of transgenic or knockout animals as well as comprehensive SNP analyses. The amount of such data will now dramatically increase with the advent of RNA interference (RNAi) which allows to specifically silence one gene at a time and to observe the phenotypic outcome. All these methods of genotype-phenotype examinations are generating exponentially growing amounts of data. Strategies need now to be developed to systematically transform these data into knowledge.

To accomplish this task, tools for storing and processing phenotypic information are needed. But even basic key tools are still missing, e.g. there is no central repository for available knowledge on genotype-phenotype relationships. Hence, the comparison of phenotypes over a range of species (i.e. comparative phenomics) has lacked practical applicability and is even more difficult for more distantly related phenotypes. Most importantly, prerequisites such as phenotype vocabulary, ontologies, or standardized protocols for phenotypical assays are being developed just now.

In this talk, I will review the currently available resources for phenotypic data and tools already available for comparing phenotypes. I will introduce phenotype vocabularies and ontologies that are currently being developed and show the remaining gaps that still need to be filled. I will explain how some of the resulting bioinformatics opportunities in comparative phenomics can be approached and I will outline tasks at hand to accomplish these goals.