Biomedical subjects

G Kastenmüller

Publications and source records attributed to G Kastenmüller.

3 recordsLinked to original sources

PubMed2005

CYGD: the Comprehensive Yeast Genome Database.

The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes. The database serves as a common resource generated by a European consortium, going beyond the provision of sequence information and functional annotations on individual genes and proteins. In addition, it provides information on the physical and functional interactions among proteins as well as other genetic elements. These cellular networks include metabolic and regulatory pathways, signal transduction and transport processes as well as co-regulated gene clusters. As more yeast genomes are published, their annotation becomes greatly facilitated using S.cerevisiae as a reference. CYGD provides a way of exploring related genomes with the aid of the S.cerevisiae genome as a backbone and SIMAP, the Similarity Matrix of Proteins. The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/.

Binding Sites↗

PubMed2001

The HIB database of annotated UniGene clusters.

SUMMARY: The HumanInfoBase (HIB) is a database of putative human gene transcripts. UniGene clusters are assembled, and the resulting consensus sequences are submitted to the PEDANT software system (Frishman,D., Albermann,K., Hani,J., Heumann,K., Metanomski,A., Zollner,A. and Mewes,H.-W., 2001, Bioinformatics, 17, 44--57) for fully automatic sequence analysis and annotation. Predicted transcripts are classified using a variety of functional and structural categories, and hyperlinks to various databases are provided for additional information. A WWW-based graphical user interface represents the assembly process as well as functionally important sites in the putative transcripts.

Data Collection↗

PubMed1999

Nearest neighbor classification in 3D protein databases.

In molecular databases, structural classification is a basic task that can be successfully approached by nearest neighbor methods. The underlying similarity models consider spatial properties such as shape and extension as well as thematic attributes. We introduce 3D shape histograms as an intuitive and powerful approach to model similarity for solid objects such as molecules. Errors of measurement, sampling, and numerical rounding may result in small displacements of atomic coordinates. These effects may be handled by using quadratic form distance functions. An efficient processing of similarity queries based on quadratic forms is supported by a filter-refinement architecture. Experiments on our 3D protein database demonstrate the high classification accuracy of more than 90% and the good performance of the technique.

Algorithms↗