title: On dimension reduction of clustering results in structural bioinformatics.
abstract: OPTICS is a density-based clustering algorithm that performs well in a wide variety of applications. For a set of input objects, the algorithm creates a reachability plot that can either be used to produce cluster membership assignments, or interpreted itself as an expressive two-dimensional representation of the clustering structure of the input set, even if the input set is embedded in higher dimensions. The focus of this work is a visualization method that can be applied for comparing two, independent hierarchical clusterings by assigning colors to all entries of the input database. We give two applications related to macromolecular structural properties: the first is a sequence-based clustering of the SwissProt database that is evaluated using NCBI taxonomy identifiers, and the second application involves clustering locations of specific atoms in the serine protease enzyme family-and the clusters are evaluated using SCOP structural classifications.
authors.affiliation: PIT Bioinformatics Group, Eötvös University, Pázmány Péter stny. 1/C, H-1117 Budapest, Hungary; Uratim Ltd., H-1118 Budapest, Hungary. Electronic address: hugeaux@pitgroup.org.
authors.forename: Gábor
authors.initials: G
authors.lastname: Iván
authors.affiliation: PIT Bioinformatics Group, Eötvös University, Pázmány Péter stny. 1/C, H-1117 Budapest, Hungary; Uratim Ltd., H-1118 Budapest, Hungary. Electronic address: grolmusz@pitgroup.org.
authors.forename: Vince
authors.initials: V
authors.lastname: Grolmusz
keywords: Clustering; OPTICS; Phylogenetics; Phylogenomics; Protein sequences; SCOP classification; SCOP tree; Sequence alignment; SwissProt; UniProt