Large scale hierarchical clustering of protein sequences
1 Max Planck Institute for Molecular Genetics, Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany
2 Universität Bielefeld, Technische Fakultät, AG Genominformatik, Postfach 100131, 33501 Bielefeld, Germany
3 TFH Wildau, Bahnhofstrasse 1, 15745 Wildau, Germany
BMC Bioinformatics 2005, 6:15 doi:10.1186/1471-2105-6-15Published: 22 January 2005
Searching a biological sequence database with a query sequence looking for homologues has become a routine operation in computational biology. In spite of the high degree of sophistication of currently available search routines it is still virtually impossible to identify quickly and clearly a group of sequences that a given query sequence belongs to.
We report on our developments in grouping all known protein sequences hierarchically into superfamily and family clusters. Our graph-based algorithms take into account the topology of the sequence space induced by the data itself to construct a biologically meaningful partitioning. We have applied our clustering procedures to a non-redundant set of about 1,000,000 sequences resulting in a hierarchical clustering which is being made available for querying and browsing at http://systers.molgen.mpg.de/ webcite.
Comparisons with other widely used clustering methods on various data sets show the abilities and strengths of our clustering methods in producing a biologically meaningful grouping of protein sequences.