Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

Large scale hierarchical clustering of protein sequences

Antje Krause13*, Jens Stoye2 and Martin Vingron1

Author Affiliations

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

For all author emails, please log on.

BMC Bioinformatics 2005, 6:15  doi:10.1186/1471-2105-6-15

Published: 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 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.