Large scale hierarchical clustering of protein sequences.
BACKGROUND
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.


RESULTS
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/.


CONCLUSIONS
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.
Krause
Antje
A
Max Planck Institute for Molecular Genetics, Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany. akrause@igw.tfh-wildau.de
Stoye
Jens
J
Vingron
Martin
M
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D030541:Databases, Genetic; D030561:Databases, Nucleic Acid; D030562:Databases, Protein; D005656:Fungal Proteins / Q000737:chemistry; D008040:Genetic Linkage; D016678:Genome; D016247:Information Storage and Retrieval; D008954:Models, Biological; D005810:Multigene Family; D010802:Phylogeny; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software
D005656:Fungal Proteins; D011506:Proteins