TY  - JOUR
AB  - 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.
DA  - 2005
DO  - 10.1186/1471-2105-6-15
KW  - Protein Clustering
KW  - Clustering
LA  - eng
IS  - 1
PY  - 2005
SN  - 1471-2105
T2  - BMC Bioinformatics
TI  - Large scale hierarchical clustering of protein sequences
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-17751684
Y2  - 2024-11-21T22:43:16
ER  -