Large scale hierarchical clustering of protein sequences Export

@article{citeulike:2211305, abstract = {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.}, author = {Krause, Antje and Stoye, Jens and Vingron, Martin}, citeulike-article-id = {2211305}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-6-15}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/15663796}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=15663796}, doi = {10.1186/1471-2105-6-15}, journal = {BMC Bioinformatics}, keywords = {clustering, hierarchical}, number = {1}, posted-at = {2008-12-31 03:14:29}, priority = {2}, title = {Large scale hierarchical clustering of protein sequences}, url = {http://dx.doi.org/10.1186/1471-2105-6-15}, volume = {6}, year = {2005} }

TY - JOUR ID - citeulike:2211305 L3 - citeulike-article-id:2211305 N2 - 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. IS - 1 JF - BMC Bioinformatics TI - Large scale hierarchical clustering of protein sequences VL - 6 KW - clustering KW - hierarchical AU - Krause, Antje AU - Stoye, Jens AU - Vingron, Martin PY - 2005/// UR - http://dx.doi.org/10.1186/1471-2105-6-15 ER -