SYSTERS


15663796	Large scale hierarchical clustering of protein sequences. [PMID: 15663796] Krause A, Stoye J, Vingron M. 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. BMC Bioinformatics. 2005:6() \| 30 Citations (from Europe PMC, 2025-03-15)
15608183	The SYSTERS Protein Family Database in 2005. [PMID: 15608183] Meinel T, Krause A, Luz H, Vingron M, Staub E. Abstract The SYSTERS project aims to provide a meaningful partitioning of the whole protein sequence space by a fully automatic procedure. A refined two-step algorithm assigns each protein to a family and a superfamily. The sequence data underlying SYSTERS release 4 now comprise several protein sequence databases derived from completely sequenced genomes (ENSEMBL, TAIR, SGD and GeneDB), in addition to the comprehensive Swiss-Prot/TrEMBL databases. The SYSTERS web server (http://systers.molgen.mpg.de) provides access to 158 153 SYSTERS protein families. To augment the automatically derived results, information from external databases like Pfam and Gene Ontology are added to the web server. Furthermore, users can retrieve pre-processed analyses of families like multiple alignments and phylogenetic trees. New query options comprise a batch retrieval tool for functional inference about families based on automatic keyword extraction from sequence annotations. A new access point, PhyloMatrix, allows the retrieval of phylogenetic profiles of SYSTERS families across organisms with completely sequenced genomes. Nucleic Acids Res. 2005:33(Database issue) \| 23 Citations (from Europe PMC, 2025-03-15)

Large scale hierarchical clustering of protein sequences. [PMID: 15663796]

Krause A, Stoye J, Vingron M.

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.

BMC Bioinformatics. 2005:6() | 30 Citations (from Europe PMC, 2025-03-15)

The SYSTERS Protein Family Database in 2005. [PMID: 15608183]

Meinel T, Krause A, Luz H, Vingron M, Staub E.

Abstract

The SYSTERS project aims to provide a meaningful partitioning of the whole protein sequence space by a fully automatic procedure. A refined two-step algorithm assigns each protein to a family and a superfamily. The sequence data underlying SYSTERS release 4 now comprise several protein sequence databases derived from completely sequenced genomes (ENSEMBL, TAIR, SGD and GeneDB), in addition to the comprehensive Swiss-Prot/TrEMBL databases. The SYSTERS web server (http://systers.molgen.mpg.de) provides access to 158 153 SYSTERS protein families. To augment the automatically derived results, information from external databases like Pfam and Gene Ontology are added to the web server. Furthermore, users can retrieve pre-processed analyses of families like multiple alignments and phylogenetic trees. New query options comprise a batch retrieval tool for functional inference about families based on automatic keyword extraction from sequence annotations. A new access point, PhyloMatrix, allows the retrieval of phylogenetic profiles of SYSTERS families across organisms with completely sequenced genomes.

Nucleic Acids Res. 2005:33(Database issue) | 23 Citations (from Europe PMC, 2025-03-15)

URL:	http://systers.molgen.mpg.de
Full name:
Description:	The SYSTERS web server (http://systers.molgen.mpg.de) provides access to 158 153 SYSTERS protein families.
Year founded:	2005
Last update:	2005-01-04
Version:	v1.0
Accessibility:	Unaccessible
Country/Region:	Germany

Data type:	Protein
Data object:	NA
Database category:	Gene genome and annotation Phylogeny and homology
Major species:	NA
Keywords:	multiple alignments phylogenetic trees protein family

University/Institution:	Max Planck Institute for Molecular Genetics
Address:	Ihnestrasse 63-73, 14195 Berlin, Germany
City:	Berlin
Province/State:
Country/Region:	Germany
Contact name (PI/Team):	Eike Staub
Contact email (PI/Helpdesk):	Eike.Staub@molgen.mpg.de

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Database Commons a catalog of worldwide biological databases