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  Large Scale Hierarchical Clustering of Protein Sequences

Krause, A., Stoye, J., & Vingron, M. (2005). Large Scale Hierarchical Clustering of Protein Sequences. BMC Bioinformatics, 6, 15-15. doi:10.1186/1471-2105-6-15.

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Datensatz-Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-870E-A Versions-Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-870F-8
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SYSTERS Large-scale Protein Clustering and Protein Family Database.htm (beliebiger Volltext), 11KB
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 Urheber:
Krause, Antje1, Autor
Stoye, Jens, Autor
Vingron, Martin2, Autor              
Affiliations:
1Max Planck Society, escidoc:persistent13              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, escidoc:1479639              

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 Zusammenfassung: 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.

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Sprache(n): eng - Englisch
 Datum: 2005-01-22
 Publikationsstatus: Im Druck publiziert
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 Ort, Verlag, Ausgabe: -
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 Identifikatoren: eDoc: 265192
DOI: 10.1186/1471-2105-6-15
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Titel: BMC Bioinformatics
Genre der Quelle: Zeitschrift
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Seiten: - Band / Heft: 6 Artikelnummer: - Start- / Endseite: 15 - 15 Identifikator: ISSN: 1471-2105