PuSH - Publikationsserver des Helmholtz Zentrums München

Tetko, I.V. ; Facius, A. ; Ruepp, A. ; Mewes, H.-W.

Super paramagnetic clustering of protein sequences.

BMC Bioinformatics 6:82 (2005)
Verlagsversion Volltext DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
BACKGROUND: Detection of sequence homologues represents a challenging task that is important for the discovery of protein families and the reliable application of automatic annotation methods. The presence of domains in protein families of diverse function, inhomogeneity and different sizes of protein families create considerable difficulties for the application of published clustering methods. RESULTS: Our work analyses the Super Paramagnetic Clustering (SPC) and its extension, global SPC (gSPC) algorithm. These algorithms cluster input data based on a method that is analogous to the treatment of an inhomogeneous ferromagnet in physics. For the SwissProt and SCOP databases we show that the gSPC improves the specificity and sensitivity of clustering over the original SPC and Markov Cluster algorithm (TRIBE-MCL) up to 30%. The three algorithms provided similar results for the MIPS FunCat 1.3 annotation of four bacterial genomes, Bacillus subtilis, Helicobacter pylori, Listeria innocua and Listeria monocytogenes. However, the gSPC covered about 12% more sequences compared to the other methods. The SPC algorithm was programmed in house using C++ and it is available at http://mips.gsf.de/proj/spc. The FunCat annotation is available at http://mips.gsf.de. CONCLUSION: The gSPC calculated to a higher accuracy or covered a larger number of sequences than the TRIBE-MCL algorithm. Thus it is a useful approach for automatic detection of protein families and unsupervised annotation of full genomes.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter NEURAL-NETWORK; GENE-EXPRESSION; WHOLE GENOMES; YEAST GENOME; DATABASE; CLASSIFICATION; ANNOTATION; ALGORITHM; FAMILIES; GENERATION
ISSN (print) / ISBN 1471-2105
e-ISSN 1471-2105
Zeitschrift BMC Bioinformatics
Quellenangaben Band: 6, Heft: , Seiten: , Artikelnummer: 82 Supplement: ,
Verlag BioMed Central
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)