Super paramagnetic clustering of protein sequences.
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.


CONCLUSIONS
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.
Tetko
Igor V
IV
GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany. i.tetko@gsf.de
Facius
Axel
A
Ruepp
Andreas
A
Mewes
Hans-Werner
HW
D000465:Algorithms; D001412:Bacillus subtilis / Q000378:metabolism; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003198:Computer Simulation; D003628:Database Management Systems; D030541:Databases, Genetic; D030561:Databases, Nucleic Acid; D030562:Databases, Protein; D005798:Genes, Bacterial; D016678:Genome; D016680:Genome, Bacterial; D016480:Helicobacter pylori / Q000378:metabolism; D016247:Information Storage and Retrieval; D008087:Listeria / Q000378:metabolism; D008089:Listeria monocytogenes / Q000378:metabolism; D008280:Magnetics; D008390:Markov Chains; D009010:Monte Carlo Method; D011381:Programming Languages; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins