A set-theoretic approach to database searching and clustering.
BACKGROUND
In this paper, we introduce an iterative method of database searching and apply it to design a database clustering algorithm applicable to an entire protein database. The clustering procedure relies on the quality of the database searching routine and further improves its results based on a set-theoretic analysis of a highly redundant yet efficient to generate cluster system.


RESULTS
Overall, we achieve unambiguous assignment of 80% of SWISS-PROT sequences to non-overlapping sequence clusters in an entirely automatic fashion. Our results are compared to an expert-generated clustering for validation. The database searching method is fast and the clustering technique does not require time-consuming all-against-all comparison. This allows for fast clustering of large amounts of sequences.


BACKGROUND
The resulting clustering for the PIR1 (Release 51) and SWISS-PROT (Release 34) databases is available over the Internet from http://www.dkfz-heidelberg.de/tbi/services/modest/b rowsesysters.pl.


BACKGROUND
a.krause@dkfz-heidelberg.de; m.vingron@dkfz-heidelberg.de
Deutsches Krebsforschungszentrum (DKFZ), Theoretische Bioinformatik, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. a.krause@dkfz-heidelberg.de
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Krause
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Vingron
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D016208:Databases, Factual; D011506:Proteins / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software
D011506:Proteins