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Nucleic Acids Research, 2003, Vol. 31, No. 1 348-352
© 2003 Oxford University Press

ProtoNet: hierarchical classification of the protein space

Ori Sasson, Avishay Vaaknin, Hillel Fleischer, Elon Portugaly, Yonatan Bilu, Nathan Linial and Michal Linial*,1

School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel 1 Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel

*To whom correspondence should be addressed. Tel: +972 26585425; Fax: +972 26586448; Email: michall{at}mail.ls.huji.ac.il

ABSTRACT

The ProtoNet site provides an automatic hierarchical clustering of the SWISS-PROT protein database. The clustering is based on an all-against-all BLAST similarity search. The similarities' E-score is used to perform a continuous bottom-up clustering process by applying alternative rules for merging clusters. The outcome of this clustering process is a classification of the input proteins into a hierarchy of clusters of varying degrees of granularity. ProtoNet (version 1.3) is accessible in the form of an interactive web site at http://www.protonet.cs.huji.ac.il. ProtoNet provides navigation tools for monitoring the clustering process with a vertical and horizontal view. Each cluster at any level of the hierarchy is assigned with a statistical index, indicating the level of purity based on biological keywords such as those provided by SWISS-PROT and InterPro. ProtoNet can be used for function prediction, for defining superfamilies and subfamilies and for large-scale protein annotation purposes.


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