ProtoNet: Hierarchical classification of the protein space

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

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

65 Scopus citations


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 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.

Original languageAmerican English
Pages (from-to)348-352
Number of pages5
JournalNucleic Acids Research
Issue number1
StatePublished - 1 Jan 2003

Bibliographical note

Funding Information:
This work was supported by the Israeli Ministry of Defense, the Israeli Ministry of Science and by the Horowitz Foundation.


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