ProtoNet 4.0: A hierarchical classification of one million protein sequences

Noam Kaplan*, Ori Sasson, Uri Inbar, Moriah Friedlinch, Menachem Fromer, Hillel Fleischer, Elon Portugaly, Nathan Linial, Michal Linial

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

Abstract

ProtoNet is an automatic hierarchical classification of the protein sequence space. In 2004, the ProtoNet (version 4.0) presents the analysis of over one million proteins merged from SwissProt and TrEMBL databases. In addition to rich visualization and analysis tools to navigate the clustering hierarchy, we incorporated several improvements that allow a simplified view of the scaffold of the proteins. An unsupervised, biologically valid method that was developed resulted in a condensation of the ProtoNet hierarchy to only 12% of the clusters. A large portion of these clusters was automatically assigned high confidence biological names according to their correspondence with functional annotations. ProtoNet is available at: http://www.protonet.cs.huji.ac.il.

Original languageAmerican English
Pages (from-to)D216-D218
JournalNucleic Acids Research
Volume33
Issue numberDATABASE ISS.
DOIs
StatePublished - 1 Jan 2005

Bibliographical note

Funding Information:
We thank the entire current and previous ProtoNet teams for their endless support. Special thanks to Alex Savenok for the web design as well as for the development of the visualization tools. We thank the fellowship support by the Sudarsky Center for Computational Biology (SCCB) to N.K., U.I., M.F. and M.F.. This study is partially supported by the EU NoE BioSapiens consortium and the CESG consortium supported by the NIH.

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