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AUTOMATED FUNCTION PREDICTION

Functional annotation prediction: All for one and one for all

¹ School of Computer Science and Engineering
² Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904, Israel

(RECEIVED February 23, 2006; FINAL REVISION February 23, 2006; ACCEPTED February 23, 2006)

In an era of rapid genome sequencing and high-throughput technology, automatic function prediction for a novel sequence is of utter importance in bioinformatics. While automatic annotation methods based on local alignment searches can be simple and straightforward, they suffer from several drawbacks, including relatively low sensitivity and assignment of incorrect annotations that are not associated with the region of similarity. ProtoNet is a hierarchical organization of the protein sequences in the UniProt database. Although the hierarchy is constructed in an unsupervised automatic manner, it has been shown to be coherent with several biological data sources. We extend the ProtoNet system in order to assign functional annotations automatically. By leveraging on the scaffold of the hierarchical classification, the method is able to overcome some frequent annotation pitfalls.

Keywords: protein family; hierarchical classification; InterPro; clustering

Reprint requests to: Michal Linial, CCB, The Sudarsky Center for Computational Biology, Department of Biological Chemistry, Life Science Institute, The Hebrew University, Jerusalem 91904, Israel; e-mail: michall{at}cc.huji.ac.il; fax: 972-2-6585448.

Article published online ahead of print. Article and publication date are at http://www.proteinscience.org/cgi/doi/10.1110/ps.062185706.

Abbreviations GAS1, growth arrest sequence 1; GDNF, glial-cell-line-derived neurotrophic factor; GFR, GDNF family receptor; GPI, glycosyl phosphatidylinositol; HMM, hidden Markov model; PSSM, position-specific scoring matrix.

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