Functional annotation prediction: All for one and one for all

Authors

  • Ori Sasson,

    1. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
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    • These authors contributed equally to this work.

  • Noam Kaplan,

    1. Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
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    • These authors contributed equally to this work.

  • Michal Linial

    Corresponding author
    1. Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
    • Michal Linial, CCB, The Sudarsky Center for Computational Biology, Department of Biological Chemistry, Life Science Institute, The Hebrew University, Jerusalem 91904, Israel; fax: 972-2-6585448.
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Abstract

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.

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