Functional annotation prediction: All for one and one for all

Ori Sasson, Noam Kaplan, Michal Linial*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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.

Original languageAmerican English
Pages (from-to)1557-1562
Number of pages6
JournalProtein Science
Volume15
Issue number6
DOIs
StatePublished - Jun 2006

Keywords

  • Clustering
  • Hierarchical classification
  • InterPro
  • Protein family

Fingerprint

Dive into the research topics of 'Functional annotation prediction: All for one and one for all'. Together they form a unique fingerprint.

Cite this