Title

Functional Annotation Prediction: All for One and One for All

Publication Type

Journal Article

Publication Date

2006

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.

Discipline

Bioinformatics | Computer Sciences

Research Areas

Intelligent Systems and Decision Analytics

Publication

Protein Science

Volume

15

Issue

6

First Page

1557

Last Page

1562

ISSN

0961-8368

Identifier

10.1110/ps.062185706

Publisher

Wiley

Additional URL

http://dx.doi.org/10.1110/ps.062185706