WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

Citation data:

Bioinformatics (Oxford, England), ISSN: 1367-4811, Vol: 28, Issue: 7, Page: 1028-30

Publication Year:
2012
Usage 112
Abstract Views 112
Captures 36
Readers 35
Exports-Saves 1
Citations 32
Citation Indexes 32
Repository URL:
http://scholarworks.unist.ac.kr/handle/201301/2895
PMID:
22296788
DOI:
10.1093/bioinformatics/bts062
Author(s):
Chi, Sang-Mun; Nam, Dougu
Publisher(s):
Oxford University Press (OUP); OXFORD UNIV PRESS
Tags:
Biochemistry, Genetics and Molecular Biology; Computer Science; Mathematics; Medicine; SEQUENCE
article description
We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method.