Ontology highlight
ABSTRACT: Unlabelled
We present a novel Golgi-prediction server, GolgiP, for computational prediction of both membrane- and non-membrane-associated Golgi-resident proteins in plants. We have employed a support vector machine-based classification method for the prediction of such Golgi proteins, based on three types of information, dipeptide composition, transmembrane domain(s) (TMDs) and functional domain(s) of a protein, where the functional domain information is generated through searching against the Conserved Domains Database, and the TMD information includes the number of TMDs, the length of TMD and the number of TMDs at the N-terminus of a protein. Using GolgiP, we have made genome-scale predictions of Golgi-resident proteins in 18 plant genomes, and have made the preliminary analysis of the predicted data.Availability
The GolgiP web service is publically available at http://csbl1.bmb.uga.edu/GolgiP/.
SUBMITTER: Chou WC
PROVIDER: S-EPMC2944200 | biostudies-literature | 2010 Oct
REPOSITORIES: biostudies-literature
Chou Wen-Chi WC Yin Yanbin Y Xu Ying Y
Bioinformatics (Oxford, England) 20100823 19
<h4>Unlabelled</h4>We present a novel Golgi-prediction server, GolgiP, for computational prediction of both membrane- and non-membrane-associated Golgi-resident proteins in plants. We have employed a support vector machine-based classification method for the prediction of such Golgi proteins, based on three types of information, dipeptide composition, transmembrane domain(s) (TMDs) and functional domain(s) of a protein, where the functional domain information is generated through searching again ...[more]