title: GolgiP: prediction of Golgi-resident proteins in plants.
abstract: 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.
  
  
  BACKGROUND
  The GolgiP web service is publically available at http://csbl1.bmb.uga.edu/GolgiP/.
authors: Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, BioEnergy Science Center, GA, USA.
authors: Wen-Chi
authors: WC
authors: Chou
authors: Yanbin
authors: Y
authors: Yin
authors: Ying
authors: Y
authors: Xu
mesh_terms: D030562:Databases, Protein; D006056:Golgi Apparatus / Q000737:chemistry*; D008565:Membrane Proteins / Q000737:chemistry*; D010940:Plant Proteins / Q000737:chemistry*; D017434:Protein Structure, Tertiary; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
chemical_list: D008565:Membrane Proteins; D010940:Plant Proteins