title: Predict prokaryotic proteins through detecting N-formylmethionine residues in protein sequences using support vector machine.
abstract: Identifying prokaryotes in silico is commonly based on DNA sequences. In experiments where DNA sequences may not be immediately available, we need to have a different approach to detect prokaryotes based on RNA or protein sequences. N-formylmethionine (fMet) is known as a typical characteristic of prokaryotes. A web tool has been implemented here for predicting prokaryotes through detecting the N-formylmethionine residues in protein sequences. The predictor is constructed using support vector machine. An online predictor has been implemented using Python. The implemented predictor is able to achieve the total prediction accuracy 80% with the specificity 80% and the sensitivity 81%.
authors.affiliation: School of Biosciences, University of Exeter, Hatherly Building, Exeter, UK. Z.R.Yang@exeter.ac.uk
authors.forename: Zheng Rong
authors.initials: ZR
authors.lastname: Yang
mesh_terms: D002965:Classification / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D020407:Internet; D009239:N-Formylmethionine / Q000032:analysis*; D011387:Prokaryotic Cells / Q000737:chemistry*; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D011506:Proteins; D009239:N-Formylmethionine