title: Features for computational operon prediction in prokaryotes.
abstract: Accurate prediction of operons can improve the functional annotation and application of genes within operons in prokaryotes. Here, we review several features: (i) intergenic distance, (ii) metabolic pathways, (iii) homologous genes, (iv) promoters and terminators, (v) gene order conservation, (vi) microarray, (vii) clusters of orthologous groups, (viii) gene length ratio, (ix) phylogenetic profiles, (x) operon length/size and (xi) STRING database scores, as well as some other features, which have been applied in recent operon prediction methods in prokaryotes in the literature. Based on a comparison of the prediction performances of these features, we conclude that other, as yet undiscovered features, or feature selection with a receiver operating characteristic analysis before algorithm processing can improve operon prediction in prokaryotes.
authors: Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Taiwan.
authors: Li-Yeh
authors: LY
authors: Chuang
authors: Hsueh-Wei
authors: HW
authors: Chang
authors: Jui-Hung
authors: JH
authors: Tsai
authors: Cheng-Hong
authors: CH
authors: Yang
mesh_terms: D000465:Algorithms; D001412:Bacillus subtilis / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D016680:Genome, Bacterial; D020411:Oligonucleotide Array Sequence Analysis; D009876:Operon; D010802:Phylogeny; D011387:Prokaryotic Cells / Q000166:cytology*; D011401:Promoter Regions, Genetic; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity