title: iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.
abstract: The σ(54) promoters are unique in prokaryotic genome and responsible for transcripting carbon and nitrogen-related genes. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the σ(54) promoters. Here, a predictor called 'iPro54-PseKNC' was developed. In the predictor, the samples of DNA sequences were formulated by a novel feature vector called 'pseudo k-tuple nucleotide composition', which was further optimized by the incremental feature selection procedure. The performance of iPro54-PseKNC was examined by the rigorous jackknife cross-validation tests on a stringent benchmark data set. As a user-friendly web-server, iPro54-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iPro54-PseKNC. For the convenience of the vast majority of experimental scientists, a step-by-step protocol guide was provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented in this paper just for its integrity. Meanwhile, we also discovered through an in-depth statistical analysis that the distribution of distances between the transcription start sites and the translation initiation sites were governed by the gamma distribution, which may provide a fundamental physical principle for studying the σ(54) promoters.
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China Gordon Life Science Institute, Belmont, MA, USA hlin@gordonlifescience.org hlin@uestc.edu.cn.
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: En-Ze
authors.initials: EZ
authors.lastname: Deng
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Ding
authors.affiliation: Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China Gordon Life Science Institute, Belmont, MA, USA hlin@gordonlifescience.org hlin@uestc.edu.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: Gordon Life Science Institute, Belmont, MA, USA Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia hlin@gordonlifescience.org hlin@uestc.edu.cn.
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D016680:Genome, Bacterial; D009711:Nucleotides / Q000737:chemistry; D010442:Peptide Chain Initiation, Translational; D011401:Promoter Regions, Genetic; D051877:RNA Polymerase Sigma 54 / Q000378:metabolism*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D024363:Transcription Initiation Site
chemical_list: D009711:Nucleotides; D051877:RNA Polymerase Sigma 54