title: RBF-TSS: identification of transcription start site in human using radial basis functions network and oligonucleotide positional frequencies.
abstract: Accurate identification of promoter regions and transcription start sites (TSS) in genomic DNA allows for a more complete understanding of the structure of genes and gene regulation within a given genome. Many recently published methods have achieved high identification accuracy of TSS. However, models providing more accurate modeling of promoters and TSS are needed. A novel identification method for identifying transcription start sites that improves the accuracy of TSS recognition for recently published methods is proposed. This method incorporates a metric feature based on oligonucleotide positional frequencies, taking into account the nature of promoters. A radial basis function neural network for identifying transcription start sites (RBF-TSS) is proposed and employed as a classification algorithm. Using non-overlapping chunks (windows) of size 50 and 500 on the human genome, the proposed method achieves an area under the Receiver Operator Characteristic curve (auROC) of 94.75% and 95.08% respectively, providing increased performance over existing TSS prediction methods.
authors.affiliation: Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA.
authors.forename: Rami N
authors.initials: RN
authors.lastname: Mahdi
authors.forename: Eric C
authors.initials: EC
authors.lastname: Rouchka
mesh_terms: D006801:Humans; D009841:Oligonucleotides / Q000235:genetics*; D014158:Transcription, Genetic
chemical_list: D009841:Oligonucleotides