RBF-TSS: Identification of Transcription Start Site in Human Using Radial Basis Functions Network and Oligonucleotide Positional Frequencies

• Published in: PloS one Vol. 4; no. 3; p. e4878
• Main Authors: Mahdi, Rami N., Rouchka, Eric C.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.03.2009; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial intelligence; Artificial neural networks; Basis functions; Binding sites; Computational Biology; Computational Biology/Comparative Sequence Analysis; Computational Biology/Sequence Motif Analysis; Computational Biology/Transcriptional Regulation; Computer engineering; Computer science; Deoxyribonucleic acid; DNA; Feature based; Gene expression; Gene regulation; Genomes; Genomics; Humans; Identification methods; Information processing; International conferences; Neural networks; Oligonucleotides; Oligonucleotides - genetics; Operators (mathematics); Progress reports; Promoters; Radial basis function; Transcription; Transcription (Genetics); Transcription, Genetic; United States > US; Kentucky
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0004878

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.