S Burden1, Y-X Lin, R Zhang. 1. Department of Mathematics and Applied Statistics, University of Wollongong Wollongong, NSW 2522, Australia. alh98@uow.edu.au
Abstract
MOTIVATION: Although a great deal of research has been undertaken in the area of promoter prediction, prediction techniques are still not fully developed. Many algorithms tend to exhibit poor specificity, generating many false positives, or poor sensitivity. The neural network prediction program NNPP2.2 is one such example. RESULTS: To improve the NNPP2.2 prediction technique, the distance between the transcription start site (TSS) associated with the promoter and the translation start site (TLS) of the subsequent gene coding region has been studied for Escherichia coli K12 bacteria. An empirical probability distribution that is consistent for all E.coli promoters has been established. This information is combined with the results from NNPP2.2 to create a new technique called TLS-NNPP, which improves the specificity of promoter prediction. The technique is shown to be effective using E.coli DNA sequences, however, it is applicable to any organism for which a set of promoters has been experimentally defined. AVAILABILITY: The data used in this project and the prediction results for the tested sequences can be obtained from http://www.uow.edu.au/~yanxia/E_Coli_paper/SBurden_Results.xls CONTACT: alh98@uow.edu.au.
MOTIVATION: Although a great deal of research has been undertaken in the area of promoter prediction, prediction techniques are still not fully developed. Many algorithms tend to exhibit poor specificity, generating many false positives, or poor sensitivity. The neural network prediction program NNPP2.2 is one such example. RESULTS: To improve the NNPP2.2 prediction technique, the distance between the transcription start site (TSS) associated with the promoter and the translation start site (TLS) of the subsequent gene coding region has been studied for Escherichia coli K12 bacteria. An empirical probability distribution that is consistent for all E.coli promoters has been established. This information is combined with the results from NNPP2.2 to create a new technique called TLS-NNPP, which improves the specificity of promoter prediction. The technique is shown to be effective using E.coli DNA sequences, however, it is applicable to any organism for which a set of promoters has been experimentally defined. AVAILABILITY: The data used in this project and the prediction results for the tested sequences can be obtained from http://www.uow.edu.au/~yanxia/E_Coli_paper/SBurden_Results.xls CONTACT: alh98@uow.edu.au.
Authors: Peter D Karp; Wai Kit Ong; Suzanne Paley; Richard Billington; Ron Caspi; Carol Fulcher; Anamika Kothari; Markus Krummenacker; Mario Latendresse; Peter E Midford; Pallavi Subhraveti; Socorro Gama-Castro; Luis Muñiz-Rascado; César Bonavides-Martinez; Alberto Santos-Zavaleta; Amanda Mackie; Julio Collado-Vides; Ingrid M Keseler; Ian Paulsen Journal: EcoSal Plus Date: 2018-11