Bioinformatics Advance Access published online on September 28, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti047
Bioinformatics © Oxford University Press 2004; all rights reserved
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1 Department of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW 2522, Australia
* To whom correspondence should be addressed. E-mail: alh98{at}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 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 E.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.
Revised September 23, 2004
Accepted September 24, 2004
Article
Improving promoter prediction for the NNPP2.2 algorithm: a case study using E-Coli DNA sequences
2 Department of Biological Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
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