Improved prediction of bacterial transcription start sites | Zendy

J Gordon | Zendy; Michael Towsey | Zendy; James M. Hogan | Zendy; Sarah Mathews | Zendy; Peter Timms | Zendy

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Improved prediction of bacterial transcription start sites

Author(s) -

J Gordon,

Michael Towsey,

James M. Hogan,

Sarah Mathews,

Peter Timms

Publication year - 2005

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti771

Subject(s) - false positive paradox , computer science , support vector machine , classifier (uml) , promoter , artificial intelligence , bacterial transcription , machine learning , string (physics) , ensemble forecasting , task (project management) , ensemble learning , data mining , pattern recognition (psychology) , gene , biology , mathematics , genetics , gene expression , management , economics , mathematical physics

Identifying bacterial promoters is an important step towards understanding gene regulation. In this paper, we address the problem of predicting the location of promoters and their transcription start sites (TSSs) in Escherichia coli. The accepted method for this problem is to use position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in large numbers of false positive predictions.

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