SVM based prediction of bacterial transcription start sites

Authors:
James Gordon;Michael Towsey
Affiliations:
Centre for Information Technology and Innovation, Faculty of Information Technology, Queensland University of Technology, Brisbane, Australia;Centre for Information Technology and Innovation, Faculty of Information Technology, Queensland University of Technology, Brisbane, Australia
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

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Abstract

Identifying bacterial promoters is the key to understanding gene expression. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). Knowing the TSS position, one can predict promoter positions to within a few base pairs, and vice versa. As a route to promoter identification, we formally address the problem of TSS prediction, drawing on the RegulonDB database of known (mapped) Escherichia coli TSS locations. The accepted method of finding promoters (and therefore TSSs) is to use position weight matrices (PWMs). We use an alternative approach based on support vector machines (SVMs). In particular, we quantify performance of several SVM models versus a PWM approach, using area under the detection-error tradeoff (DET) curve as a performance metric. SVM models are shown to outperform the PWM at TSS prediction, and to substantially reduce numbers of false positives, which are the bane of this problem.