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Nucleic Acids Research 2005 33(3):1069-1076; doi:10.1093/nar/gki247
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Published online 18 February 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

Plant promoter prediction with confidence estimation

I. A. Shahmuradov1, V. V. Solovyev1,2,* and A. J. Gammerman1

1 Royal Holloway, University of London Egham, Surrey TW20 0EX, UK 2 Softberry Inc. 116 Radio Circle, Suite 400, Mount Kisco, NY 10549, USA

*To whom correspondence should be addressed. Tel: +44 1784 443673; Email: victor{at}cs.rhul.ac.uk

Received September 28, 2004. Revised December 15, 2004. Accepted January 24, 2005.

Accurate prediction of promoters is fundamental to understanding gene expression patterns, where confidence estimation is one of the main requirements. Using recently developed transductive confidence machine (TCM) techniques, we developed a new program TSSP-TCM for the prediction of plant promoters that also provides confidence of the prediction. The program was trained on 132 and 104 sequences and tested on 40 and 25 sequences (containing TATA and TATA-less promoters, respectively) with known transcription start sites (TSSs). As negative training samples for TCM learning we used coding and intron sequences of plant genes annotated in the GenBank. In the test set of TATA promoters, the program correctly predicted TSS for 35 out of 40 (87.5%) genes with a median deviation of several base pairs from the true site location. For 25 TATA-less promoters, TSSs were predicted for 21 out of 25 (84%) genes, including 14 cases of 5 bp distance between annotated and predicted TSSs. Using TSSP-TCM program we annotated promoters in the whole Arabidopsis genome. The predicted promoters were in good agreement with the start position of known Arabidopsis mRNAs. Thus, TCM technique has produced a plant-oriented promoter prediction tool of high accuracy. TSSP-TCM program and annotated promoters are available at http://mendel.cs.rhul.ac.uk/mendel.php?topic=fgen.

Correspondence may also be addressed to I. A. Shahmuradov. Tel: +99 412 4975009; Email: ilham{at}cs.rhul.ac.uk


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