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Bioinformatics Advance Access published online on November 25, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti166
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received August 23, 2004
Revised October 25, 2004
Accepted November 18, 2004

Article

SpliceMachine: predicting splice sites from high-dimensional local context representations

Sven Degroeve 1*, Yvan Saeys 1, Bernard De Baets 2, Pierre Rouzé 3, and Yves Van de Peer 1

1 Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Technologiepark 927, Gent, 9052, Belgium
2 Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, Gent, 9000, Belgium
3 Laboratoire associé de l'INRA (France), Technologiepark 927, Gent, 9052, Belgium

* To whom correspondence should be addressed.
Sven Degroeve, E-mail: Sven.Degroeve{at}psb.ugent.be


  Abstract

Motivation: In this age of complete genome sequencing, finding the location and structure of genes is crucial for further molecular research. The accurate prediction of intron boundaries largely facilitates the correct prediction of gene structure in nuclear genomes. Many tools for localizing these boundaries on DNA sequences have been developed and are available to researchers through the internet. Nevertheless, these tools still make many false positive predictions.

Results: This manuscript presents a novel publicly available splice site prediction tool named SpliceMachine that (i) shows state-of-the-art prediction performance on Arabidopsis thaliana and human sequences, (ii) performs a computationally fast annotation, and (iii) can be trained by the user on its own data.

Availability: Results, figures and software are available at http://bioinformatics.psb.ugent.be/supplementary_data/.


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