Predicting splice sites from high-dimensional local context representations.

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.

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.

Results, figures and software are available at


Degroeve, S., Saeys, Y., De Baets, B., Rouzé, P., Van de Peer, Y. (2005) Predicting splice sites from high-dimensional local context representations. Bioinformatics 21(8):1332-8.

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