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


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


BACKGROUND
sven.degroeve@psb.ugent.be; yves.vandepeer@psb.ugent.be.
Degroeve
Sven
S
Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Technologiepark 927, Gent 9052, Belgium. sven.degroeve@psb.ugent.be
Saeys
Yvan
Y
De Baets
Bernard
B
Rouzé
Pierre
P
Van de Peer
Yves
Y
D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics*; D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D030541:Databases, Genetic; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D022821:RNA Splice Sites / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
D022821:RNA Splice Sites