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

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...

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Bibliographic Details
Published in:Bioinformatics 2005-04, Vol.21 (8), p.1332-1338
Main Authors: Degroeve, Sven, Saeys, Yvan, De Baets, Bernard, Rouzé, Pierre, Van de Peer, Yves
Format: Article
Language:English
Subjects:
Algorithms
Arabidopsis - genetics
Arabidopsis thaliana
Artificial Intelligence
Biological and medical sciences
Chromosome Mapping - methods
Databases, Genetic
Fundamental and applied biological sciences. Psychology
General aspects
Genome, Human
Humans
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Models, Statistical
RNA Splice Sites - genetics
Sequence Alignment - methods
Sequence Analysis, DNA - methods
Software
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Summary: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://www.bioinformatics.psb.ugent.be/supplementary_data/ Contact: sven.degroeve@psb.ugent.be; yves.vandepeer@psb.ugent.be
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bti166