SpliceMachine: predicting splice sites from high-dimensional local context representations
- Author
- Sven Degroeve (UGent) , Yvan Saeys (UGent) , Bernard De Baets (UGent) , Pierre Rouzé (UGent) and Yves Van de Peer (UGent)
- Organization
- 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.
- Keywords
- GENOMIC DNA, GENE-PREDICTION, SEQUENCE
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-305227
- MLA
- Degroeve, Sven, et al. “SpliceMachine: Predicting Splice Sites from High-Dimensional Local Context Representations.” BIOINFORMATICS, vol. 21, no. 8, 2005, pp. 1332–38, doi:10.1093/bioinformatics/bti166.
- APA
- Degroeve, S., Saeys, Y., De Baets, B., Rouzé, P., & Van de Peer, Y. (2005). SpliceMachine: predicting splice sites from high-dimensional local context representations. BIOINFORMATICS, 21(8), 1332–1338. https://doi.org/10.1093/bioinformatics/bti166
- Chicago author-date
- Degroeve, Sven, Yvan Saeys, Bernard De Baets, Pierre Rouzé, and Yves Van de Peer. 2005. “SpliceMachine: Predicting Splice Sites from High-Dimensional Local Context Representations.” BIOINFORMATICS 21 (8): 1332–38. https://doi.org/10.1093/bioinformatics/bti166.
- Chicago author-date (all authors)
- Degroeve, Sven, Yvan Saeys, Bernard De Baets, Pierre Rouzé, and Yves Van de Peer. 2005. “SpliceMachine: Predicting Splice Sites from High-Dimensional Local Context Representations.” BIOINFORMATICS 21 (8): 1332–1338. doi:10.1093/bioinformatics/bti166.
- Vancouver
- 1.Degroeve S, Saeys Y, De Baets B, Rouzé P, Van de Peer Y. SpliceMachine: predicting splice sites from high-dimensional local context representations. BIOINFORMATICS. 2005;21(8):1332–8.
- IEEE
- [1]S. Degroeve, Y. Saeys, B. De Baets, P. Rouzé, and Y. Van de Peer, “SpliceMachine: predicting splice sites from high-dimensional local context representations,” BIOINFORMATICS, vol. 21, no. 8, pp. 1332–1338, 2005.
@article{305227,
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.}},
author = {{Degroeve, Sven and Saeys, Yvan and De Baets, Bernard and Rouzé, Pierre and Van de Peer, Yves}},
issn = {{1367-4803}},
journal = {{BIOINFORMATICS}},
keywords = {{GENOMIC DNA,GENE-PREDICTION,SEQUENCE}},
language = {{eng}},
number = {{8}},
pages = {{1332--1338}},
title = {{SpliceMachine: predicting splice sites from high-dimensional local context representations}},
url = {{http://doi.org/10.1093/bioinformatics/bti166}},
volume = {{21}},
year = {{2005}},
}
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