Advanced search
1 file | 75.17 KB

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

Sven Degroeve (UGent) , Yvan Saeys (UGent) , Bernard De Baets (UGent) , Pierre Rouzé (UGent) and Yves Van de Peer (UGent)
(2005) BIOINFORMATICS. 21(8). p.1332-1338
Author
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

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 75.17 KB

Citation

Please use this url to cite or link to this publication:

Chicago
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.
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.
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.
MLA
Degroeve, Sven, Yvan Saeys, Bernard De Baets, et al. “SpliceMachine: Predicting Splice Sites from High-dimensional Local Context Representations.” BIOINFORMATICS 21.8 (2005): 1332–1338. Print.
@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 Rouz{\'e}, Pierre and Van de Peer, Yves},
  issn         = {1367-4803},
  journal      = {BIOINFORMATICS},
  keyword      = {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://dx.doi.org/10.1093/bioinformatics/bti166},
  volume       = {21},
  year         = {2005},
}

Altmetric
View in Altmetric
Web of Science
Times cited: