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

Sven Degroeve UGent, Yvan Saeys UGent, Bernard De Baets UGent, Pierre Rouzé and Yves Van de Peer UGent (2005) BIOINFORMATICS. 21(8). p.1332-1338
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
GENOMIC DNA, GENE-PREDICTION, SEQUENCE
journal title
BIOINFORMATICS
Bioinformatics
volume
21
issue
8
pages
1332 - 1338
Web of Science type
Article
Web of Science id
000228401800008
JCR category
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
JCR impact factor
6.019 (2005)
JCR rank
1/83 (2005)
JCR quartile
1 (2005)
ISSN
1367-4803
DOI
10.1093/bioinformatics/bti166
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
305227
handle
http://hdl.handle.net/1854/LU-305227
date created
2005-05-10 16:31:00
date last changed
2016-12-19 15:45:35
@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},
}

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.