Genome Research

Home Help [Feedback] [For Subscribers] [Archive] [Search] --
 QUICK SEARCH:   [advanced]


     


Published online before print December 20, 2007
Genome Research, DOI: 10.1101/gr.6991408
This Article
Right arrow Full Text (PDF)
Right arrow Supplemental Research Data
Right arrow All Versions of this Article:
gr.6991408v1
18/2/310    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Abeel, T.
Right arrow Articles by Van de Peer, Y.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Abeel, T.
Right arrow Articles by Van de Peer, Y.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Methods

Generic eukaryotic core promoter prediction using structural features of DNA

Thomas Abeel1,2, Yvan Saeys1,2, Eric Bonnet1,2, Pierre Rouzé1,2,3, and Yves Van de Peer1,2,4

1 Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), 9052 Gent, Belgium; 2 Department of Molecular Genetics, Ghent University, 9052 Gent, Belgium; 3 Laboratoire Associé de l’INRA (France), Ghent University, 9052 Gent, Belgium

Despite many recent efforts, in silico identification of promoter regions is still in its infancy. However, the accurate identification and delineation of promoter regions is important for several reasons, such as improving genome annotation and devising experiments to study and understand transcriptional regulation. Current methods to identify the core region of promoters require large amounts of high-quality training data and often behave like black box models that output predictions that are difficult to interpret. Here, we present a novel approach for predicting promoters in whole-genome sequences by using large-scale structural properties of DNA. Our technique requires no training, is applicable to many eukaryotic genomes, and performs extremely well in comparison with the best available promoter prediction programs. Moreover, it is fast, simple in design, and has no size constraints, and the results are easily interpretable. We compared our approach with 14 current state-of-the-art implementations using human gene and transcription start site data and analyzed the ENCODE region in more detail. We also validated our method on 12 additional eukaryotic genomes, including vertebrates, invertebrates, plants, fungi, and protists.


4 Corresponding author.

E-mail yves.vandepeer{at}psb.ugent.be; fax 32-(0)-9-33-13-809.

[Supplemental material is available online at www.genome.org.]

Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.6991408


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?


This article has been cited by other articles:


Home page
BioinformaticsHome page
J. R. Goni, C. Fenollosa, A. Perez, D. Torrents, and M. Orozco
DNAlive: a tool for the physical analysis of DNA at the genomic scale
Bioinformatics, August 1, 2008; 24(15): 1731 - 1732.
[Abstract] [PDF]


Home page
BioinformaticsHome page
T. Abeel, Y. Saeys, P. Rouze, and Y. Van de Peer
ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles
Bioinformatics, July 1, 2008; 24(13): i24 - i31.
[Abstract] [PDF]




Home Help [Feedback] [For Subscribers] [Archive] [Search] --
Genes Dev. Learn. Mem.
Protein Science RNA Genome Res.
Copyright © 2007 by Cold Spring Harbor Laboratory Press.