Skip Navigation



Bioinformatics Advance Access published online on September 16, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti025
Bioinformatics © Oxford University Press 2004; all rights reserved
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/4/471    most recent
bti025v1
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 Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Chen, T.-M.
Right arrow Articles by Li, W.-H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Chen, T.-M.
Right arrow Articles by Li, W.-H.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Received October 27, 2003
Revised August 11, 2004
Accepted May 3, 2004

Article

Prediction of splice sites with dependency graphs and their expanded bayesian networks

Te-Ming Chen 1, Chung-Chin Lu 1*, and Wen-Hsiung Li 2

1 Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
2 Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA

* To whom correspondence should be addressed. E-mail: cclu{at}ee.nthu.edu.tw.


  Abstract

Motivation: Thanks to the complete sequencing of the human and many other genomes, huge amounts of DNA sequence data have already been accumulated. In bioinformatics, an important issue is how to predict the complete structure of genes from the genomic DNA sequence, especially in the human genome. A crucial part in gene structure prediction is to determine the precise exon-intron boundaries, i.e., the splice sites, in the coding region.

Results: We have developed a dependency graph model to fully capture the intrinsic inter-dependency between base positions in a splice site. The establishment of dependency between two positions is based on a chi-square test from known sample data. To facilitate statistical inference, we have expanded the dependency graph (which is usually a graph with cycles that make probabilistic reasoning very difficult, if not impossible) into a Bayesian network (which is a directed acyclic graph that facilitates statistical resoning).

Compared with existing models such as WMM, WAM, MDD, Cai et al.'s Tree model in the literature as well as the second-order and third-order Markov chain models, which has not been well studied in the literature, the expanded Bayesian networks from our dependency graph models perform the best in nearly all cases studied.

Availability: Software (a program called DGSplicer) and datasets used are available at http://csrl.ee.nthu.edu.tw/bioinf/.


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


This article has been cited by other articles:


Home page
 BioinformaticsHome page
S. Sonnenburg, A. Zien, P. Philips, and G. Ratsch
POIMs: positional oligomer importance matrices--understanding support vector machine-based signal detectors
Bioinformatics, July 1, 2008; 24(13): i6 - i14.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
S. Nikolajewa, R. Pudimat, M. Hiller, M. Platzer, and R. Backofen
BioBayesNet: a web server for feature extraction and Bayesian network modeling of biological sequence data
Nucleic Acids Res., July 13, 2007; 35(suppl_2): W688 - W693.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.