Skip Navigation

Bioinformatics 2005 21(Suppl 1):i283-i291; doi:10.1093/bioinformatics/bti1025
This Article
Right arrow FREE Full Text (Print PDF) Freely available
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 Mahony, S.
Right arrow Articles by Benos, P. V.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Mahony, S.
Right arrow Articles by Benos, P. V.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Improved detection of DNA motifs using a self-organized clustering of familial binding profiles

Shaun Mahony 1,*, Aaron Golden 1,2, Terry J. Smith 1 and Panayiotis V. Benos 3

1National Centre for Biomedical Engineering Science, NUI Galway Galway, Ireland
2Department of Information Technology, NUI Galway Galway, Ireland
3Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh Cancer Institute and Department of Computational Biology, School of Medicine, University of Pittsburgh Pittsburgh, PA 15213, USA

*To whom correspondence should be addressed.

Motivation: One of the limiting factors in deciphering transcriptional regulatory networks is the effectiveness of motif-finding software. An emerging avenue for improving motif-finding accuracy aims to incorporate generalized binding constraints of related transcription factors (TFs), named familial binding profiles (FBPs), as priors in motif identification methods. A motif-finder can thus be ‘biased’ towards finding motifs from a particular TF family. However, current motif-finders allow only a single FBP to be used as a prior in a given motif-finding run. In addition, current FBP construction methods are based on manual clustering of position specific scoring matrices (PSSMs) according to the known structural properties of the TF proteins. Manual clustering assumes that the binding preferences of structurally similar TFs will also be similar. This assumption is not true, at least not for some TF families. Automatic PSSM clustering methods are thus required for augmenting the usefulness of FBPs.

Results: A novel method is developed for automatic clustering of PSSM models. The resulting FBPs are incorporated into the SOMBRERO motif-finder, significantly improving its performance when finding motifs related to those that have been incorporated. SOMBRERO is thus the only existing de novo motif-finder that can incorporate knowledge of all known PSSMs in a given motif-finding run.

Availability: The methods outlined will be incorporated into the next release of SOMBRERO, which is available from http://bioinf.nuigalway.ie/sombrero

Contact: shaun.mahony{at}nuigalway.ie.


Received on January 15, 2005; accepted on March 27, 2005

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
U. J. Pape, S. Rahmann, and M. Vingron
Natural similarity measures between position frequency matrices with an application to clustering
Bioinformatics, February 1, 2008; 24(3): 350 - 357.
[Abstract] [Full Text] [PDF]


Home page
Nucleic Acids ResHome page
S. Mahony and P. V. Benos
STAMP: a web tool for exploring DNA-binding motif similarities
Nucleic Acids Res., July 13, 2007; 35(suppl_2): W253 - W258.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
S. Mahony, P. E. Auron, and P. V. Benos
Inferring protein DNA dependencies using motif alignments and mutual information
Bioinformatics, July 1, 2007; 23(13): i297 - i304.
[Abstract] [Full Text] [PDF]


Home page
Proc. Natl. Acad. Sci. USAHome page
A. V. Morozov and E. D. Siggia
Connecting protein structure with predictions of regulatory sites
PNAS, April 24, 2007; 104(17): 7068 - 7073.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
K. D. MacIsaac, D. B. Gordon, L. Nekludova, D. T. Odom, J. Schreiber, D. K. Gifford, R. A. Young, and E. Fraenkel
A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data
Bioinformatics, February 15, 2006; 22(4): 423 - 429.
[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.