Skip Navigation



Bioinformatics Advance Access published online on April 7, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti402
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/11/2636    most recent
bti402v1
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 Hong, P.
Right arrow Articles by Wong, W. H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Hong, P.
Right arrow Articles by Wong, W. H.
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@oupjournals.org
Received July 30, 2004
Revised January 10, 2005
Accepted March 21, 2005

Article

A boosting approach for motif modeling using ChIP-chip data

Pengyu Hong 1, X. Shirley Liu 2, Qing Zhou 1, Xin Lu 2, Jun S. Liu 3, and Wing H. Wong 3*

1 Department of Statistics, Harvard University, Cambridge, MA 02138
2 Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115
3 Department of Statistics, Harvard University, Cambridge, MA 02138; Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115

* To whom correspondence should be addressed.
Wing H. Wong, E-mail: wwong{at}hsph.harvard.edu


  Abstract

Motivation: Building an accurate binding model for a transcription factor (TF) is essential to differentiate its true binding targets from those spurious ones. This is an important step towards the understanding of gene regulation.

Results: This paper describes a boosting approach for modeling TF-DNA binding. Different from the widely used weight matrix model, which predicts TF-DNA binding based on a linear combination of position-specific contributions, our approach builds a TF binding classifier by combining a set of weight-matrix-based classifiers, thus yielding a non-linear binding decision rule. The proposed approach is applied to the ChIP-chip data of Saccharomyces cerevisiae. When compared to the weight matrix method, our new approach shows significant improvements on the specificity in a majority of cases.

Supplementary Information: The software and the supplementary data are available at http://bayes.fas.harvard.edu/MotifBooster.


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
 Nucleic Acids ResHome page
Q. Zhou and J. S. Liu
Extracting sequence features to predict protein-DNA interactions: a comparative study
Nucleic Acids Res., July 1, 2008; 36(12): 4137 - 4148.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
X. Chen, L. Guo, Z. Fan, and T. Jiang
W-AlignACE: an improved Gibbs sampling algorithm based on more accurate position weight matrices learned from sequence and gene expression/ChIP-chip data
Bioinformatics, May 1, 2008; 24(9): 1121 - 1128.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
C.-Y. Chen, H.-K. Tsai, C.-M. Hsu, M.-J. May Chen, H.-G. Hung, G. T.-W. Huang, and W.-H. Li
Discovering gapped binding sites of yeast transcription factors
PNAS, February 19, 2008; 105(7): 2527 - 2532.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
B. Jiang, M. Q. Zhang, and X. Zhang
OSCAR: One-class SVM for accurate recognition of cis-elements
Bioinformatics, November 1, 2007; 23(21): 2823 - 2828.
[Abstract] [Full Text] [PDF]

Home page
 Genome ResHome page
V. X. Jin, H. O'Geen, S. Iyengar, R. Green, and P. J. Farnham
Identification of an OCT4 and SRY regulatory module using integrated computational and experimental genomics approaches
Genome Res., June 1, 2007; 17(6): 807 - 817.
[Abstract] [Full Text] [PDF]

Home page
 Genome ResHome page
L. Elnitski, V. X. Jin, P. J. Farnham, and S. J.M. Jones
Locating mammalian transcription factor binding sites: A survey of computational and experimental techniques
Genome Res., December 1, 2006; 16(12): 1455 - 1464.
[Abstract] [Full Text] [PDF]

Home page
 Genome ResHome page
V. X. Jin, A. Rabinovich, S. L. Squazzo, R. Green, and P. J. Farnham
A computational genomics approach to identify cis-regulatory modules from chromatin immunoprecipitation microarray data--A case study using E2F1
Genome Res., December 1, 2006; 16(12): 1585 - 1595.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
D. GuhaThakurta
Computational identification of transcriptional regulatory elements in DNA sequence
Nucleic Acids Res., July 19, 2006; 34(12): 3585 - 3598.
[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.