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Bioinformatics Advance Access published online on December 6, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti815
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received September 14, 2005
Revised November 14, 2005
Accepted December 1, 2005

Article

A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data

Kenzie D. MacIsaac 1, D. Benjamin Gordon 2, Lena Nekludova 2, Duncan T. Odom 2, Joerg Schreiber 2, David K. Gifford 1, Richard A. Young 3, and Ernest Fraenkel 4 *

1 MIT Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA
2 Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA
3 Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
4 MIT Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA; Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA; Division of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

* To whom correspondence should be addressed.
Ernest Fraenkel, E-mail: efraenkel{at}wi.mit.edu


  Abstract

Motivation: Genome-wide chromatin-immunoprecipitation (ChIP-chip) detects binding of transcriptional regulators to DNA in vivo at low resolution. Motif discovery algorithms can be used to discover sequence patterns in the bound regions that may be recognized by the immunoprecipitated protein. However, the discovered motifs often do not agree with the binding specificity of the protein, when it is known.

Results: We present a powerful approach to analyzing ChIP-chip data, called THEME, that tests hypotheses concerning the sequence specificity of a protein. Hypotheses are refined using constrained local optimization. Cross-validation provides a principled standard for selecting the optimal weighting of the hypothesis and the ChIP-chip data and for choosing the best refined hypothesis. We demonstrate how to derive hypotheses for proteins from 36 domain families. Using THEME together with these hypotheses, we analyze ChIP-chip datasets for fourteen human and mouse proteins. In all cases the identified motifs are consistent with published data regarding the binding specificity of the proteins.

Availability: THEME is freely available for download.

Online Supplementary Information: http://jura.wi.mit.edu/fraenkel/THEME.


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