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Bioinformatics 2006 22(14):e141-e149; doi:10.1093/bioinformatics/btl223
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE

Barrett C. Foat 1, Alexandre V. Morozov 2 and Harmen J. Bussemaker 1,3,*

1 Department of Biological Sciences, Columbia University New York, NY 10027, USA
2 Center for Studies in Physics and Biology, The Rockefeller University New York, NY 10021, USA
3 Center for Computational Biology and Bioinformatics, Columbia University New York, NY 10032, USA

*To whom correspondence should be addressed.

Motivation: Regulation of gene expression by a transcription factor requires physical interaction between the factor and the DNA, which can be described by a statistical mechanical model. Based on this model, we developed the MatrixREDUCE algorithm, which uses genome-wide occupancy data for a transcription factor (e.g. ChIP-chip) and associated nucleotide sequences to discover the sequence-specific binding affinity of the transcription factor. Advantages of our approach are that the information for all probes on the microarray is efficiently utilized because there is no need to delineate "bound" and "unbound" sequences, and that, unlike information content-based methods, it does not require a background sequence model.

Results: We validated the performance of MatrixREDUCE by inferring the sequence-specific binding affinities for several transcription factors in S. cerevisiae and comparing the results with three other independent sources of transcription factor sequence-specific affinity information: (i) experimental measurement of transcription factor binding affinities for specific oligonucleotides, (ii) reporter gene assays for promoters with systematically mutated binding sites, and (iii) relative binding affinities obtained by modeling transcription factor-DNA interactions based on co-crystal structures of transcription factors bound to DNA substrates. We show that transcription factor binding affinities inferred by MatrixREDUCE are in good agreement with all three validating methods.

Availability: MatrixREDUCE source code is freely available for non-commercial use at http://www.bussemakerlab.org/. The software runs on Linux, Unix, and Mac OS X.

Contact: Harmen.Bussemaker{at}columbia.edu


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