"Distinguishing direct versus indirect transcription factor-DNA interactions".
Raluca Gordân 1, Alexander J. Hartemink 1, and Martha L. Bulyk 2, 3, 4, @
1 Duke University, Dept. of Computer Science, Box 90129,
Durham, NC 27708, USA.
2 Division of Genetics, Department of Medicine, and 3
Department of Pathology, Brigham and Women’s Hospital and Harvard
Medical School, Boston, MA 02115, USA.
4 Harvard-MIT Division of Health Sciences and Technology
(HST), Harvard Medical School,
Boston, MA 02115, USA.
@To whom correspondence should be addressed. Email: mlbulyk@receptor.med.harvard.edu
Transcriptional regulation is largely enacted by transcription factors
(TFs) binding DNA.
Large numbers of TF binding motifs have been revealed by ChIP-chip
experiments
followed by computational DNA motif discovery. However, the
success of motif discovery
algorithms has been limited when applied to sequences bound in
vivo (such as those
identified by ChIP-chip) because the observed TF-DNA interactions
are not necessarily
direct: some TFs predominantly associate with DNA indirectly through
protein partners,
while others exhibit both direct and indirect binding. Here, we
present the first method for
distinguishing between direct and indirect TF-DNA interactions,
integrating in vivo TF
binding data, in vivo nucleosome occupancy data, and
motifs from in vitro protein binding
microarray experiments. When applied to yeast ChIP-chip data, our
method reveals that
only 48% of the data sets can be readily explained by direct
binding of the profiled TF,
while 16% can be explained by indirect DNA binding. In the
remaining 36%, none of the
motifs used in our analysis was able to explain the ChIP-chip data,
either because the data
were too noisy or because the set of motifs was incomplete. As more
in vitro TF DNA
binding motifs become available, our method could be used to build
a complete catalog of
direct and indirect TF-DNA interactions. Our method is not
restricted to yeast or to ChIPchip
data, but can be applied in any system for which both
in vivo binding data and in vitro
DNA binding motifs are available.
1. Todd Wasson, and Alexander J. Hartemink, (August, 2009),
"An
ensemble model of competitive multi-factor binding of the genome."
2. Frenster JH, and Hovsepian JA, (December, 2008),
"Models of
successive levels of resolution during individual gene transcription".
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