| dc.description.abstract |
<p>Hundreds of different factors adorn the eukaryotic genome, binding to it in large
number. These DNA binding factors (DBFs) include nucleosomes, transcription factors
(TFs), and other proteins and protein complexes, such as the origin recognition complex
(ORC). DBFs compete with one another for binding along the genome, yet many current
models of genome binding do not consider different types of DBFs together simultaneously.
Additionally, binding is a stochastic process that results in a continuum of binding
probabilities at any position along the genome, but many current models tend to consider
positions as being either binding sites or not.</p><p>Here, we present a model that
allows a multitude of DBFs, each at different concentrations, to compete with one
another for binding sites along the genome. The result is an 'occupancy profile',
a probabilistic description of the DNA occupancy of each factor at each position.
We implement our model efficiently as the software package COMPETE. We demonstrate
genome-wide and at specific loci how modeling nucleosome binding alters TF binding,
and vice versa, and illustrate how factor concentration influences binding occupancy.
Binding cooperativity between nearby TFs arises implicitly via mutual competition
with nucleosomes. Our method applies not only to TFs, but also recapitulates known
occupancy profiles of a well-studied replication origin with and without ORC binding.</p><p>We
then develop a statistical framework for tuning our model concentrations to further
improve its predictions. Importantly, this tuning optimizes with respect to actual
biological data. We take steps to ensure that our tuned parameters are biologically
plausible.</p><p>Finally, we discuss novel extensions and applications of our model,
suggesting next steps in its development and deployment.</p>
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