We have confirmed the conclusion of Kaplan et al 
that sequence-specific binding of nucleosomes plays a role in the selection of binding sites by TFs, although most TFs are more strongly associated with in vivo nucleosome positions than in vitro. This reflects the fact that TF binding itself is one of the causes of the differential nucleosome occupancy in vivo that is correlated with TF binding. The stronger association with in vivo nucleosome data was even more evident in the experiments we performed using ChIP-qPCR enrichment at consensus binding sites. The relatively weaker association with nucleosome binding in vitro perhaps lies in the different standards being applied in the two analyses. In the ChIP-chip analyses we asked how well nucleosome occupancy could classify bound vs. unbound sites but in the ChIP-qPCR experiments, we assessed quantitatively the correlation between nucleosome occupancy and TF ChIP enrichment. Perhaps it is too much to expect strong correlations between ChIP enrichment values and nucleosome occupancies as there are many factors that contribute to ChIP enrichment. Indeed, it is not even clear how strong the correlation is between TF occupancy and ChIP enrichment.
The difference in TF binding associations for the in vivo and in vitro nucleosome data is most striking for the outliers Abf1 and Reb1. These two TFs are thought to play key roles in chromatin remodeling and the formation of nucleosome free regions (NFRs) in yeast promoters 
. The association between binding and nucleosome depletion in vivo is so strong for these factors that we find clear evidence of Abf1-mediated depletion even in genomic regions for which the ChIP-chip enrichment p-value is far worse than what can be considered significant. This is not unexpected because binding is not a discrete phenomenon that lends itself to absolute cutoffs, and it has been shown that authentic and biologically relevant binding occurs even for sequences whose ChIP enrichment p-values are extremely poor. 
We provide further evidence for this conclusion by showing that low nucleosomal occupancies are predictive of Abf1 binding, even when the statistical confidence in binding is extraordinarily low.
The most important question we have addressed in this paper is the following: how much does the preferred location of nucleosomes matter to the selection and occupancy of binding sites by transcription factors. The way we sought to answer this is to ask whether data resolution is important to the conclusion that TF binding is associated with low nucleosome occupancy regions. Lower resolution data was simulated by averaging the high resolution data over increasingly larger windows. If the precise location and occupancy of nucleosomes is of great importance for the binding of TFs, then lower resolution data ought to do a worse job of showing the relationship between nucleosome occupancy and TF binding. We find that this is generally true for the in vivo data, which is determined in part by TF binding. However, just the opposite is true for the in vitro nucleosome data: simulating low resolution data by averaging over 600bp windows generally improves the predictions. This could not have been the result if mapped nucleosome positions were both accurate and strongly preferred over alternative positions.
For three reasons, we believe the resolution to this observation lies not in questioning the accuracy of the nucleosome occupancy maps, but in the assumption that the precision of intrinsic nucleosome binding matters a great deal to where transcription factors bind. First, the methodology for mapping nucleosomes was the same in vivo and in vitro. The in vivo maps show the expected trend, wherein a blurring of the data weakens the correlations, and we know of no reason to expect or believe that the accuracy of nucleosome mapping is different in the two different chromatin preparations. Second, the energetic differences between a preferred nucleosome configuration and an alternative are not expected to be large, in general. 
This is especially true if the overall nucleosome occupancy is low because then there are many alternatives that accommodate transcription factor binding, increasing the configurational entropy of the binding-tolerant alternative(s). Lower nucleosome density also allows each nucleosome to find local positions that are closer to the optimal. Third, the data itself suggests that most preferred nucleosome occupancies are only slightly more favorable than alternatives because the tag count densities under well-occupied nucleosomes are typically only a few fold higher than they are in the adjacent spacer regions. This suggests a free energy difference between preferred position and the spacer region on the order of 1 kcal/mol or less. The high tag count in spacer regions might reflect limitations in the experimental method, but it is also consistent with our expectations based on the energetics of nucleosome binding. 
We conclude that intrinsic nucleosome binding specificity plays a role in determining the selection and occupancy of transcription factor binding sites with which nucleosomes compete. However, the role is not so much in occluding binding sites based on precise nucleosome positioning, but more in defining broad regions of lower or higher nucleosome density that accommodate TF binding with differing degrees of ease.
There are several exceptions to the rule that the blurring of in vitro nucleosome data improves the association between nucleosome occupancy and binding. These exceptions tend to be TFs like Fhl1 and Rap1 that have relatively high nucleosome density flanking their binding sites. There is also a modest tendency for these TFs to be bound less frequently at TATA+ promoters (15±6% vs 27±9%; Figure S6
). However, it is not clear whether there is a mechanistic connection between these two facts.
The second important observation we report is the difference between nucleosome maps constructed in the conventional way, from uncrosslinked chromatin, and those constructed from formaldehyde-crosslinked chromatin. This difference is not only well correlated with TF binding but is, if anything, better correlated than nucleosome occupancy in the uncrosslinked sample.
The origin of this effect is uncertain. Conceivably it is a consequence of differences in higher order chromatin around TF binding sites, or it may be that the crosslinked TF itself provides protection against nuclease digestion. However, both explanations would require that at least some of the protected DNA survive size selection for mononucleosome-sized DNA. Another problem with the TF-protection explanation is that we would expect higher TF concentrations to increase the difference between crosslinking and non-crosslinking at binding sites, whereas the opposite is true, at least for Gal4 binding at the GAL1–GAL10 promoter (Figure S2
Alternatively, and more simply, the excess sequence tags may be due to transient nucleosomes sitting on TF binding sites. Crosslinking might be expected to have the greatest effect on nucleosomes that are ‘volatile’ relative to other nucleosomes in the genome: nucleosomes with slow association/dissociation kinetics (slow relative to nuclease treatment) should be relatively unaffected by crosslinking, while nucleosomes with fast kinetics should have their apparent occupancies increased because of the ease with which nucleosomes can be crosslinked to DNA. Competition with TFs can be expected to alter the apparent kinetics of nucleosomes by competing with them for reassociation, and histone turnover measurements have indeed shown faster exchange kinetics at yeast promoters 
and at presumptive regulatory elements in Drosophila. 
It may also be that the nucleosomes at binding sites contain histone variants that render their binding inherently more labile.
However, we were unable to establish an association between the nucleosome difference map (effect of crosslinking) and the replication-independent exchange rate of nucleosomes mapped at 265bp resolution (data not shown). 
Whatever the mechanisms, it seems clear that there is an association between regions of regulatory protein binding and higher nucleosome lability. The crosslink-noncrosslink difference map, which seems to be identifying labile nucleosomes, might therefore be used to discover non-histone protein binding sites in the genome.
The interactions among nucleosomes, transcription factors, and the enzymes that act on DNA and chromatin are complicated, but central to a deep understanding of gene regulation. Nucleosomes are a dominant factor in these interactions because they cover roughly 80% of the genome. Together with their intrinsic DNA sequence specificity, this adds further complexity to the problem. Our analyses suggest a simplifying principle to this complexity, namely that the precise position defined by nucleosome sequence specificity is not (on average, and for most TFs) of critical importance. Instead, the genome has evolved to define regions of lower and higher intrinsic nucleosome occupancy and these broad regions typically matter more than the precise most-favored configuration. Having said that, we expect there will be many exceptions in which precise positions are proven to be important. The technology now exists to explore these phenomena in greater detail and to begin to examine the kinetics of remodeling from one chromatin state to another. As data accumulates, we are confident that the incorporation of DNA-encoded nucleosome position information into computational models of TF binding will continue to improve the predictive quality of these models.