Blurring of high-resolution data shows that the effect of intrinsic nucleosome occupancy on transcription factor binding is mostly regional, not local.
Genome wide maps of nucleosome occupancy in yeast have recently been produced through deep sequencing of nuclease-protected DNA. These maps have been obtained from both crosslinked and uncrosslinked chromatin in vivo, and from chromatin assembled from genomic DNA and nucleosomes in vitro. Here, we analyze these maps in combination with existing ChIP-chip data, and with new ChIP-qPCR experiments reported here. We show that the apparent nucleosome density in crosslinked chromatin, when compared to uncrosslinked chromatin, is preferentially increased at transcription factor (TF) binding sites, suggesting a strategy for mapping generic transcription factor binding sites that would not require immunoprecipitation of a particular factor. We also confirm previous conclusions that the intrinsic, sequence dependent binding of nucleosomes helps determine the localization of TF binding sites. However, we find that the association between low nucleosome occupancy and TF binding is typically greater if occupancy at a site is averaged over a 600bp window, rather than using the occupancy at the binding site itself. We have also incorporated intrinsic nucleosome binding occupancies as weights in a computational model for TF binding, and by this measure as well we find better prediction if the high resolution nucleosome occupancy data is averaged over 600bp. We suggest that the intrinsic DNA binding specificity of nucleosomes plays a role in TF binding site selection not so much through the specification of precise nucleosome positions that permit or occlude binding, but rather through the creation of low occupancy regions that can accommodate competition from TFs through rearrangement of nucleosomes.
Goh
Wee Siong
WS
Computational and Systems Biology, Genome Institute of Singapore, Singapore.
Orlov
Yuriy
Y
Li
Jingmei
J
Clarke
Neil D
ND
D019540:Area Under Curve; D001665:Binding Sites; D002843:Chromatin / Q000378:metabolism; D047369:Chromatin Immunoprecipitation; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000378:metabolism; D030562:Databases, Protein; D008957:Models, Genetic; D009707:Nucleosomes / Q000378:metabolism*; D016133:Polymerase Chain Reaction; D054730:Protein Interaction Domains and Motifs; D012372:ROC Curve; D012440:Saccharomyces; D014157:Transcription Factors / Q000378:metabolism*
D002843:Chromatin; D009707:Nucleosomes; D014157:Transcription Factors; D004247:DNA