Blurring of High-Resolution Data Shows that the Effect of Intrinsic Nucleosome Occupancy on Transcription Factor Binding is Mostly Regional, Not Local

Abstract

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.

Nucleosome repositioning links DNA (de)methylation and differential CTCF binding during stem cell development

“…6B). This conclusion is in line with recent studies showing that, at least for some TFs, their occupancy predictions can be improved by taking into account the competition with histones for DNA binding (Narlikar et al 2007;Gordan et al 2009;Raveh-Sadka et al 2009;Wasson and Hartemink 2009;Goh et al 2010;Locke et al 2010;John et al 2011;Kaplan et al 2011;Li et al 2011;Zhou and O'Shea 2011;Lickwar et al 2012;He et al 2013).…”

Nucleosome Positioning in Saccharomyces cerevisiae

“…Many of the bound TFbinding sites reside in NFRs where accessibility is increased due to the low nucleosome occupancy, making it thermodynamically more favorable for TFs to bind (4,54,96,207). The in vitro reconstitution of the yeast genome into nucleosomes revealed that part of the nucleosome depletion observed at TF-binding sites is intrinsically encoded in the genome, through the DNA sequence preferences of nucleosomes.…”

Computer analysis of co-localization of transcription factor binding sites in genome by ChIP-seq data

“…Анализ расположения кластеров относительно генов с помощью таких инструментов, как GREAT (Genomic Regions Enrichment of Annotations Tool), дает возможность оценить расположение сайтов связывания отдельных факторов и кластеров сайтов относительно генов (Guo et al, 2012), классифицировать их как промоторные и дистальные, что является дополнительной функциональ-ной характеристикой таких кластеров. Дополнительные характеристики нуклеотидных последовательностей для описания кластеров сайтов связывания, таких как участки низкой сложности текста (Orlov, Potapov, 2004;, нуклеосомная упаковка (Орлов и др., 2006; Goh et al, 2010), позволяют более точно определить регуля-торные районы в геноме и выполнять их поиск на основе непрямых данных, без экспериментов ChIP-seq. Интегра-ция геномных данных средствами UGENE (Vas'kin et al, 2011;Васькин и др., 2012;Golosova et al, 2014) позволит качественно решать новые задачи комбинаторного анализа сайтов по данным проектов ENCODE (https://genome.ucsc.…”

“…Первые работы основывались на вы-явлении олигонуклеотидных мотивов и были ограничены статистической базой (десятки, в лучшем случае сотни последовательностей в базах данных) (Heinemeyer et al, 1998). Появление полногеномных методов секвенирова-ния дало толчок к поиску сайтов и регуляторных районов с помощью геномных профилей ChIP-seq, определению предпочтений геномной локализации, нуклеосомной упаковки (Goh et al, 2010;Орлов, 2014).…”