Nucleosome occupancy plays a key role in regulating access to eukaryotic genomes. Although various chromatin regulatory complexes are known to regulate nucleosome occupancy, the role of DNA sequence in this regulation remains unclear, particularly in mammals. To address this problem, we measured nucleosome distribution at high temporal resolution in human cells at hundreds of genes during the reactivation of Kaposi's sarcoma-associated herpesvirus (KSHV). We show that nucleosome redistribution peaks at 24 h post-KSHV reactivation and that the nucleosomal redistributions are widespread and transient. To clarify the role of DNA sequence in these nucleosomal redistributions, we compared the genes with altered nucleosome distribution to a sequence-based computer model and in vitro-assembled nucleosomes. We demonstrate that both the predicted model and the assembled nucleosome distributions are concordant with the majority of nucleosome redistributions at 24 h post-KSHV reactivation. We suggest a model in which loci are held in an unfavorable chromatin architecture and ''spring'' to a transient intermediate state directed by DNA sequence information. We propose that DNA sequence plays a more considerable role in the regulation of nucleosome positions than was previously appreciated. The surprising findings that nucleosome redistributions are widespread, transient, and DNA-directed shift the current perspective regarding regulation of nucleosome distribution in humans.

The development and progression of lung adenocarcinoma, one of the most common cancers, is driven by the interplay of genetic and epigenetic changes and the role of chromatin structure in malignant transformation remains poorly understood. We used systematic nucleosome distribution and chromatin accessibility microarray mapping platforms to analyze the genome-wide chromatin structure from normal tissues and from primary lung adenocarcinoma of different grades and stages. We identified chromatin-based patterns across different patients with lung adenocarcinoma of different cancer grade and stage. Low-grade cancers had nucleosome distributions very different compared with the corresponding normal tissue but had nearly identical chromatin accessibility. Conversely, nucleosome distributions of high-grade cancers showed few differences. Substantial disruptions in chromosomal accessibility were seen in a patient with a high-grade and high-stage tumor. These data imply that chromatin structure changes during the progression of lung adenocarcinoma. We have therefore developed a model in which low-grade lung adenocarcinomas are linked to changes in nucleosome distributions, whereas higher-grade tumors are linked to large-scale chromosomal changes. These results provide a foundation for the development of a comprehensive framework linking the general and locus-specific roles of chromatin structure to lung cancer progression. We propose that this strategy has the potential to identify a new class of chromatin-based diagnostic, prognostic and therapeutic markers in cancer progression.

The nucleosome is a fundamental structural and functional chromatin unit that affects nearly all DNA-templated events in eukaryotic genomes. It is also a biochemical substrate for higher order, cis-acting gene expression codes and the monomeric structural unit for chromatin packaging at multiple scales. To predict the nucleosome landscape of a model plant genome, we used a support vector machine computational algorithm trained on human chromatin to predict the nucleosome occupancy likelihood (NOL) across the maize (Zea mays) genome. Experimentally validated NOL plots provide a novel genomic annotation that highlights gene structures, repetitive elements, and chromosome-scale domains likely to reflect regional gene density. We established a new genome browser (http://www.genomaize.org) for viewing support vector machine-based NOL scores. This annotation provides sequence-based comprehensive coverage across the entire genome, including repetitive genomic regions typically excluded from experimental genomics data. We find that transposable elements often displayed family-specific NOL profiles that included distinct regions, especially near their termini, predicted to have strong affinities for nucleosomes. We examined transcription start site consensus NOL plots for maize gene sets and discovered that most maize genes display a typical +1 nucleosome positioning signal just downstream of the start site but not upstream. This overall lack of a -1 nucleosome positioning signal was also predicted by our method for Arabidopsis (Arabidopsis thaliana) genes and verified by additional analysis of previously published Arabidopsis MNase-Seq data, revealing a general feature of plant promoters. Our study advances plant chromatin research by defining the potential contribution of the DNA sequence to observed nucleosome positioning and provides an invariant baseline annotation against which other genomic data can be compared.

The regulation of metazoan gene expression occurs in part by pre-mRNA splicing into mature RNAs. Signals affecting the efficiency and specificity with which introns are removed have not been completely elucidated. Splicing likely occurs cotranscriptionally, with chromatin structure playing a key regulatory role. We calculated DNA encoded nucleosome occupancy likelihood (NOL) scores at the boundaries between introns and exons across five metazoan species. We found that (i) NOL scores reveal a sequence-based feature at the introns on both sides of the intron-exon boundary; (ii) this feature is not part of any recognizable consensus sequence; (iii) this feature is conserved throughout metazoa; (iv) this feature is enriched in genes sharing similar functions: ATPase activity, ATP binding, helicase activity, and motor activity; (v) genes with these functions exhibit different genomic characteristics; (vi) in vivo nucleosome positioning data confirm ontological enrichment at this feature; and (vii) genes with this feature exhibit unique dinucleotide distributions at the intron-exon boundary. The NOL scores point toward a physical property of DNA that may play a role in the mechanism of pre-mRNA splicing. These results provide a foundation for identification of a new set of regulatory DNA elements involved in splicing regulation.

Recent genome wide experiments indicate that DNA sequences themselves strongly influence nucleosome positioning as an intrinsic cell regulatory mechanism. While some sequence features are known to be nucleosome forming or nucleosome inhibiting, there is no systematic study on identifying optimal sequence features for quantitatively modeling of DNA binding affinity. In this paper, we propose a computationally efficient method of identifying a (small) number of sequence features for intrinsic nucleosome positioning. By using a modified version of AdaBoost, the proposed method is able to identify features to be used with a strong classifier to categorize nucleosome forming and nucleosome inhibiting local DNA sequences. Experimental results on extensive datasets show that the resulting classifiers give typically better prediction performance than the existing discrimination models on all the tested datasets with a much smaller number of features.

More than two meters of DNA is organized inside a five micrometer nucleus of every human cell. This is accomplished by the wrapping of DNA around protein "spools" (histones) to form nucleosomes. Some DNA sequences are more likely to be organized into nucleosomes than others. Because nucleosomes, not bare DNA, are the substrate for DNA-templated reactions, the position and density of nucleosomes play an important role in these processes (e.g., affecting gene regulation). We used a machine learning tool trained to distinguish nucleosome forming from nucleosome inhibitory DNA sequences. This Support Vector Machine (SVM) was used to evaluate DNA sequence and generate nucleosome occupancy predictions for the entire human genome. We used the resulting scores to characterize DNA-encoded nucleosome positioning signals at specific genomic regions. Scores around protein coding regions, specifically intron-exon boundaries, showed a characteristic nucleosome positioning pattern. Previously these boundaries had been identified using a DNA consensus sequence, and we were able to identify a subset of protein genes based on sequence features recognized by the SVM that are not readily identifiable by traditional sequence evaluation techniques. We anticipate that this type of analysis will allow us to refine our understanding of DNA regulatory processes.