The exact positions of nucleosomes along genomic DNA can influence many aspects of chromosome function. However, existing methods for mapping nucleosomes do not provide the necessary single-base-pair accuracy to determine these positions. Here we develop and apply a new approach for direct mapping of nucleosome centres on the basis of chemical modification of engineered histones. The resulting map locates nucleosome positions genome-wide in unprecedented detail and accuracy. It shows new aspects of the in vivo nucleosome organization that are linked to transcription factor binding, RNA polymerase pausing and the higher-order structure of the chromatin fibre.

Background: The nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genome's nucleosome organization may facilitate genome function. Results: We present a duration Hidden Markov model for nucleosome positioning prediction by explicitly modeling the linker DNA length. The nucleosome and linker models trained from yeast data are re-scaled when making predictions for other species to adjust for differences in base composition. A software tool named NuPoP is developed in three formats for free download. Conclusions: Simulation studies show that modeling the linker length distribution and utilizing a base composition re-scaling method both improve the prediction of nucleosome positioning regarding sensitivity and false discovery rate. NuPoP provides a user-friendly software tool for predicting the nucleosome occupancy and the most probable nucleosome positioning map for genomic sequences of any size. When compared with two existing methods, NuPoP shows improved performance in sensitivity.

Significance: This paper presents a high-resolution map of nucleosome positions of Schizosaccharomyces pombe . Comparison with the high-resolution map of Saccharomyces cerevisiae has provided important insights into nucleosome–DNA interaction and mechanistic variation in nucleosome positioning. The map shows a preponderance of linker lengths centered on 4/5 bp, placing adjacent nucleosomes on opposite faces of the DNA. The dinucleotide signature for nucleosome positioning is equally strong in exons as in introns. Unexpectedly, S. pombe nucleosomes have a preference for A/T residues surrounding the nucleosome dyad, and nucleosome occupancy is very mildly affected by poly (dA-dT) tracts. The preference for A/T residues around the dyad and its role in nucleosome phasing suggest a coevolution of genomes with the DNA binding preferences of nucleosomes across species.

Over the past years, tremendous efforts have been made to elucidate the molecular basis of the initiation and progression of ovarian cancer. However, most existing studies have been focused on individual genes or a single type of data, which may lack the power to detect the complex mechanisms of cancer formation by overlooking the interactions of different genetic and epigenetic factors. We propose an integrative framework to identify genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a probabilistic graphical model based on the Cancer Genome Atlas (TCGA) data. In the feature selection, we first defined a set of seed genes by including 48 candidate tumor suppressors or oncogenes and an additional 20 ovarian cancer related genes reported in the literature. The seed genes were then fed into a stepwise correlation-based selector to identify 271 additional features including 177 genes, 82 copy number variation sites, 11 methylation sites and 1 somatic mutation (at gene TP53). We built a Bayesian network model with a logit link function to quantify the causal relationships among these features and discovered a set of 13 hub genes including ARID1A, C19orf53, CSKN2A1 and COL5A2. The directed graph revealed many potential genetic pathways, some of which confirmed the existing results in the literature. Clustering analysis further suggested four gene clusters, three of which correspond to well-defined cellular processes including cell division, tumor invasion and mitochondrial system. In addition, two genes related to glycoprotein synthesis, PSG11 and GALNT10, were found highly predictive for the overall survival time of ovarian cancer patients. The proposed framework is effective in identifying possible important genetic and epigenetic features that are related to complex cancer diseases. The constructed Bayesian network has identified some new genetic/epigenetic pathways, which may shed new light into the molecular mechanisms of ovarian cancer.

The exact lengths of linker DNAs connecting adjacent nucleosomes specify the intrinsic three-dimensional structures of eukaryotic chromatin fibers. Some studies suggest that linker DNA lengths preferentially occur at certain quantized values, differing one from another by integral multiples of the DNA helical repeat, ∼10 bp; however, studies in the literature are inconsistent. Here, we investigate linker DNA length distributions in the yeast Saccharomyces cerevisiae genome, using two novel methods: a Fourier analysis of genomic dinucleotide periodicities adjacent to experimentally mapped nucleosomes and a duration hidden Markov model applied to experimentally defined dinucleosomes. Both methods reveal that linker DNA lengths in yeast are preferentially periodic at the DNA helical repeat (∼10 bp), obeying the forms 10n+5 bp (integer n). This 10 bp periodicity implies an ordered superhelical intrinsic structure for the average chromatin fiber in yeast. Eukaryotic genomic DNA exists as chromatin, with the DNA wrapped locally into a repeating array of protein–DNA complexes (“nucleosomes”) separated by short stretches of unwrapped “linker” DNA. Nucleosome arrays further compact into ∼30-nm-wide higher-order chromatin structures. Despite decades of work, there remains no agreement about the structure of the 30 nm fiber, or even if the structure is ordered or random. The helical symmetry of DNA couples the one-dimensional distribution of nucleosomes along the DNA to an intrinsic three-dimensional structure for the chromatin fiber. Random linker length distributions imply random three-dimensional intrinsic fiber structures, whereas different possible nonrandom length distributions imply different ordered structures. Here we use two independent computational methods, with two independent kinds of experimental data, to experimentally define the probability distribution of linker DNA lengths in yeast. Both methods agree that linker DNA lengths in yeast come in a set of preferentially quantized lengths that differ one from another by ∼10 bp, the DNA helical repeat, with a preferred phase offset of 5 bp. The preferential quantization of lengths implies that the intrinsic three-dimensional structure for the average chromatin fiber is ordered, not random. The 5 bp offset implies a particular geometry for this intrinsic structure.