Genomic sequence is highly predictive of local nucleosome depletion

Interpretable deep residual network uncovers nucleosome positioning and associated features

Nucleosomes represent elementary building units of eukaryotic chromosomes and consist of DNA wrapped around a histone octamer flanked by linker DNA segments. Nucleosomes are central in epigenetic pathways and their genomic positioning is associated with regulation of gene expression, DNA replication, DNA methylation and DNA repair, among other functions. Building on prior discoveries that DNA sequences noticeably affect nucleosome positioning, our objective is to identify nucleosome positions and related features across entire genome. Here, we introduce an interpretable framework based on the concepts of deep residual networks (NuPoSe). Trained on high-coverage human experimental MNase-seq data, NuPoSe is able to learn sequence and structural patterns associated with nucleosome organization in human genome. NuPoSe can be also applied to unseen data from different organisms and cell types. Our findings point to 43 informative features, most of them constitute tri-nucleotides, di-nucleotides and one tetra-nucleotide. Most features are significantly associated with the nucleosomal structural characteristics, namely, periodicity of nucleosomal DNA and its location with respect to a histone octamer. Importantly, we show that features derived from the 27 bp linker DNA flanking nucleosomes contribute up to 10% to the quality of the prediction model. This, along with the comprehensive training sets, deep-learning architecture, and feature selection method, may contribute to the NuPoSe's 80-89% classification accuracy on different independent datasets.

Context dependent prediction in DNA sequence using neural networks

One way to better understand the structure in DNA is by learning to predict the sequence. Here, we trained a model to predict the missing base at any given position, given its left and right flanking contexts. Our best-performing model was a neural network that obtained an accuracy close to 54% on the human genome, which is 2% points better than modelling the data using a Markov model. In likelihood-ratio tests, the neural network performed significantly better than any of the alternative models by a large margin. We report on where the accuracy was obtained, first observing that the performance appeared to be uniform over the chromosomes. The models performed best in repetitive sequences, as expected, although their performance far from random in the more difficult coding sections, the proportions being ~70:40%. We further explored the sources of the accuracy, Fourier transforming the predictions revealed weak but clear periodic signals. In the human genome the characteristic periods hinted at connections to nucleosome positioning. We found similar periodic signals in GC/AT content in the human genome, which to the best of our knowledge have not been reported before. On other large genomes similarly high accuracy was found, while lower predictive accuracy was observed on smaller genomes. Only in the mouse genome did we see periodic signals in the same range as in the human genome, though weaker and of a different type. This indicates that the sources of these signals are other or more than nucleosome arrangement. Interestingly, applying a model trained on the mouse genome to the human genome resulted in a performance far below that of the human model, except in the difficult coding regions. Despite the clear outcomes of the likelihood-ratio tests, there is currently a limited superiority of the neural network methods over the Markov model. We expect, however, that there is great potential for better modelling DNA using different neural network architectures.

A deformation energy model reveals sequence-dependent property of nucleosome positioning

We present a deformation energy model for predicting nucleosome positioning, in which a position-dependent structural parameter set derived from crystal structures of nucleosomes was used to calculate the DNA deformation energy. The model is successful in predicting nucleosome occupancy genome-wide in budding yeast, nucleosome free energy, and rotational positioning of nucleosomes. Our model also indicates that the genomic regions underlying the MNase-sensitive nucleosomes in budding yeast have high deformation energy and, consequently, low nucleosome-forming ability, while the MNase-sensitive non-histone particles are characterized by much lower DNA deformation energy and high nucleosome preference. In addition, we also revealed that remodelers, SNF2 and RSC8, are likely to act in chromatin remodeling by binding to broad nucleosome-depleted regions that are intrinsically favorable for nucleosome positioning. Our data support the important role of position-dependent physical properties of DNA in nucleosome positioning.

ZCMM: A Novel Method Using Z-Curve Theory- Based and Position Weight Matrix for Predicting Nucleosome Positioning

Nucleosomes are the basic units of eukaryotes. The accurate positioning of nucleosomes plays a significant role in understanding many biological processes such as transcriptional regulation mechanisms and DNA replication and repair. Here, we describe the development of a novel method, termed ZCMM, based on Z-curve theory and position weight matrix (PWM). The ZCMM was trained and tested using the nucleosomal and linker sequences determined by support vector machine (SVM) in Saccharomyces cerevisiae (S. cerevisiae), and experimental results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC) values for ZCMM were 91.40%, 96.56%, 96.75%, and 0.88, respectively, and the average area under the receiver operating characteristic curve (AUC) value was 0.972. A ZCMM predictor was developed to predict nucleosome positioning in Homo sapiens (H. sapiens), Caenorhabditis elegans (C. elegans), and Drosophila melanogaster (D. melanogaster) genomes, and the accuracy (Acc) values were 77.72%, 85.34%, and 93.62%, respectively. The maximum AUC values of the four species were 0.982, 0.861, 0.912 and 0.911, respectively. Another independent dataset for S. cerevisiae was used to predict nucleosome positioning. Compared with the results of Wu's method, it was found that the Sn, Sp, Acc, and MCC of ZCMM results for S. cerevisiae were all higher, reaching 96.72%, 96.54%, 94.10%, and 0.88. Compared with the Guo's method 'iNuc-PseKNC', the results of ZCMM for D. melanogaster were better. Meanwhile, the ZCMM was compared with some experimental data in vitro and in vivo for S. cerevisiae, and the results showed that the nucleosomes predicted by ZCMM were highly consistent with those confirmed by these experiments. Therefore, it was further confirmed that the ZCMM method has good accuracy and reliability in predicting nucleosome positioning.

LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks

Motivation

Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite a multitude of nucleosome positioning resources available on line including experimental datasets of genome-wide nucleosome occupancy profiles and computational tools to the analysis on these data, the complex language of eukaryotic Nucleosome positioning remains incompletely understood.

Results

Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) to understand nucleosome positioning. We combined Inception-like networks with a gating mechanism for the response of multiple patterns and long term association in DNA sequences. We developed the open-source package LeNup based on the CNN to predict nucleosome positioning in Homo sapiens, Caenorhabditis elegans, Drosophila melanogaster as well as Saccharomyces cerevisiae genomes. We trained LeNup on four benchmark datasets. LeNup achieved greater predictive accuracy than previously published methods.

Availability and implementation

LeNup is freely available as Python and Lua script source code under a BSD style license from https://github.com/biomedBit/LeNup.

Supplementary information

Supplementary data are available at Bioinformatics online.

Androgen receptor splice variants bind to constitutively open chromatin and promote abiraterone-resistant growth of prostate cancer

Androgen receptor (AR) splice variants (ARVs) are implicated in development of castration-resistant prostate cancer (CRPC). Upregulation of ARVs often correlates with persistent AR activity after androgen deprivation therapy (ADT). However, the genomic and epigenomic characteristics of ARV-dependent cistrome and the disease relevance of ARV-mediated transcriptome remain elusive. Through integrated chromatin immunoprecipitation coupled sequencing (ChIP-seq) and RNA sequencing (RNA-seq) analysis, we identified ARV-preferential-binding sites (ARV-PBS) and a set of genes preferentially transactivated by ARVs in CRPC cells. ARVs preferentially bind to enhancers located in nucleosome-depleted regions harboring the full AR-response element (AREfull), while full-length AR (ARFL)-PBS are enhancers resided in closed chromatin regions containing the composite FOXA1-nnnn-AREhalf motif. ARV-PBS exclusively overlapped with AR binding sites in castration-resistant (CR) tumors in patients and ARV-preferentially activated genes were up-regulated in abiraterone-resistant patient specimens. Expression of ARV-PBS target genes, such as oncogene RAP2A and cell cycle gene E2F7, were significantly associated with castration resistance, poor survival and tumor progression. We uncover distinct genomic and epigenomic features of ARV-PBS, highlighting that ARVs are useful tools to depict AR-regulated oncogenic genome and epigenome landscapes in prostate cancer. Our data also suggest that the ARV-preferentially activated transcriptional program could be targeted for effective treatment of CRPC.

Deep learning architectures for prediction of nucleosome positioning from sequences data

Background

Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using a sequence features representation.

Results

In this work, we propose a deep learning model for nucleosome identification. Our model stacks convolutional layers and Long Short-term Memories to automatically extract features from short- and long-range dependencies in a sequence. Using this model we are able to avoid the feature extraction and selection steps while improving the classification performances.

Conclusions

Results computed on eleven data sets of five different organisms, from Yeast to Human, show the superiority of the proposed method with respect to the state of the art recently presented in the literature.

iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition

Nucleosome is a central element of eukaryotic chromatin, which composes of histone proteins and DNA molecules. It performs vital roles in many eukaryotic intra-nuclear processes, for instance, chromatin structure and transcriptional regulation formation. Identification of nucleosome positioning via wet lab is difficult; so, the attention is diverted towards the accurate intelligent automated prediction. In this regard, a novel intelligent automated model "iNuc-ext-PseTNC" is developed to identify the nucleosome positioning in genomes accurately. In this predictor, the sequences of DNA are mathematically represented by two different discrete feature extraction techniques, namely pseudo-tri-nucleotide composition (PseTNC) and pseudo-di-nucleotide composition. Several contemporary machine learning algorithms were examined. Further, the predictions of individual classifiers were integrated through an evolutionary genetic algorithm. The success rates of the ensemble model are higher than individual classifiers. After analyzing the prediction results, it is noticed that iNuc-ext-PseTNC model has achieved better performance in combination with PseTNC feature space, which are 94.3%, 93.14%, and 88.60% of accuracies using six-fold cross-validation test for the three benchmark datasets S₁, S₂, and S₃, respectively. The achieved outcomes exposed that the results of iNuc-ext-PseTNC model are prominent compared to the existing methods so far notifiable in the literature. It is ascertained that the proposed model might be more fruitful and a practical tool for rudimentary academia and research.

Functional conservation of sequence determinants at rapidly evolving regulatory regions across mammals

Recent advances in epigenomics have made it possible to map genome-wide regulatory regions using empirical methods. Subsequent comparative epigenomic studies have revealed that regulatory regions diverge rapidly between genome of different species, and that the divergence is more pronounced in enhancers than in promoters. To understand genomic changes underlying these patterns, we investigated if we can identify specific sequence fragments that are over-enriched in regulatory regions, thus potentially contributing to regulatory functions of such regions. Here we report numerous sequence fragments that are statistically over-enriched in enhancers and promoters of different mammals (which we refer to as ‘sequence determinants’). Interestingly, the degree of statistical enrichment, which presumably is associated with the degree of regulatory impacts of the specific sequence determinant, was significantly higher for promoter sequence determinants than enhancer sequence determinants. We further used a machine learning method to construct prediction models using sequence determinants. Remarkably, prediction models constructed from one species could be used to predict regulatory regions of other species with high accuracy. This observation indicates that even though the precise locations of regulatory regions diverge rapidly during evolution, the functional potential of sequence determinants underlying regulatory sequences may be conserved between species.

Regions of the genome that do not encode genes but affect expression of other genes, such as enhancers and promoters, are referred to as regulatory regions. Because of their regulatory functions, it was thought that enhancers and promoters should be evolutionarily conserved. Regulatory regions can be now epigenomically identified because they are marked by specific modifications of histone tails at the chromatin level. Interestingly, when we compare epigenomically identified regulatory regions from different mammals, the specific positions of regulatory regions are often divergent between species. Enhancers in particular are highly divergent between species. In this study, we show that we can find sequence fragments that are statistically enriched in enhancers and promoters of different species, and that the degree of statistical enrichment can explain different levels of evolutionary sequence conservation between enhancers and promoters. We further constructed predictive models of enhancers and promoters using the enriched sequence fragments, and show that these models can not only accurately predict enhancers and promoters of the same species, but works comparably well when applied to other species. These results indicate that even though the specific positions of regulatory regions have diverged between species, the functions of sequence fragments that comprise those regions may be conserved.

The affinity of DNA sequences containing R5Y5 motif and TA repeats with 10.5-bp periodicity to histone octamer in vitro

Nucleosome positioning along the genome is partially determined by the intrinsic DNA sequence preferences on histone. RRRRRYYYYY (R5Y5, R = Purine and Y = Pyrimidine) motif in nucleosome DNA, which was presented based on several theoretical models by Trifonov et al., might be a facilitating sequence pattern for nucleosome assembly. However, there is not a high conformity experimental evidence to support the concept that R5Y5 motif is a key element for the determination of nucleosome positioning. In this work, the ability of the canonical, H2A.Z- and H3.3-containing octamers to assemble nucleosome on DNA templates containing R5Y5 motif and TA repeats within 10.5-bp periodicity was investigated by using salt-dialysis method in vitro. The results showed that the10.5-bp periodical distributions of both R5Y5 motif and TA repeats along DNA templates can significantly promote canonical nucleosome assembly and may be key sequence factors for canonical nucleosome assembly. Compared with TA repeats within 10.5-bp periodicity, R5Y5 motif in DNA templates did not elevate H2A.Z- and H3.3-containing nucleosome formation efficiency in vitro. This result indicates that R5Y5 motif probably isn't a pivotal factor to regulate nucleosome assembly on histone variants. It is speculated that the regulatory mechanism of nucleosome assembly is different between canonical and variant histone. These conclusions can provide a deeper insight on the mechanism of nucleosome positioning. Communicated by Ramaswamy H. Sarma.

DNA physical properties outperform sequence compositional information in classifying nucleosome-enriched and -depleted regions

The nucleosome is the fundamental structural unit of eukaryotic chromatin and plays an essential role in the epigenetic regulation of cellular processes, such as DNA replication, recombination, and transcription. Hence, it is important to identify nucleosome positions in the genome. Our previous model based on DNA deformation energy, in which a set of DNA physical descriptors was used, performed well in predicting nucleosome dyad positions and occupancy. In this study, we established a machine-learning model for predicting nucleosome occupancy in order to further verify the physical descriptors. Results showed that (1) our model outperformed several other sequence compositional information-based models, indicating a stronger dependence of nucleosome positioning on DNA physical properties; (2) nucleosome-enriched and -depleted regions have distinct features in terms of DNA physical descriptors like sequence-dependent flexibility and equilibrium structure parameters; (3) gene transcription start sites and termination sites can be well characterized with the distribution patterns of the physical descriptors, indicating the regulatory role of DNA physical properties in gene transcription. In addition, we developed a web server for the model, which is freely accessible at http://lin-group.cn/server/iNuc-force/.

Evidence for DNA Sequence Encoding of an Accessible Nucleosomal Array across Vertebrates

Nucleosome-depleted regions around which nucleosomes order following the "statistical" positioning scenario were recently shown to be encoded in the DNA sequence in human. This intrinsic nucleosomal ordering strongly correlates with oscillations in the local GC content as well as with the interspecies and intraspecies mutation profiles, revealing the existence of both positive and negative selection. In this letter, we show that these predicted nucleosome inhibitory energy barriers (NIEBs) with compacted neighboring nucleosomes are indeed ubiquitous to all vertebrates tested. These 1 kb-sized chromatin patterns are widely distributed along vertebrate chromosomes, overall covering more than a third of the genome. We have previously observed in human deviations from neutral evolution at these genome-wide distributed regions, which we interpreted as a possible indication of the selection of an open, accessible, and dynamic nucleosomal array to constitutively facilitate the epigenetic regulation of nuclear functions in a cell-type-specific manner. As a first, very appealing observation supporting this hypothesis, we report evidence of a strong association between NIEB borders and the poly(A) tails of Alu sequences in human. These results suggest that NIEBs provide adequate chromatin patterns favorable to the integration of Alu retrotransposons and, more generally to various transposable elements in the genomes of primates and other vertebrates.

A comparison of nucleosome organization in Drosophila cell lines

Changes in the distribution of nucleosomes along the genome influence chromatin structure and impact gene expression by modulating the accessibility of DNA to transcriptional machinery. However, the role of genome-wide nucleosome positioning in gene expression and in maintaining differentiated cell states remains poorly understood. Drosophila melanogaster cell lines represent distinct tissue types and exhibit cell-type specific gene expression profiles. They thus could provide a useful tool for investigating cell-type specific nucleosome organization of an organism's genome. To evaluate this possibility, we compared genome-wide nucleosome positioning and occupancy in five different Drosophila tissue-specific cell lines, and in reconstituted chromatin, and then tested for correlations between nucleosome positioning, transcription factor binding motifs, and gene expression. Nucleosomes in all cell lines were positioned in accordance with previously known DNA-nucleosome interactions, with helically repeating A/T di-nucleotide pairs arranged within nucleosomal DNAs and AT-rich pentamers generally excluded from nucleosomal DNA. Nucleosome organization in all cell lines differed markedly from in vitro reconstituted chromatin, with highly expressed genes showing strong nucleosome organization around transcriptional start sites. Importantly, comparative analysis identified genomic regions that exhibited cell line-specific nucleosome enrichment or depletion. Further analysis of these regions identified 91 out of 16,384 possible heptamer sequences that showed differential nucleosomal occupation between cell lines, and 49 of the heptamers matched one or more known transcription factor binding sites. These results demonstrate that there is differential nucleosome positioning between these Drosophila cell lines and therefore identify a system that could be used to investigate the functional significance of differential nucleosomal positioning in cell type specification.

Genomes of Multicellular Organisms Have Evolved to Attract Nucleosomes to Promoter Regions

Sequences that influence nucleosome positioning in promoter regions, and their relation to gene regulation, have been the topic of much research over the last decade. In yeast, significant nucleosome-depleted regions are found, which facilitate transcription. With the arrival of nucleosome positioning maps for the human genome, it was discovered that in our genome, unlike in that of yeast, promoters encode for high nucleosome occupancy. In this work, we look at the genomes of a range of different organisms, to provide a catalog of nucleosome positioning signals in promoters across the tree of life. We utilize a computational model of the nucleosome, based on crystallographic analyses of the structure and elasticity of the nucleosome, to predict the nucleosome positioning signals in promoter regions. To be able to apply our model to large genomic datasets, we introduce an approximative scheme that makes use of the limited range of correlations in nucleosomal sequence preferences to create a computationally efficient approximation of the full biophysical model. Our predictions show that a clear distinction between unicellular and multicellular life is visible in the intrinsically encoded nucleosome affinity. Furthermore, the strength of the nucleosome positioning signals correlates with the complexity of the organism. We conclude that encoding for high nucleosome occupancy, as in the human genome, is in fact a universal feature of multicellular life.

Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition

Motivation

Nucleosome positioning plays important roles in many eukaryotic intranuclear processes, such as transcriptional regulation and chromatin structure formation. The investigations of nucleosome positioning rules provide a deeper understanding of these intracellular processes.

Results

Nucleosome positioning prediction was performed using a model consisting of three types of variables characterizing a DNA sequence—the number of five-nucleotide sequences, the number of three-nucleotide combinations in one period of a helix, and mono- and di-nucleotide distributions in DNA fragments. Using recently proposed stringent benchmark datasets with low biases for Saccharomyces cerevisiae, Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster, the present model was shown to have a better prediction performance than the recently proposed predictors. This model was able to display the common and organism-dependent factors that affect nucleosome forming and inhibiting sequences as well. Therefore, the predictors developed here can accurately predict nucleosome positioning and help determine the key factors influencing this process.

Supplementary information

Supplementary data are available at Bioinformatics online.

Nucleosome dynamics: Sequence matters

About three quarter of all eukaryotic DNA is wrapped around protein cylinders, forming nucleosomes. Even though the histone proteins that make up the core of nucleosomes are highly conserved in evolution, nucleosomes can be very different from each other due to posttranslational modifications of the histones. Another crucial factor in making nucleosomes unique has so far been underappreciated: the sequence of their DNA. This review provides an overview of the experimental and theoretical progress that increasingly points to the importance of the nucleosomal base pair sequence. Specifically, we discuss the role of the underlying base pair sequence in nucleosome positioning, sliding, breathing, force-induced unwrapping, dissociation and partial assembly and also how the sequence can influence higher-order structures. A new view emerges: the physical properties of nucleosomes, especially their dynamical properties, are determined to a large extent by the mechanical properties of their DNA, which in turn depends on DNA sequence.

Categorical spectral analysis of periodicity in nucleosomal DNA

DNA helical twist imposes geometric constraints on the location of histone–DNA interaction sites along nucleosomal DNA. Certain 10.5-bp periodic nucleotides in phase with these geometric constraints have been suggested to facilitate nucleosome positioning. However, the extent of nucleotide periodicity in nucleosomal DNA and its significance in directing nucleosome positioning still remain unclear. We clarify these issues by applying categorical spectral analysis to high-resolution nucleosome maps in two yeast species. We find that only a small fraction of nucleosomal sequences contain significant 10.5-bp periodicity. We further develop a spectral decomposition method to show that the previously observed periodicity in aligned nucleosomal sequences mainly results from proper phasing among nucleosomal sequences, and not from a preponderant occurrence of periodicity within individual sequences. Importantly, we show that this phasing may arise from the histones’ proclivity for putting preferred nucleotides at some of the evenly spaced histone–DNA contact points with respect to the dyad axis. We demonstrate that 10.5-bp periodicity, when present, significantly facilitates rotational, but not translational, nucleosome positioning. Finally, although periodicity only moderately affects nucleosome occupancy genome wide, reduced periodicity is an evolutionarily conserved signature of nucleosome-depleted regions around transcription start/termination sites.

Nucleosome positioning: resources and tools online

Nucleosome positioning is an important process required for proper genome packing and its accessibility to execute the genetic program in a cell-specific, timely manner. In the recent years hundreds of papers have been devoted to the bioinformatics, physics and biology of nucleosome positioning. The purpose of this review is to cover a practical aspect of this field, namely, to provide a guide to the multitude of nucleosome positioning resources available online. These include almost 300 experimental datasets of genome-wide nucleosome occupancy profiles determined in different cell types and more than 40 computational tools for the analysis of experimental nucleosome positioning data and prediction of intrinsic nucleosome formation probabilities from the DNA sequence. A manually curated, up to date list of these resources will be maintained at http://generegulation.info.

Comparative Genomics Reveals Chd1 as a Determinant of Nucleosome Spacing in Vivo

Packaging of genomic DNA into nucleosomes is nearly universally conserved in eukaryotes, and many features of the nucleosome landscape are quite conserved. Nonetheless, quantitative aspects of nucleosome packaging differ between species because, for example, the average length of linker DNA between nucleosomes can differ significantly even between closely related species. We recently showed that the difference in nucleosome spacing between two Hemiascomycete species—Saccharomyces cerevisiae and Kluyveromyces lactis—is established by trans-acting factors rather than being encoded in cis in the DNA sequence. Here, we generated several S. cerevisiae strains in which endogenous copies of candidate nucleosome spacing factors are deleted and replaced with the orthologous factors from K. lactis. We find no change in nucleosome spacing in such strains in which H1 or Isw1 complexes are swapped. In contrast, the K. lactis gene encoding the ATP-dependent remodeler Chd1 was found to direct longer internucleosomal spacing in S. cerevisiae, establishing that this remodeler is partially responsible for the relatively long internucleosomal spacing observed in K. lactis. By analyzing several chimeric proteins, we find that sequence differences that contribute to the spacing activity of this remodeler are dispersed throughout the coding sequence, but that the strongest spacing effect is linked to the understudied N-terminal end of Chd1. Taken together, our data find a role for sequence evolution of a chromatin remodeler in establishing quantitative aspects of the chromatin landscape in a species-specific manner.

Predicting the human epigenome from DNA motifs

The epigenome is established and maintained by the site-specific recruitment of chromatin-modifying enzymes and their cofactors. Identifying the cis elements that regulate epigenomic modification is critical for understanding the regulatory mechanisms that control gene expression patterns. We present Epigram, an analysis pipeline that predicts histone modification and DNA methylation patterns from DNA motifs. The identified cis elements represent interactions with the site-specific DNA-binding factors that establish and maintain epigenomic modifications. We cataloged the cis elements in embryonic stem cells and four derived lineages and found numerous motifs that have location preference, such as at the center of H3K27ac or at the edges of H3K4me3 and H3K9me3, which provides mechanistic insight about the shaping of the epigenome. The Epigram pipeline and predictive motifs are at http://wanglab.ucsd.edu/star/epigram/.

Ubiquitous human 'master' origins of replication are encoded in the DNA sequence via a local enrichment in nucleosome excluding energy barriers

As the elementary building block of eukaryotic chromatin, the nucleosome is at the heart of the compromise between the necessity of compacting DNA in the cell nucleus and the required accessibility to regulatory proteins. The recent availability of genome-wide experimental maps of nucleosome positions for many different organisms and cell types has provided an unprecedented opportunity to elucidate to what extent the DNA sequence conditions the primary structure of chromatin and in turn participates in the chromatin-mediated regulation of nuclear functions, such as gene expression and DNA replication. In this study, we use in vivo and in vitro genome-wide nucleosome occupancy data together with the set of nucleosome-free regions (NFRs) predicted by a physical model of nucleosome formation based on sequence-dependent bending properties of the DNA double-helix, to investigate the role of intrinsic nucleosome occupancy in the regulation of the replication spatio-temporal programme in human. We focus our analysis on the so-called replication U/N-domains that were shown to cover about half of the human genome in the germline (skew-N domains) as well as in embryonic stem cells, somatic and HeLa cells (mean replication timing U-domains). The 'master' origins of replication (MaOris) that border these megabase-sized U/N-domains were found to be specified by a few hundred kb wide regions that are hyper-sensitive to DNase I cleavage, hypomethylated, and enriched in epigenetic marks involved in transcription regulation, the hallmarks of localized open chromatin structures. Here we show that replication U/N-domain borders that are conserved in all considered cell lines have an environment highly enriched in nucleosome-excluding-energy barriers, suggesting that these ubiquitous MaOris have been selected during evolution. In contrast, MaOris that are cell-type-specific are mainly regulated epigenetically and are no longer favoured by a local abundance of intrinsic NFRs encoded in the DNA sequence. At the smaller few hundred bp scale of gene promoters, CpG-rich promoters of housekeeping genes found nearby ubiquitous MaOris as well as CpG-poor promoters of tissue-specific genes found nearby cell-type-specific MaOris, both correspond to in vivo NFRs that are not coded as nucleosome-excluding-energy barriers. Whereas the former promoters are likely to correspond to high occupancy transcription factor binding regions, the latter are an illustration that gene regulation in human is typically cell-type-specific.

Predicting nucleosome positioning based on geometrically transformed Tsallis entropy

As the fundamental unit of eukaryotic chromatin structure, nucleosome plays critical roles in gene expression and regulation by controlling physical access to transcription factors. In this paper, based on the geometrically transformed Tsallis entropy and two index-vectors, a valid nucleosome positioning information model is developed to describe the distribution of A/T-riched and G/C-riched dimeric and trimeric motifs along the DNA duplex. When applied to train the support vector machine, the model achieves high AUCs across five organisms, which have significantly outperformed the previous studies. Besides, we adopt the concept of relative distance to describe the probability of arbitrary DNA sequence covered by nucleosome. Thus, the average nucleosome occupancy profile over the S.cerevisiae genome is calculated. With our peak detection model, the isolated nucleosomes along genome sequence are located. When compared with some published results, it shows that our model is effective for nucleosome positioning. The index-vector component is identified to be an important influencing factor of nucleosome organizations.

Prediction of nucleosome rotational positioning in yeast and human genomes based on sequence-dependent DNA anisotropy

BACKGROUND

An organism's DNA sequence is one of the key factors guiding the positioning of nucleosomes within a cell's nucleus. Sequence-dependent bending anisotropy dictates how DNA is wrapped around a histone octamer. One of the best established sequence patterns consistent with this anisotropy is the periodic occurrence of AT-containing dinucleotides (WW) and GC-containing dinucleotides (SS) in the nucleosomal locations where DNA is bent in the minor and major grooves, respectively. Although this simple pattern has been observed in nucleosomes across eukaryotic genomes, its use for prediction of nucleosome positioning was not systematically tested.

RESULTS

We present a simple computational model, termed the W/S scheme, implementing this pattern, without using any training data. This model accurately predicts the rotational positioning of nucleosomes both in vitro and in vivo, in yeast and human genomes. About 65 - 75% of the experimentally observed nucleosome positions are predicted with the precision of one to two base pairs. The program is freely available at http://people.rit.edu/fxcsbi/WS_scheme/. We also introduce a simple and efficient way to compare the performance of different models predicting the rotational positioning of nucleosomes.

CONCLUSIONS

This paper presents the W/S scheme to achieve accurate prediction of rotational positioning of nucleosomes, solely based on the sequence-dependent anisotropic bending of nucleosomal DNA. This method successfully captures DNA features critical for the rotational positioning of nucleosomes, and can be further improved by incorporating additional terms related to the translational positioning of nucleosomes in a species-specific manner.

Computational discovery of feature patterns in nucleosomal DNA sequences

The identification of important factors that affect nucleosome formation is critical to clarify nucleosome-forming mechanisms and the role of the nucleosome in gene regulation. Various features reported in the literature led to our hypothesis that multiple features can together contribute to nucleosome formation. Therefore, we compiled 779 features and developed a pattern discovery and scoring algorithm FFN (Finding Features for Nucleosomes) to identify feature patterns that are differentially enriched in nucleosome-forming sequences and nucleosome-depletion sequences. Applying FFN to genome-wide nucleosome occupancy data in yeast and human, we identified statistically significant feature patterns that may influence nucleosome formation, many of which are common to the two species. We found that both sequence and structural features are important in nucleosome occupancy prediction. We discovered that, even for the same feature combinations, variations in feature values may lead to differences in predictive power. We demonstrated that the identified feature patterns could be used to assist nucleosomal sequence prediction.

A Novel Wavelet-Based Approach for Predicting Nucleosome Positions Using DNA Structural Information

Nucleosomes are basic elements of chromatin structure. The positioning of nucleosomes along a genome is very important to dictate eukaryotic DNA compaction and access. Current computational methods have focused on the analysis of nucleosome occupancy and the positioning of well-positioned nucleosomes. However, fuzzy nucleosomes require more complex configurations and are more difficult to predict their positions. We analyzed the positioning of well-positioned and fuzzy nucleosomes from a novel structural perspective, and proposed WaveNuc, a computational approach for inferring their positions based on continuous wavelet transformation. The comparative analysis demonstrates that these two kinds of nucleosomes exhibit different propeller twist structural characteristics. Well-positioned nucleosomes tend to locate at sharp peaks of the propeller twist profile, whereas fuzzy nucleosomes correspond to broader peaks. The sharpness of these peaks shows that the propeller twist profile may contain nucleosome positioning information. Exploiting this knowledge, we applied WaveNuc to detect the two different kinds of peaks of the propeller twist profile along the genome. We compared the performance of our method with existing methods on real data sets. The results show that the proposed method can accurately resolve complex configurations of fuzzy nucleosomes, which leads to better performance of nucleosome positioning prediction on the whole genome.

Applications of alignment-free methods in epigenomics

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.

iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition

MOTIVATION

Nucleosome positioning participates in many cellular activities and plays significant roles in regulating cellular processes. With the avalanche of genome sequences generated in the post-genomic age, it is highly desired to develop automated methods for rapidly and effectively identifying nucleosome positioning. Although some computational methods were proposed, most of them were species specific and neglected the intrinsic local structural properties that might play important roles in determining the nucleosome positioning on a DNA sequence.

RESULTS

Here a predictor called 'iNuc-PseKNC' was developed for predicting nucleosome positioning in Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster genomes, respectively. In the new predictor, the samples of DNA sequences were formulated by a novel feature-vector called 'pseudo k-tuple nucleotide composition', into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on the three stringent benchmark datasets that the overall success rates achieved by iNuc-PseKNC in predicting the nucleosome positioning of the aforementioned three genomes were 86.27%, 86.90% and 79.97%, respectively. Meanwhile, the results obtained by iNuc-PseKNC on various benchmark datasets used by the previous investigators for different genomes also indicated that the current predictor remarkably outperformed its counterparts.

AVAILABILITY

A user-friendly web-server, iNuc-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iNuc-PseKNC.

Analysis of chromatin-state plasticity identifies cell-type-specific regulators of H3K27me3 patterns

Chromatin states are highly cell-type-specific, but the underlying mechanisms for the establishment and maintenance of their genome-wide patterns remain poorly understood. Here we present a computational approach for investigation of chromatin-state plasticity. We applied this approach to investigate an ENCODE ChIP-seq dataset profiling the genome-wide distributions of the H3K27me3 mark in 19 human cell lines. We found that the high plasticity regions (HPRs) can be divided into two functionally and mechanistically distinct subsets, which correspond to CpG island (CGI) proximal or distal regions, respectively. Although the CGI proximal HPRs are typically associated with continuous variation across different cell-types, the distal HPRs are associated with binary-like variations. We developed a computational approach to predict putative cell-type-specific modulators of H3K27me3 patterns and validated the predictions by comparing with public ChIP-seq data. Furthermore, we applied this approach to investigate mechanisms for poised enhancer establishment in primary human erythroid precursors. Importantly, we predicted and experimentally validated that the principal hematopoietic regulator T-cell acute lymphocytic leukemia-1 (TAL1) is involved in regulating H3K27me3 variations in collaboration with the transcription factor growth factor independent 1B (GFI1B), providing fresh insights into the context-specific role of TAL1 in erythropoiesis. Our approach is generally applicable to investigate the regulatory mechanisms of epigenetic pathways in establishing cellular identity.

The spring-loaded genome: nucleosome redistributions are widespread, transient, and DNA-directed

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.

A comparative evaluation on prediction methods of nucleosome positioning

Nucleosome positioning plays an essential role in cellular processes by modulating accessibility of DNA to proteins. Many computational models have been developed to predict genome-wide nucleosome positions from DNA sequences. Comparative analysis of predicted and experimental nucleosome positioning maps facilitates understanding the regulatory mechanisms of transcription and DNA replication. Therefore, a comprehensive evaluation of existing computational methods is important and useful for biologists to choose appropriate ones in their research. In this article, we carried out a performance comparison among eight widely used computational methods on four species including yeast, fruitfly, mouse and human. In particular, we compared these methods on different regions of each species such as gene sequences, promoters and 5'UTR exons. The experimental results show that the performances of the two latest versions of the thermodynamic model are relatively steadier than the other four methods. Moreover, these methods are workable on four species, but their performances decrease gradually from yeast to human, indicating that the fundamental mechanism of nucleosome positioning is conserved through the evolution process, but more and more factors participate in the determination of nucleosome positions, which leads to sophisticated regulation mechanisms.

Archaeal nucleosome positioning in vivo and in vitro is directed by primary sequence motifs

Background

Histone wrapping of DNA into nucleosomes almost certainly evolved in the Archaea, and predates Eukaryotes. In Eukaryotes, nucleosome positioning plays a central role in regulating gene expression and is directed by primary sequence motifs that together form a nucleosome positioning code. The experiments reported were undertaken to determine if archaeal histone assembly conforms to the nucleosome positioning code.

Results

Eukaryotic nucleosome positioning is favored and directed by phased helical repeats of AA/TT/AT/TA and CC/GG/CG/GC dinucleotides, and disfavored by longer AT-rich oligonucleotides. Deep sequencing of genomic DNA protected from micrococcal nuclease digestion by assembly into archaeal nucleosomes has established that archaeal nucleosome assembly is also directed and positioned by these sequence motifs, both in vivo in Methanothermobacter thermautotrophicus and Thermococcus kodakarensis and in vitro in reaction mixtures containing only one purified archaeal histone and genomic DNA. Archaeal nucleosomes assembled at the same locations in vivo and in vitro, with much reduced assembly immediately upstream of open reading frames and throughout the ribosomal rDNA operons. Providing further support for a common positioning code, archaeal histones assembled into nucleosomes on eukaryotic DNA and eukaryotic histones into nucleosomes on archaeal DNA at the same locations. T. kodakarensis has two histones, designated HTkA and HTkB, and strains with either but not both histones deleted grow normally but do exhibit transcriptome differences. Comparisons of the archaeal nucleosome profiles in the intergenic regions immediately upstream of genes that exhibited increased or decreased transcription in the absence of HTkA or HTkB revealed substantial differences but no consistent pattern of changes that would correlate directly with archaeal nucleosome positioning inhibiting or stimulating transcription.

Conclusions

The results obtained establish that an archaeal histone and a genome sequence together are sufficient to determine where archaeal nucleosomes preferentially assemble and where they avoid assembly. We confirm that the same nucleosome positioning code operates in Archaea as in Eukaryotes and presumably therefore evolved with the histone-fold mechanism of DNA binding and compaction early in the archaeal lineage, before the divergence of Eukaryotes.

Genome-wide prediction of nucleosome occupancy in maize reveals plant chromatin structural features at genes and other elements at multiple scales

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.

Dynamic nucleosome organization at hox promoters during zebrafish embryogenesis

Nucleosome organization at promoter regions plays an important role in regulating gene activity. Genome-wide studies in yeast, flies, worms, mammalian embryonic stem cells and transformed cell lines have found well-positioned nucleosomes flanking a nucleosome depleted region (NDR) at transcription start sites. This nucleosome arrangement depends on DNA sequence (cis-elements) as well as DNA binding factors and ATP-dependent chromatin modifiers (trans-factors). However, little is understood about how the nascent embryonic genome positions nucleosomes during development. This is particularly intriguing since the embryonic genome must undergo a broad reprogramming event upon fusion of sperm and oocyte. Using four stages of early embryonic zebrafish development, we map nucleosome positions at the promoter region of 37 zebrafish hox genes. We find that nucleosome arrangement at the hox promoters is a progressive process that takes place over several stages. At stages immediately after fertilization, nucleosomes appear to be largely disordered at hox promoter regions. At stages after activation of the embryonic genome, nucleosomes are detectable at hox promoters, with positions becoming more uniform and more highly occupied. Since the genomic sequence is invariant during embryogenesis, this progressive change in nucleosome arrangement suggests that trans-factors play an important role in organizing nucleosomes during embryogenesis. Separating hox genes into expressed and non-expressed groups shows that expressed promoters have better positioned and occupied nucleosomes, as well as distinct NDRs, than non-expressed promoters. Finally, by blocking the retinoic acid-signaling pathway, we disrupt early hox gene transcription, but observe no effect on nucleosome positions, suggesting that active hox transcription is not a driving force behind the arrangement of nucleosomes at the promoters of hox genes during early development.

Prediction of Epigenetic Target Sites by Using Genomic DNA Sequence

Epigenetic regulation provides an extra layer of gene control in addition to the genomic sequence and is critical for the maintenance of cell-type specific gene expression programs. Significant changes of epigenetic patterns have been linked to developmental stages, environmental exposure, ageing, and diet. However, the regulatory mechanisms for epigenetic recruitment, maintenance, and switch are still poorly understood. Computational biology provides tools to deeply uncover hidden connections and these tools have played a major role in shaping the current understanding of gene regulation, but its application in epigenetics is still in the infancy. This chapter reviews some recent developments of computational approaches to predict epigenetic target sites.

An analysis and prediction of nucleosome positioning based on information content

Nucleosome positioning plays a key role in the regulation of many biological processes. In this study, the statistical difference of information content was investigated in nucleosome and linker DNA regions across eukaryotic organisms. By analyzing the information redundancy, D k , in Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans genomes, the short-range dominance of nucleotide correlation in nucleosome and linker DNA regions was confirmed. Significant difference of the D k value between the nucleosome and linker DNA regions was also found. The underlying reason for many successful oligonucleotide-based predictions of nucleosome positioning in eukaryotic model organisms may be attributed to the short-range dominance of nucleotide correlation in the nucleosome and linker DNA regions. When applying power spectrum analysis to the nucleosome and linker DNA regions, some obvious differences in sequence periodic signals were observed. The parameter F k was introduced to describe particular base correlation. Furthermore, the support vector machine combining F k was used to classify nucleosome and linker DNA regions in Homo sapiens, Oryzias latipes, C. elegans, Candida albicans, and S. cerevisiae. Independent test demonstrated that a good performance can be achieved by using this algorithm. This result further revealed that base correlation information has an important role in nucleosome positioning.

The roles of the monomer length and nucleotide context of plant tandem repeats in nucleosome positioning

Similar to regularly spaced nucleosomes in chromatin, long tandem DNA arrays are composed of regularly alternating monomers that have almost identical primary DNA structures. Such a similarity in the structural organization makes these arrays especially interesting for studying the role of intrinsic DNA preferences in nucleosome positioning. We have studied the nucleosome formation potential of DNA tandem repeat families with different monomer lengths (ML). In total, 165 plant tandem repeat families from the PlantSat database (http://w3lamc.umbr.cas.cz/PlantSat/) were divided into two classes based on the number of nucleosome repeats in one DNA monomer. For predicting nucleosome formation potential, we developed the Phase method, which combines the advantages of multiple bioinformatics models. The Phase method was able to distinguish interfamily differences and intrafamily monomer variation and identify the influence of nucleotide context on nucleosome formation potential. Three main types of nucleosome arrangement in DNA tandem repeat arrays--regular, partially regular (partial), and flexible--were distinguished among a great variety of Phase profiles. The regular type, in which all nucleosomes of the monomer array are positioned in a context-dependent manner, is the most representative type of the class 1 families, with ML equal to or a multiple of the nucleosome repeat length (NRL). In the partially regular type, nucleotide context influences the positioning of only a subset of nucleosomes. The influence of the nucleotide context on nucleosome positioning has the least effect in the flexible type, which contains the greatest number of families (65). The majority of these families belong to class 2 and have nonmultiple ML to NRL ratios.

NucleoFinder: a statistical approach for the detection of nucleosome positions

Motivation: The identification of nucleosomes along the chromatin is key to understanding their role in the regulation of gene expression and other DNA-related processes. However, current experimental methods (MNase-ChIP, MNase-Seq) sample nucleosome positions from a cell population and contain biases, making thus the precise identification of individual nucleosomes not straightforward. Recent works have only focused on the first point, where noise reduction approaches have been developed to identify nucleosome positions.

Results: In this article, we propose a new approach, termed NucleoFinder, that addresses both the positional heterogeneity across cells and experimental biases by seeking nucleosomes consistently positioned in a cell population and showing a significant enrichment relative to a control sample. Despite the absence of validated dataset, we show that our approach (i) detects fewer false positives than two other nucleosome calling methods and (ii) identifies two important features of the nucleosome organization (the nucleosome spacing downstream of active promoters and the enrichment/depletion of GC/AT dinucleotides at the centre of in vitro nucleosomes) with equal or greater ability than the other two methods.

Availability: The R code of NucleoFinder, an example datafile and instructions are available for download from https://sites.google.com/site/beckerjeremie/

Contact: cholmes@stats.ox.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Predicting nucleosome positions in yeast: using the absolute frequency

Nucleosome is the basic structure of chromatin in eukaryotic cells, and they form the chromatin fiber interconnected by sections of linker DNA. Nucleosome positioning is of great significance for gene transcription regulation. In this paper, we consider the difference of absolute frequency of nucleotides between the nucleosome forming and nucleosome inhibiting sequences. Based on the 2-mer absolute frequency of nucleotides in genome, a new model is constructed to distinguish nucleosome DNA and linker DNA. When used to predict DNA potential for forming nucleosomes in S. cerevisiae, the model achieved a high accuracy of 96.05%. Thus, the model is very useful for predicting nucleosome positioning.

iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties

Nucleosome positioning has important roles in key cellular processes. Although intensive efforts have been made in this area, the rules defining nucleosome positioning is still elusive and debated. In this study, we carried out a systematic comparison among the profiles of twelve DNA physicochemical features between the nucleosomal and linker sequences in the Saccharomyces cerevisiae genome. We found that nucleosomal sequences have some position-specific physicochemical features, which can be used for in-depth studying nucleosomes. Meanwhile, a new predictor, called iNuc-PhysChem, was developed for identification of nucleosomal sequences by incorporating these physicochemical properties into a 1788-D (dimensional) feature vector, which was further reduced to a 884-D vector via the IFS (incremental feature selection) procedure to optimize the feature set. It was observed by a cross-validation test on a benchmark dataset that the overall success rate achieved by iNuc-PhysChem was over 96% in identifying nucleosomal or linker sequences. As a web-server, iNuc-PhysChem is freely accessible to the public at http://lin.uestc.edu.cn/server/iNuc-PhysChem. For the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented just for the integrity in developing the predictor. Meanwhile, for those who prefer to run predictions in their own computers, the predictor's code can be easily downloaded from the web-server. It is anticipated that iNuc-PhysChem may become a useful high throughput tool for both basic research and drug design.

Sequence-based prediction of single nucleosome positioning and genome-wide nucleosome occupancy

Nucleosome positioning dictates eukaryotic DNA compaction and access. To predict nucleosome positions in a statistical mechanics model, we exploited the knowledge that nucleosomes favor DNA sequences with specific periodically occurring dinucleotides. Our model is the first to capture both dyad position within a few base pairs, and free binding energy within 2 k(B)T, for all the known nucleosome positioning sequences. By applying Percus's equation to the derived energy landscape, we isolate sequence effects on genome-wide nucleosome occupancy from other factors that may influence nucleosome positioning. For both in vitro and in vivo systems, three parameters suffice to predict nucleosome occupancy with correlation coefficients of respectively 0.74 and 0.66. As predicted, we find the largest deviations in vivo around transcription start sites. This relatively simple algorithm can be used to guide future studies on the influence of DNA sequence on chromatin organization.

Active nucleosome positioning beyond intrinsic biophysics is revealed by in vitro reconstitution

Genome-wide nucleosome maps revealed well-positioned nucleosomes as a major theme in eukaryotic genome organization. Promoter regions often show a conserved pattern with an NDR (nucleosome-depleted region) from which regular nucleosomal arrays emanate. Three mechanistic contributions to such NDR-array-organization and nucleosome positioning in general are discussed: DNA sequence, DNA binders and DNA-templated processes. Especially, intrinsic biophysics of DNA sequence preferences for nucleosome formation was prominently suggested to explain the majority of nucleosome positions ('genomic code for nucleosome positioning'). Nonetheless, non-histone factors that bind DNA with high or low specificity, such as transcription factors or remodelling enzymes respectively and processes such as replication, transcription and the so-called 'statistical positioning' may be involved too. Recently, these models were tested for yeast by genome-wide reconstitution. DNA sequence preferences as probed by SGD (salt gradient dialysis) reconstitution generated many NDRs, but only few individual nucleosomes, at their proper positions, and no arrays. Addition of a yeast extract and ATP led to dramatically more in vivo-like nucleosome positioning, including regular arrays for the first time. This improvement depended essentially on the extract and ATP but not on transcription or replication. Nucleosome occupancy and close spacing were maintained around promoters, even at lower histone density, arguing for active packing of nucleosomes against the 5' ends of genes rather than statistical positioning. A first extract fractionation identified a direct, specific, necessary, but not sufficient role for the RSC (remodels the structure of chromatin) remodelling enzyme. Collectively, nucleosome positioning in yeast is actively determined by factors beyond intrinsic biophysics, and in steady-state rather than at equilibrium.

Manipulating nucleosome disfavoring sequences allows fine-tune regulation of gene expression in yeast

Understanding how precise control of gene expression is specified within regulatory DNA sequences is a key challenge with far-reaching implications. Many studies have focused on the regulatory role of transcription factor-binding sites. Here, we explore the transcriptional effects of different elements, nucleosome-disfavoring sequences and, specifically, poly(dA:dT) tracts that are highly prevalent in eukaryotic promoters. By measuring promoter activity for a large-scale promoter library, designed with systematic manipulations to the properties and spatial arrangement of poly(dA:dT) tracts, we show that these tracts significantly and causally affect transcription. We show that manipulating these elements offers a general genetic mechanism, applicable to promoters regulated by different transcription factors, for tuning expression in a predictable manner, with resolution that can be even finer than that attained by altering transcription factor sites. Overall, our results advance the understanding of the regulatory code and suggest a potential mechanism by which promoters yielding prespecified expression patterns can be designed.

Structure-based identification of new high-affinity nucleosome binding sequences

The substrate for the proteins that express genetic information in the cell is not naked DNA but an assembly of nucleosomes, where the DNA is wrapped around histone proteins. The organization of these nucleosomes on genomic DNA is influenced by the DNA sequence. Here, we present a structure-based computational approach that translates sequence information into the energy required to bend DNA into a nucleosome-bound conformation. The calculations establish the relationship between DNA sequence and histone octamer binding affinity. In silico selection using this model identified several new DNA sequences, which were experimentally found to have histone octamer affinities comparable to the highest-affinity sequences known. The results provide insights into the molecular mechanism through which DNA sequence information encodes its organization. A quantitative appreciation of the thermodynamics of nucleosome positioning and rearrangement will be one of the key factors in understanding the regulation of transcription and in the design of new promoter architectures for the purposes of tuning gene expression dynamics.