Genomic Sequence is Highly Predictive of Local Nucleosome Depletion

The regulation of DNA accessibility through nucleosome positioning is important for transcription control. Computational models have been developed to predict genome-wide nucleosome positions from DNA sequences, but these models consider only nucleosome sequences, which may have limited their power. We developed a statistical multi-resolution approach to identify a sequence signature, called the N-score, that distinguishes nucleosome binding DNA from non-nucleosome DNA. This new approach has significantly improved the prediction accuracy. The sequence information is highly predictive for local nucleosome enrichment or depletion, whereas predictions of the exact positions are only modestly more accurate than a null model, suggesting the importance of other regulatory factors in fine-tuning the nucleosome positions. The N-score in promoter regions is negatively correlated with gene expression levels. Regulatory elements are enriched in low N-score regions. While our model is derived from yeast data, the N-score pattern computed from this model agrees well with recent high-resolution protein-binding data in human.Statistic

Genomic Sequence Is Highly Predictive of Local Nucleosome Depletion

The regulation of DNA accessibility through nucleosome positioning is important for transcription control. Computational models have been developed to predict genome-wide nucleosome positions from DNA sequences, but these models consider only nucleosome sequences, which may have limited their power. We developed a statistical multi-resolution approach to identify a sequence signature, called the N-score, that distinguishes nucleosome binding DNA from non-nucleosome DNA. This new approach has significantly improved the prediction accuracy. The sequence information is highly predictive for local nucleosome enrichment or depletion, whereas predictions of the exact positions are only modestly more accurate than a null model, suggesting the importance of other regulatory factors in fine-tuning the nucleosome positions. The N-score in promoter regions is negatively correlated with gene expression levels. Regulatory elements are enriched in low N-score regions. While our model is derived from yeast data, the N-score pattern computed from this model agrees well with recent high-resolution protein-binding data in human

Nucleosomal Context of Binding Sites Influences Transcription Factor Binding Affinity and Gene Regulation

Transcription factor (TF) binding to its DNA target site plays an essential role in gene regulation. The location, orientation and spacing of transcription factor binding sites (TFBSs) also affect regulatory function of the TF. However, how nucleosomal context of TFBSs influences TF binding and subsequent gene regulation remains to be elucidated. Using genome-wide nucleosome positioning and TF binding data in budding yeast, we found that binding affinities of TFs to DNA tend to decrease with increasing nucleosome occupancy of the associated binding sites. We further demonstrated that nucleosomal context of binding sites is correlated with gene regulation of the corresponding TF. Nucleosome-depleted TFBSs are linked to high gene activity and low expression noise, whereas nucleosome-covered TFBSs are associated with low gene activity and high expression noise. Moreover, nucleosome-covered TFBSs tend to disrupt coexpression of the corresponding TF target genes. We conclude that nucleosomal context of binding sites influences TF binding affinity, subsequently affecting the regulation of TFs on their target genes. This emphasizes the need to include nucleosomal context of TFBSs in modeling gene regulation

A motif-independent metric for DNA sequence specificity

<p>Abstract</p> <p>Background</p> <p>Genome-wide mapping of protein-DNA interactions has been widely used to investigate biological functions of the genome. An important question is to what extent such interactions are regulated at the DNA sequence level. However, current investigation is hampered by the lack of computational methods for systematic evaluating sequence specificity.</p> <p>Results</p> <p>We present a simple, unbiased quantitative measure for DNA sequence specificity called the Motif Independent Measure (MIM). By analyzing both simulated and real experimental data, we found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs. We also found that the level of specificity associated with H3K4me1 target sequences is highly cell-type specific and highest in embryonic stem (ES) cells. We predicted H3K4me1 target sequences by using the N- score model and found that the prediction accuracy is indeed high in ES cells.The software to compute the MIM is freely available at: <url>https://github.com/lucapinello/mim</url>. </p> <p>Conclusions</p> <p>Our method provides a unified framework for quantifying DNA sequence specificity and serves as a guide for development of sequence-based prediction models.</p

Sequence-dependent histone variant positioning signatures

Background: Nucleosome, the fundamental unit of chromatin, is formed by wrapping nearly 147bp of DNA around an octamer of histone proteins. This histone core has many variants that are different from each other by their biochemical compositions as well as biological functions. Although the deposition of histone variants onto chromatin has been implicated in many important biological processes, such as transcription and replication, themechanisms of how they are deposited on target sites are still obscure. Results: By analyzing genomic sequences of nucleosomes bearing different histone variants from human, including H2A.Z, H3.3 and both (H3.3/H2A.Z, so-called double variant histones), we found that genomic sequencecontributes in part to determining target sites for different histone variants. Moreover, dinucleotides CA/TG are remarkably important in distinguishing target sites of H2A.Z-only nucleosomes with those of H3.3-containing (both H3.3-only and double variant) nucleosomes. Conclusions: There exists a DNA-related mechanism regulating the deposition of different histone variants onto chromatin and biological outcomes thereof. This provides additional insights into epigenetic regulatory mechanisms of many important cellular processes

Two distinct modes of nucleosome modulation associated with different degrees of dependence of nucleosome positioning on the underlying DNA sequence

<p>Abstract</p> <p>Background</p> <p>The nucleosome is the fundamental unit of eukaryotic genomes. Its positioning plays a central role in diverse cellular processes that rely on access to genomic DNA. Experimental evidence suggests that the genomic DNA sequence is one important determinant of nucleosome positioning. Yet it is less clear whether the role of the underlying DNA sequence in nucleosome positioning varies across different promoters. Whether different determinants of nucleosome positioning have characteristic influences on nucleosome modulation also remains to be elucidated.</p> <p>Results</p> <p>We identified two typical promoter classes in yeast associated with high or low dependence of nucleosome positioning on the underlying DNA sequence, respectively. Importantly, the two classes have low or high intrinsic sequence preferences for nucleosomes, respectively. The two classes are further distinguished by multiple promoter features, including nucleosome occupancy, nucleosome fuzziness, H2A.Z occupancy, changes in nucleosome positions before and after transcriptional perturbation, and gene activity. Both classes have significantly high turnover rates of histone H3, but employ distinct modes of nucleosome modulation: The first class is characterized by hyperacetylation, whereas the second class is highly regulated by ATP-dependent chromatin remodelling.</p> <p>Conclusion</p> <p>Our results, coupled with the known features of nucleosome modulation, suggest that the two distinct modes of nucleosome modulation selectively employed by different genes are linked with the intrinsic sequence preferences for nucleosomes. The difference in modes of nucleosome modulation can account for the difference in the contribution of DNA sequence to nucleosome positioning between both promoter classes.</p

Predicting nucleosome positioning using a duration Hidden Markov Model

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p