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

Modeling the dynamics of bivalent histone modifications

Epigenetic modifications to histones may promote either activation or repression of the transcription of nearby genes. Recent experimental studies show that the promoters of many lineage-control genes in stem cells have "bivalent domains" in which the nucleosomes contain both active (H3K4me3) and repressive (H3K27me3) marks. It is generally agreed that bivalent domains play an important role in stem cell differentiation, but the underlying mechanisms remain unclear. Here we formulate a mathematical model to investigate the dynamic properties of histone modification patterns. We then illustrate that our modeling framework can be used to capture key features of experimentally observed combinatorial chromatin states.Comment: 23 pages, 10 figure

Epigenetic domains found in mouse embryonic stem cells via a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Epigenetics is an important layer of transcriptional control necessary for cell-type specific gene regulation. Recent studies have shown significant epigenetic patterns associated with developmental stages and diseases. However, previous studies have been mostly limited to focal epigenetic patterns, whereas methods for analyzing large-scale organizations are still lacking.</p> <p>Results</p> <p>We developed a hidden Markov model (HMM) approach for detecting the types and locations of epigenetic domains from multiple histone modifications. We used this method to analyze a published ChIP-seq dataset of five histone modification marks (H3K4me2, H3K4me3, H3K27me3, H3K9me3, and H3K36me3) in mouse embryonic stem (ES) cells. We identified three types of domains, corresponding to active, non-active, and null states. In total, our three-state HMM identified 258 domains in the mouse genome containing 9.6 genes on average. These domains were validated by a number of criteria. The largest domains correspond to olfactory receptor (OR) gene clusters. Each <it>Hox </it>gene cluster also forms a separate epigenetic domain. We found that each type of domain is associated with distinct biological functions and structural changes during early cell differentiation.</p> <p>Conclusions</p> <p>The HMM approach successfully detects domains of consistent epigenetic patterns from ChIP-seq data, providing new insights into the role of epigenetics in long-range gene regulation.</p

Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis

Background: A fundamental challenge for cancer therapy is that each tumor contains a highly heterogeneous cell population whose structure and mechanistic underpinnings remain incompletely understood. Recent advances in single-cell gene expression profiling have created new possibilities to characterize this heterogeneity and to dissect the potential intra-cancer cellular hierarchy. Results: Here, we apply single-cell analysis to systematically characterize the heterogeneity within leukemic cells using the MLL-AF9 driven mouse model of acute myeloid leukemia. We start with fluorescence-activated cell sorting analysis with seven surface markers, and extend by using a multiplexing quantitative polymerase chain reaction approach to assay the transcriptional profile of a panel of 175 carefully selected genes in leukemic cells at the single-cell level. By employing a set of computational tools we find striking heterogeneity within leukemic cells. Mapping to the normal hematopoietic cellular hierarchy identifies two distinct subtypes of leukemic cells; one similar to granulocyte/monocyte progenitors and the other to macrophage and dendritic cells. Further functional experiments suggest that these subtypes differ in proliferation rates and clonal phenotypes. Finally, co-expression network analysis reveals similarities as well as organizational differences between leukemia and normal granulocyte/monocyte progenitor networks. Conclusions: Overall, our single-cell analysis pinpoints previously uncharacterized heterogeneity within leukemic cells and provides new insights into the molecular signatures of acute myeloid leukemia. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0525-9) contains supplementary material, which is available to authorized users

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