G+C content dominates intrinsic nucleosome occupancy

<p>Abstract</p> <p>Background</p> <p>The relative preference of nucleosomes to form on individual DNA sequences plays a major role in genome packaging. A wide variety of DNA sequence features are believed to influence nucleosome formation, including periodic dinucleotide signals, poly-A stretches and other short motifs, and sequence properties that influence DNA structure, including base content. It was recently shown by Kaplan et al. that a probabilistic model using composition of all 5-mers within a nucleosome-sized tiling window accurately predicts intrinsic nucleosome occupancy across an entire genome <it>in vitro</it>. However, the model is complicated, and it is not clear which specific DNA sequence properties are most important for intrinsic nucleosome-forming preferences.</p> <p>Results</p> <p>We find that a simple linear combination of only 14 simple DNA sequence attributes (G+C content, two transformations of dinucleotide composition, and the frequency of eleven 4-bp sequences) explains nucleosome occupancy <it>in vitro </it>and <it>in vivo </it>in a manner comparable to the Kaplan model. G+C content and frequency of AAAA are the most important features. G+C content is dominant, alone explaining ~50% of the variation in nucleosome occupancy <it>in vitro</it>.</p> <p>Conclusions</p> <p>Our findings provide a dramatically simplified means to predict and understand intrinsic nucleosome occupancy. G+C content may dominate because it both reduces frequency of poly-A-like stretches and correlates with many other DNA structural characteristics. Since G+C content is enriched or depleted at many types of features in diverse eukaryotic genomes, our results suggest that variation in nucleotide composition may have a widespread and direct influence on chromatin structure.</p

SICTIN: Rapid footprinting of massively parallel sequencing data

BACKGROUND: Massively parallel sequencing allows for genome-wide hypothesis-free investigation of for instance transcription factor binding sites or histone modifications. Although nucleotide resolution detailed information can easily be generated, biological insight often requires a more general view of patterns (footprints) over distinct genomic features such as transcription start sites, exons or repetitive regions. The construction of these footprints is however a time consuming task. METHODS: The presented software generates a binary representation of the signals enabling fast and scalable lookup. This representation allows for footprint generation in mere minutes on a desktop computer. Several different input formats are accepted, e.g. the SAM format, bed-files and the UCSC wiggle track. CONCLUSIONS: Hypothesis-free investigation of genome wide interactions allows for biological data mining at a scale never before seen. Until recently, the main focus of analysis of sequencing data has been targeted on signal patterns around transcriptional start sites which are in manageable numbers. Today, focus is shifting to a wider perspective and numerous genomic features are being studied. To this end, we provide a system allowing for fast querying in the order of hundreds of thousands of features

CATCHprofiles: Clustering and Alignment Tool for ChIP Profiles

Chromatin Immuno Precipitation (ChIP) profiling detects in vivo protein-DNA binding, and has revealed a large combinatorial complexity in the binding of chromatin associated proteins and their post-translational modifications. To fully explore the spatial and combinatorial patterns in ChIP-profiling data and detect potentially meaningful patterns, the areas of enrichment must be aligned and clustered, which is an algorithmically and computationally challenging task. We have developed CATCHprofiles, a novel tool for exhaustive pattern detection in ChIP profiling data. CATCHprofiles is built upon a computationally efficient implementation for the exhaustive alignment and hierarchical clustering of ChIP profiling data. The tool features a graphical interface for examination and browsing of the clustering results. CATCHprofiles requires no prior knowledge about functional sites, detects known binding patterns “ab initio”, and enables the detection of new patterns from ChIP data at a high resolution, exemplified by the detection of asymmetric histone and histone modification patterns around H2A.Z-enriched sites. CATCHprofiles' capability for exhaustive analysis combined with its ease-of-use makes it an invaluable tool for explorative research based on ChIP profiling data

ARID1B is a specific vulnerability in ARID1A-mutant cancers

Summary Recent studies have revealed that ARID1A is frequently mutated across a wide variety of human cancers and also has bona fide tumor suppressor properties. Consequently, identification of vulnerabilities conferred by ARID1A mutation would have major relevance for human cancer. Here, using a broad screening approach, we identify ARID1B, a related but mutually exclusive homolog of ARID1A in the SWI/SNF chromatin remodeling complex, as the number one gene preferentially required for the survival of ARID1A-mutant cancer cell lines. We show that loss of ARID1B in ARID1A-deficient backgrounds destabilizes SWI/SNF and impairs proliferation. Intriguingly, we also find that ARID1A and ARID1B are frequently co-mutated in cancer, but that ARID1A-deficient cancers retain at least one ARID1B allele. These results suggest that loss of ARID1A and ARID1B alleles cooperatively promotes cancer formation but also results in a unique functional dependence. The results further identify ARID1B as a potential therapeutic target for ARID1A-mutant cancers

ChIP-chip versus ChIP-seq: Lessons for experimental design and data analysis

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation (ChIP) followed by microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) allows genome-wide discovery of protein-DNA interactions such as transcription factor bindings and histone modifications. Previous reports only compared a small number of profiles, and little has been done to compare histone modification profiles generated by the two technologies or to assess the impact of input DNA libraries in ChIP-seq analysis. Here, we performed a systematic analysis of a modENCODE dataset consisting of 31 pairs of ChIP-chip/ChIP-seq profiles of the coactivator CBP, RNA polymerase II (RNA PolII), and six histone modifications across four developmental stages of <it>Drosophila melanogaster</it>.</p> <p>Results</p> <p>Both technologies produce highly reproducible profiles within each platform, ChIP-seq generally produces profiles with a better signal-to-noise ratio, and allows detection of more peaks and narrower peaks. The set of peaks identified by the two technologies can be significantly different, but the extent to which they differ varies depending on the factor and the analysis algorithm. Importantly, we found that there is a significant variation among multiple sequencing profiles of input DNA libraries and that this variation most likely arises from both differences in experimental condition and sequencing depth. We further show that using an inappropriate input DNA profile can impact the average signal profiles around genomic features and peak calling results, highlighting the importance of having high quality input DNA data for normalization in ChIP-seq analysis.</p> <p>Conclusions</p> <p>Our findings highlight the biases present in each of the platforms, show the variability that can arise from both technology and analysis methods, and emphasize the importance of obtaining high quality and deeply sequenced input DNA libraries for ChIP-seq analysis.</p

Understanding the Sequence-Dependence of DNA Groove Dimensions: Implications for DNA Interactions

BACKGROUND: The B-DNA major and minor groove dimensions are crucial for DNA-protein interactions. It has long been thought that the groove dimensions depend on the DNA sequence, however this relationship has remained elusive. Here, our aim is to elucidate how the DNA sequence intrinsically shapes the grooves. METHODOLOGY/PRINCIPAL FINDINGS: The present study is based on the analysis of datasets of free and protein-bound DNA crystal structures, and from a compilation of NMR (31)P chemical shifts measured on free DNA in solution on a broad range of representative sequences. The (31)P chemical shifts can be interpreted in terms of the BI↔BII backbone conformations and dynamics. The grooves width and depth of free and protein-bound DNA are found to be clearly related to the BI/BII backbone conformational states. The DNA propensity to undergo BI↔BII backbone transitions is highly sequence-dependent and can be quantified at the dinucleotide level. This dual relationship, between DNA sequence and backbone behavior on one hand, and backbone behavior and groove dimensions on the other hand, allows to decipher the link between DNA sequence and groove dimensions. It also firmly establishes that proteins take advantage of the intrinsic DNA groove properties. CONCLUSIONS/SIGNIFICANCE: The study provides a general framework explaining how the DNA sequence shapes the groove dimensions in free and protein-bound DNA, with far-reaching implications for DNA-protein indirect readout in both specific and non specific interactions

Tight associations between transcription promoter type and epigenetic variation in histone positioning and modification

Abstract Background Transcription promoters are fundamental genomic cis-elements controlling gene expression. They can be classified into two types by the degree of imprecision of their transcription start sites: peak promoters, which initiate transcription from a narrow genomic region; and broad promoters, which initiate transcription from a wide-ranging region. Eukaryotic transcription initiation is suggested to be associated with the genomic positions and modifications of nucleosomes. For instance, it has been recently shown that histone with H3K9 acetylation (H3K9ac) is more likely to be distributed around broad promoters rather than peak promoters; it can thus be inferred that there is an association between histone H3K9 and promoter architecture. Results Here, we performed a systematic analysis of transcription promoters and gene expression, as well as of epigenetic histone behaviors, including genomic position, stability within the chromatin, and several modifications. We found that, in humans, broad promoters, but not peak promoters, generally had significant associations with nucleosome positioning and modification. Specifically, around broad promoters histones were highly distributed and aligned in an orderly fashion. This feature was more evident with histones that were methylated or acetylated; moreover, the nucleosome positions around the broad promoters were more stable than those around the peak ones. More strikingly, the overall expression levels of genes associated with broad promoters (but not peak promoters) with modified histones were significantly higher than the levels of genes associated with broad promoters with unmodified histones. Conclusion These results shed light on how epigenetic regulatory networks of histone modifications are associated with promoter architecture

Transcription Initiation Patterns Indicate Divergent Strategies for Gene Regulation at the Chromatin Level

The application of deep sequencing to map 5′ capped transcripts has confirmed the existence of at least two distinct promoter classes in metazoans: “focused” promoters with transcription start sites (TSSs) that occur in a narrowly defined genomic span and “dispersed” promoters with TSSs that are spread over a larger window. Previous studies have explored the presence of genomic features, such as CpG islands and sequence motifs, in these promoter classes, but virtually no studies have directly investigated the relationship with chromatin features. Here, we show that promoter classes are significantly differentiated by nucleosome organization and chromatin structure. Dispersed promoters display higher associations with well-positioned nucleosomes downstream of the TSS and a more clearly defined nucleosome free region upstream, while focused promoters have a less organized nucleosome structure, yet higher presence of RNA polymerase II. These differences extend to histone variants (H2A.Z) and marks (H3K4 methylation), as well as insulator binding (such as CTCF), independent of the expression levels of affected genes. Notably, differences are conserved across mammals and flies, and they provide for a clearer separation of promoter architectures than the presence and absence of CpG islands or the occurrence of stalled RNA polymerase. Computational models support the stronger contribution of chromatin features to the definition of dispersed promoters compared to focused start sites. Our results show that promoter classes defined from 5′ capped transcripts not only reflect differences in the initiation process at the core promoter but also are indicative of divergent transcriptional programs established within gene-proximal nucleosome organization