The Genomic HyperBrowser: inferential genomics at the sequence level

Efficient Analysis of Annotation Colocalization Accounting for Genomic Contexts

An annotation is a set of genomic intervals sharing a particular function or property. Examples include genes or their exons, evolutionarily conserved elements, and regions with a particular epigenetic state. A common task is to compare two annotations to determine if one is enriched or depleted in the regions covered by the other. We study the problem of assigning statistical significance to such a comparison based on a null model representing two random unrelated annotations. To incorporate more background information into such analyses,we propose a new null model based on a Markov chain which differentiates among several genomic contexts. These contexts can capture various confounding factors, such as GC content or assembly gaps. We then develop a new algorithm for estimating p-values by computing the exact expectation and variance of the test statistics and then estimating the p-value using a normal approximation. Compared to the previous algorithm by Gafurov et al., the new algorithm provides three advances: (1) the running time is improved from quadratic to linear or quasi-linear, (2) the algorithm can handle two different test statistics, and (3) the algorithm can handle both simple and context-dependent Markov chain null models. We demonstrate the efficiency and accuracy of our algorithm on synthetic and real data sets, including the recent human telomere-to-telomere assembly. In particular, our algorithm computed p-values for 450 pairs of human genome annotations using 24 threads in under three hours. Moreover, the use of genomic contexts to correct for GC bias resulted in the reversal of some previously published findings.

RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity

Colocalization analysis of genomic region sets has been widely adopted to unveil potential functional interactions between corresponding biological attributes, which often serves as the basis for further investigation. A number of methods have been developed for colocalization analysis of genomic elements. However, none of them explicitly considered the transcriptome heterogeneity and isoform ambiguity, making them less appropriate for analyzing transcriptome elements. Here, we developed RgnTX, an R/Bioconductor tool for the colocalization analysis of transcriptome elements with permutation tests. Different from existing approaches, RgnTX directly takes advantage of transcriptome annotation, and offers high flexibility in the null model to simulate realistic transcriptome-wide background, such as the complex alternative splicing patterns. Importantly, it supports the testing of transcriptome elements without clear isoform association, which is often the real scenario due to technical limitations. Proposed package offers a wide selection of pre-defined functions, easy to be utilized by users for visualizing permutation results, calculating shifted z-scores and conducting multiple hypothesis testing under Benjamini-Hochberg correction. Moreover, with synthetic and real datasets, we show that RgnTX novel testing modes return distinct and more significant results compared to existing genome-based methods. We believe RgnTX should make a useful tool to characterize the randomness of the transcriptome, and for conducting statistical association analysis for genomic region sets within the heterogeneous transcriptome. The package now has been accepted by Bioconductor and is freely available at: https://bioconductor.org/packages/RgnTX.

CTCF and R-loops are boundaries of cohesin-mediated DNA looping

Cohesin and CCCTC-binding factor (CTCF) are key regulatory proteins of three-dimensional (3D) genome organization. Cohesin extrudes DNA loops that are anchored by CTCF in a polar orientation. Here, we present direct evidence that CTCF binding polarity controls cohesin-mediated DNA looping. Using single-molecule imaging, we demonstrate that a critical N-terminal motif of CTCF blocks cohesin translocation and DNA looping. The cryo-EM structure of the cohesin-CTCF complex reveals that this CTCF motif ahead of zinc fingers can only reach its binding site on the STAG1 cohesin subunit when the N terminus of CTCF faces cohesin. Remarkably, a C-terminally oriented CTCF accelerates DNA compaction by cohesin. DNA-bound Cas9 and Cas12a ribonucleoproteins are also polar cohesin barriers, indicating that stalling may be intrinsic to cohesin itself. Finally, we show that RNA-DNA hybrids (R-loops) block cohesin-mediated DNA compaction in vitro and are enriched with cohesin subunits in vivo, likely forming TAD boundaries.

Cogito: automated and generic comparison of annotated genomic intervals

Background

Genetic and epigenetic biological studies often combine different types of experiments and multiple conditions. While the corresponding raw and processed data are made available through specialized public databases, the processed files are usually limited to a specific research question. Hence, they are unsuitable for an unbiased, systematic overview of a complex dataset. However, possible combinations of different sample types and conditions grow exponentially with the amount of sample types and conditions. Therefore the risk to miss a correlation or to overrate an identified correlation should be mitigated in a complex dataset. Since reanalysis of a full study is rarely a viable option, new methods are needed to address these issues systematically, reliably, reproducibly and efficiently.

Results

Cogito “COmpare annotated Genomic Intervals TOol” provides a workflow for an unbiased, structured overview and systematic analysis of complex genomic datasets consisting of different data types (e.g. RNA-seq, ChIP-seq) and conditions. Cogito is able to visualize valuable key information of genomic or epigenomic interval-based data, thereby providing a straightforward analysis approach for comparing different conditions. It supports getting an unbiased impression of a dataset and developing an appropriate analysis strategy for it. In addition to a text-based report, Cogito offers a fully customizable report as a starting point for further in-depth investigation.

Conclusions

Cogito implements a novel approach to facilitate high-level overview analyses of complex datasets, and offers additional insights into the data without the need for a full, time-consuming reanalysis. The R/Bioconductor package is freely available at https://bioconductor.org/packages/release/bioc/html/Cogito.html, a comprehensive documentation with detailed descriptions and reproducible examples is included.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04853-1.

OLOGRAM-MODL: mining enriched n-wise combinations of genomic features with Monte Carlo and dictionary learning

Most epigenetic marks, such as Transcriptional Regulators or histone marks, are biological objects known to work together in n-wise complexes. A suitable way to infer such functional associations between them is to study the overlaps of the corresponding genomic regions. However, the problem of the statistical significance of n-wise overlaps of genomic features is seldom tackled, which prevent rigorous studies of n-wise interactions. We introduce OLOGRAM-MODL, which considers overlaps between n ≥ 2 sets of genomic regions, and computes their statistical mutual enrichment by Monte Carlo fitting of a Negative Binomial distribution, resulting in more resolutive P-values. An optional machine learning method is proposed to find complexes of interest, using a new itemset mining algorithm based on dictionary learning which is resistant to noise inherent to biological assays. The overall approach is implemented through an easy-to-use CLI interface for workflow integration, and a visual tree-based representation of the results suited for explicability. The viability of the method is experimentally studied using both artificial and biological data. This approach is accessible through the command line interface of the pygtftk toolkit, available on Bioconda and from https://github.com/dputhier/pygtftk

Recommendations for the FAIRification of genomic track metadata

Background: Many types of data from genomic analyses can be represented as genomic tracks, i.e. features linked to the genomic coordinates of a reference genome. Examples of such data are epigenetic DNA methylation data, ChIP-seq peaks, germline or somatic DNA variants, as well as RNA-seq expression levels. Researchers often face difficulties in locating, accessing and combining relevant tracks from external sources, as well as locating the raw data, reducing the value of the generated information.

Description of work: We propose to advance the application of FAIR data principles (Findable, Accessible, Interoperable, and Reusable) to produce searchable metadata for genomic tracks. Findability and Accessibility of metadata can then be ensured by a track search service that integrates globally identifiable metadata from various track hubs in the Track Hub Registry and other relevant repositories. Interoperability and Reusability need to be ensured by the specification and implementation of a basic set of recommendations for metadata. We have tested this concept by developing such a specification in a JSON Schema, called FAIRtracks, and have integrated it into a novel track search service, called TrackFind. We demonstrate practical usage by importing datasets through TrackFind into existing examples of relevant analytical tools for genomic tracks: EPICO and the GSuite HyperBrowser.

Conclusion: We here provide a first iteration of a draft standard for genomic track metadata, as well as the accompanying software ecosystem. It can easily be adapted or extended to future needs of the research community regarding data, methods and tools, balancing the requirements of both data submitters and analytical end-users.

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

LXRα Regulates ChREBPα Transactivity in a Target Gene-Specific Manner through an Agonist-Modulated LBD-LID Interaction

The cholesterol-sensing nuclear receptor liver X receptor (LXR) and the glucose-sensing transcription factor carbohydrate responsive element-binding protein (ChREBP) are central players in regulating glucose and lipid metabolism in the liver. More knowledge of their mechanistic interplay is needed to understand their role in pathological conditions like fatty liver disease and insulin resistance. In the current study, LXR and ChREBP co-occupancy was examined by analyzing ChIP-seq datasets from mice livers. LXR and ChREBP interaction was determined by Co-immunoprecipitation (CoIP) and their transactivity was assessed by real-time quantitative polymerase chain reaction (qPCR) of target genes and gene reporter assays. Chromatin binding capacity was determined by ChIP-qPCR assays. Our data show that LXRα and ChREBPα interact physically and show a high co-occupancy at regulatory regions in the mouse genome. LXRα co-activates ChREBPα and regulates ChREBP-specific target genes in vitro and in vivo. This co-activation is dependent on functional recognition elements for ChREBP but not for LXR, indicating that ChREBPα recruits LXRα to chromatin in trans. The two factors interact via their key activation domains; the low glucose inhibitory domain (LID) of ChREBPα and the ligand-binding domain (LBD) of LXRα. While unliganded LXRα co-activates ChREBPα, ligand-bound LXRα surprisingly represses ChREBPα activity on ChREBP-specific target genes. Mechanistically, this is due to a destabilized LXRα:ChREBPα interaction, leading to reduced ChREBP-binding to chromatin and restricted activation of glycolytic and lipogenic target genes. This ligand-driven molecular switch highlights an unappreciated role of LXRα in responding to nutritional cues that was overlooked due to LXR lipogenesis-promoting function.

Proactive visual and statistical analysis of genomic data in Epiviz

MOTIVATION

Integrative analysis of genomic data that includes statistical methods in combination with visual exploration has gained widespread adoption. Many existing methods involve a combination of tools and resources: user interfaces that provide visualization of large genomic datasets, and computational environments that focus on data analyses over various subsets of a given dataset. Over the last few years, we have developed Epiviz as an integrative and interactive genomic data analysis tool that incorporates visualization tightly with state-of-the-art statistical analysis framework.

RESULTS

In this article, we present Epiviz Feed, a proactive and automatic visual analytics system integrated with Epiviz that alleviates the burden of manually executing data analysis required to test biologically meaningful hypotheses. Results of interest that are proactively identified by server-side computations are listed as notifications in a feed. The feed turns genomic data analysis into a collaborative work between the analyst and the computational environment, which shortens the analysis time and allows the analyst to explore results efficiently.

We discuss three ways where the proposed system advances the field of genomic data analysis: (i) takes the first step of proactive data analysis by utilizing available CPU power from the server to automate the analysis process; (ii) summarizes hypothesis test results in a way that analysts can easily understand and investigate; (iii) enables filtering and grouping of analysis results for quick search. This effort provides initial work on systems that substantially expand how computational and visualization frameworks can be tightly integrated to facilitate interactive genomic data analysis.

AVAILABILITY AND IMPLEMENTATION

The source code for Epiviz Feed application is available at http://github.com/epiviz/epiviz_feed_polymer. The Epiviz Computational Server is available at http://github.com/epiviz/epiviz-feed-computation. Please refer to Epiviz documentation site for details: http://epiviz.github.io/.

Cortical Neurogenesis Requires Bcl6-Mediated Transcriptional Repression of Multiple Self-Renewal-Promoting Extrinsic Pathways

During neurogenesis, progenitors switch from self-renewal to differentiation through the interplay of intrinsic and extrinsic cues, but how these are integrated remains poorly understood. Here, we combine whole-genome transcriptional and epigenetic analyses with in vivo functional studies to demonstrate that Bcl6, a transcriptional repressor previously reported to promote cortical neurogenesis, acts as a driver of the neurogenic transition through direct silencing of a selective repertoire of genes belonging to multiple extrinsic pathways promoting self-renewal, most strikingly the Wnt pathway. At the molecular level, Bcl6 represses its targets through Sirt1 recruitment followed by histone deacetylation. Our data identify a molecular logic by which a single cell-intrinsic factor represses multiple extrinsic pathways that favor self-renewal, thereby ensuring robustness of neuronal fate transition.

Colocalization analyses of genomic elements: approaches, recommendations and challenges

MOTIVATION

Many high-throughput methods produce sets of genomic regions as one of their main outputs. Scientists often use genomic colocalization analysis to interpret such region sets, for example to identify interesting enrichments and to understand the interplay between the underlying biological processes. Although widely used, there is little standardization in how these analyses are performed. Different practices can substantially affect the conclusions of colocalization analyses.

RESULTS

Here, we describe the different approaches and provide recommendations for performing genomic colocalization analysis, while also discussing common methodological challenges that may influence the conclusions. As illustrated by concrete example cases, careful attention to analysis details is needed in order to meet these challenges and to obtain a robust and biologically meaningful interpretation of genomic region set data.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mind the gaps: overlooking inaccessible regions confounds statistical testing in genome analysis

Background

The current versions of reference genome assemblies still contain gaps represented by stretches of Ns. Since high throughput sequencing reads cannot be mapped to those gap regions, the regions are depleted of experimental data. Moreover, several technology platforms assay a targeted portion of the genomic sequence, meaning that regions from the unassayed portion of the genomic sequence cannot be detected in those experiments. We here refer to all such regions as inaccessible regions, and hypothesize that ignoring these regions in the null model may increase false findings in statistical testing of colocalization of genomic features.

Results

Our explorative analyses confirm that the genomic regions in public genomic tracks intersect very little with assembly gaps of human reference genomes (hg19 and hg38). The little intersection was observed only at the beginning and end portions of the gap regions. Further, we simulated a set of synthetic tracks by matching the properties of real genomic tracks in a way that nullified any true association between them. This allowed us to test our hypothesis that not avoiding inaccessible regions (as represented by assembly gaps) in the null model would result in spurious inflation of statistical significance. We contrasted the distributions of test statistics and p-values of Monte Carlo-based permutation tests that either avoided or did not avoid assembly gaps in the null model when testing colocalization between a pair of tracks. We observed that the statistical tests that did not account for assembly gaps in the null model resulted in a distribution of the test statistic that is shifted to the right and a distribution of p-values that is shifted to the left (indicating inflated significance). We observed a similar level of inflated significance in hg19 and hg38, despite assembly gaps covering a smaller proportion of the latter reference genome.

Conclusion

We provide empirical evidence demonstrating that inaccessible regions, even when covering only a few percentages of the genome, can lead to a substantial amount of false findings if not accounted for in statistical colocalization analysis.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2438-1) contains supplementary material, which is available to authorized users.

Transcription-associated histone pruning demarcates macroH2A chromatin domains

The histone variant macroH2A occupies large repressive domains throughout the genome, however mechanisms underlying its precise deposition remain poorly understood. Here, we characterized de novo chromatin deposition of macroH2A2 using temporal genomic profiling in murine-derived fibroblasts devoid of all macroH2A isoforms. We find that macroH2A2 is first pervasively deposited genome-wide at both steady state domains and adjacent transcribed regions, the latter of which are subsequently pruned, establishing mature macroH2A2 domains. Pruning of macroH2A2 can be counteracted by chemical inhibition of transcription. Further, CRISPR/Cas9-based locus-specific transcriptional manipulation reveals that gene activation depletes pre-existing macroH2A2, while silencing triggers ectopic macroH2A2 accumulation. We demonstrate that the FACT (facilitates chromatin transcription) complex is required for macroH2A2 pruning within transcribed chromatin. Taken together, we have identified active chromatin as a boundary for macroH2A domains through a transcription-associated ‘pruning’ mechanism that establishes and maintains the faithful genomic localization of macroH2A variants.

Cellular senescence induces replication stress with almost no affect on DNA replication timing

Organismal aging entails a gradual decline of normal physiological functions and a major contributor to this decline is withdrawal of the cell cycle, known as senescence. Senescence can result from telomere diminution leading to a finite number of population doublings, known as replicative senescence (RS), or from oncogene overexpression, as a protective mechanism against cancer. Senescence is associated with large-scale chromatin re-organization and changes in gene expression. Replication stress is a complex phenomenon, defined as the slowing or stalling of replication fork progression and/or DNA synthesis, which has serious implications for genome stability, and consequently in human diseases. Aberrant replication fork structures activate the replication stress response leading to the activation of dormant origins, which is thought to be a safeguard mechanism to complete DNA replication on time. However, the relationship between replicative stress and the changes in the spatiotemporal program of DNA replication in senescence progression remains unclear. Here, we studied the DNA replication program during senescence progression in proliferative and pre-senescent cells from donors of various ages by single DNA fiber combing of replicated DNA, origin mapping by sequencing short nascent strands and genome-wide profiling of replication timing (TRT). We demonstrate that, progression into RS leads to reduced replication fork rates and activation of dormant origins, which are the hallmarks of replication stress. However, with the exception of a delay in RT of the CREB5 gene in all pre-senescent cells, RT was globally unaffected by replication stress during entry into either oncogene-induced or RS. Consequently, we conclude that RT alterations associated with physiological and accelerated aging, do not result from senescence progression. Our results clarify the interplay between senescence, aging and replication programs and demonstrate that RT is largely resistant to replication stress.

Epigenetic dysregulation of naive CD4+ T-cell activation genes in childhood food allergy

Food allergy poses a significant clinical and public health burden affecting 2–10% of infants. Using integrated DNA methylation and transcriptomic profiling, we found that polyclonal activation of naive CD4+ T cells through the T cell receptor results in poorer lymphoproliferative responses in children with immunoglobulin E (IgE)-mediated food allergy. Reduced expression of cell cycle-related targets of the E2F and MYC transcription factor networks, and remodeling of DNA methylation at metabolic (RPTOR, PIK3D, MAPK1, FOXO1) and inflammatory genes (IL1R, IL18RAP, CD82) underpins this suboptimal response. Infants who fail to resolve food allergy in later childhood exhibit cumulative increases in epigenetic disruption at T cell activation genes and poorer lymphoproliferative responses compared to children who resolved food allergy. Our data indicate epigenetic dysregulation in the early stages of signal transduction through the T cell receptor complex, and likely reflects pathways modified by gene–environment interactions in food allergy.

Immunoglobulin E (IgE)-mediated food allergy is a major issue that affects 2–10% of infants. Here the authors study the epigenetic regulation of the naive CD4+ T cell activation response among children with IgE-mediated food allergy finding epigenetic dysregulation in the early stages of signal transduction through the T cell receptor complex.

StereoGene: rapid estimation of genome-wide correlation of continuous or interval feature data

Motivation

Genomics features with similar genome-wide distributions are generally hypothesized to be functionally related, for example, colocalization of histones and transcription start sites indicate chromatin regulation of transcription factor activity. Therefore, statistical algorithms to perform spatial, genome-wide correlation among genomic features are required.

Results

Here, we propose a method, StereoGene, that rapidly estimates genome-wide correlation among pairs of genomic features. These features may represent high-throughput data mapped to reference genome or sets of genomic annotations in that reference genome. StereoGene enables correlation of continuous data directly, avoiding the data binarization and subsequent data loss. Correlations are computed among neighboring genomic positions using kernel correlation. Representing the correlation as a function of the genome position, StereoGene outputs the local correlation track as part of the analysis. StereoGene also accounts for confounders such as input DNA by partial correlation. We apply our method to numerous comparisons of ChIP-Seq datasets from the Human Epigenome Atlas and FANTOM CAGE to demonstrate its wide applicability. We observe the changes in the correlation between epigenomic features across developmental trajectories of several tissue types consistent with known biology and find a novel spatial correlation of CAGE clusters with donor splice sites and with poly(A) sites. These analyses provide examples for the broad applicability of StereoGene for regulatory genomics.

Availability and implementation

The StereoGene C ++ source code, program documentation, Galaxy integration scripts and examples are available from the project homepage http://stereogene.bioinf.fbb.msu.ru/.

Contact

favorov@sensi.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Epigenomic annotation-based interpretation of genomic data: from enrichment analysis to machine learning

Motivation

One of the goals of functional genomics is to understand the regulatory implications of experimentally obtained genomic regions of interest (ROIs). Most sequencing technologies now generate ROIs distributed across the whole genome. The interpretation of these genome-wide ROIs represents a challenge as the majority of them lie outside of functionally well-defined protein coding regions. Recent efforts by the members of the International Human Epigenome Consortium have generated volumes of functional/regulatory data (reference epigenomic datasets), effectively annotating the genome with epigenomic properties. Consequently, a wide variety of computational tools has been developed utilizing these epigenomic datasets for the interpretation of genomic data.

Results

The purpose of this review is to provide a structured overview of practical solutions for the interpretation of ROIs with the help of epigenomic data. Starting with epigenomic enrichment analysis, we discuss leading tools and machine learning methods utilizing epigenomic and 3D genome structure data. The hierarchy of tools and methods reviewed here presents a practical guide for the interpretation of genome-wide ROIs within an epigenomic context.

Contact

mikhail.dozmorov@vcuhealth.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Coloc-stats: a unified web interface to perform colocalization analysis of genomic features

Functional genomics assays produce sets of genomic regions as one of their main outputs. To biologically interpret such region-sets, researchers often use colocalization analysis, where the statistical significance of colocalization (overlap, spatial proximity) between two or more region-sets is tested. Existing colocalization analysis tools vary in the statistical methodology and analysis approaches, thus potentially providing different conclusions for the same research question. As the findings of colocalization analysis are often the basis for follow-up experiments, it is helpful to use several tools in parallel and to compare the results. We developed the Coloc-stats web service to facilitate such analyses. Coloc-stats provides a unified interface to perform colocalization analysis across various analytical methods and method-specific options (e.g. colocalization measures, resolution, null models). Coloc-stats helps the user to find a method that supports their experimental requirements and allows for a straightforward comparison across methods. Coloc-stats is implemented as a web server with a graphical user interface that assists users with configuring their colocalization analyses. Coloc-stats is freely available at https://hyperbrowser.uio.no/coloc-stats/.

Norwegian e-Infrastructure for Life Sciences (NeLS)

The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may also access it through Secure Shell (SSH) and application programming interfaces (APIs).  NeLS has been in production since 2015, with training and support provided by the help desk of ELIXIR Norway. Through collaboration with NorSeq, the national consortium for high-throughput sequencing, an integrated service is offered so that sequencing data generated in a research project is provided to the involved researchers through NeLS. Sensitive data, such as individual genomic sequencing data, are handled using the TSD (Services for Sensitive Data) platform provided by Sigma2 and the University of Oslo. NeLS integrates national e-infrastructure storage and computing resources, and is also integrated with the SEEK platform in order to store large data files produced by experiments described in SEEK.   In this article, we outline the architecture of NeLS and discuss possible directions for further development.

Allele-specific control of replication timing and genome organization during development

DNA replication occurs in a defined temporal order known as the replication-timing (RT) program. RT is regulated during development in discrete chromosomal units, coordinated with transcriptional activity and 3D genome organization. Here, we derived distinct cell types from F1 hybrid musculus × castaneus mouse crosses and exploited the high single-nucleotide polymorphism (SNP) density to characterize allelic differences in RT (Repli-seq), genome organization (Hi-C and promoter-capture Hi-C), gene expression (total nuclear RNA-seq), and chromatin accessibility (ATAC-seq). We also present HARP, a new computational tool for sorting SNPs in phased genomes to efficiently measure allele-specific genome-wide data. Analysis of six different hybrid mESC clones with different genomes (C57BL/6, 129/sv, and CAST/Ei), parental configurations, and gender revealed significant RT asynchrony between alleles across ∼12% of the autosomal genome linked to subspecies genomes but not to parental origin, growth conditions, or gender. RT asynchrony in mESCs strongly correlated with changes in Hi-C compartments between alleles but not as strongly with SNP density, gene expression, imprinting, or chromatin accessibility. We then tracked mESC RT asynchronous regions during development by analyzing differentiated cell types, including extraembryonic endoderm stem (XEN) cells, four male and female primary mouse embryonic fibroblasts (MEFs), and neural precursor cells (NPCs) differentiated in vitro from mESCs with opposite parental configurations. We found that RT asynchrony and allelic discordance in Hi-C compartments seen in mESCs were largely lost in all differentiated cell types, accompanied by novel sites of allelic asynchrony at a considerably smaller proportion of the genome, suggesting that genome organization of homologs converges to similar folding patterns during cell fate commitment.

Genome build information is an essential part of genomic track files

Genomic locations are represented as coordinates on a specific genome build version, but the build information is frequently missing when coordinates are provided. We show that this information is essential to correctly interpret and analyse the genomic intervals contained in genomic track files. Although not a substitute for best practices, we also provide a tool to predict the genome build version of genomic track files.

GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome

Background

Recent large-scale undertakings such as ENCODE and Roadmap Epigenomics have generated experimental data mapped to the human reference genome (as genomic tracks) representing a variety of functional elements across a large number of cell types. Despite the high potential value of these publicly available data for a broad variety of investigations, little attention has been given to the analytical methodology necessary for their widespread utilisation.

Findings

We here present a first principled treatment of the analysis of collections of genomic tracks. We have developed novel computational and statistical methodology to permit comparative and confirmatory analyses across multiple and disparate data sources. We delineate a set of generic questions that are useful across a broad range of investigations and discuss the implications of choosing different statistical measures and null models. Examples include contrasting analyses across different tissues or diseases. The methodology has been implemented in a comprehensive open-source software system, the GSuite HyperBrowser. To make the functionality accessible to biologists, and to facilitate reproducible analysis, we have also developed a web-based interface providing an expertly guided and customizable way of utilizing the methodology. With this system, many novel biological questions can flexibly be posed and rapidly answered.

Conclusions

Through a combination of streamlined data acquisition, interoperable representation of dataset collections, and customizable statistical analysis with guided setup and interpretation, the GSuite HyperBrowser represents a first comprehensive solution for integrative analysis of track collections across the genome and epigenome. The software is available at: https://hyperbrowser.uio.no.

Coordinates and intervals in graph-based reference genomes

BACKGROUND

It has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes.

RESULTS

We formalize offset-based coordinate systems on graph-based reference genomes and introduce methods for representing intervals on these reference structures. We show the advantage of our methods by representing genes on a graph-based representation of the newest assembly of the human genome (GRCh38) and its alternative loci for regions that are highly variable.

CONCLUSION

More complex reference genomes, containing alternative loci, require methods to represent genomic data on these structures. Our proposed notation for genomic intervals makes it possible to fully utilize the alternative loci of the GRCh38 assembly and potential future graph-based reference genomes. We have made a Python package for representing such intervals on offset-based coordinate systems, available at https://github.com/uio-cels/offsetbasedgraph . An interactive web-tool using this Python package to visualize genes on a graph created from GRCh38 is available at https://github.com/uio-cels/genomicgraphcoords .

Male-Specific Transcription Factor Occupancy Alone Does Not Account for Differential Methylation at Imprinted Genes in the mouse Germ Cell Lineage

Genomic imprinting is an epigenetic mechanism that affects a subset of mammalian genes, resulting in monoallelic expression depending on the parental origin of the alleles. Imprinted regions contain regulatory elements that are methylated in the gametes in a sex-specific manner (differentially methylated regions; DMRs). DMRs are present at nonimprinted loci as well, but whereas most regions are equalized after fertilization, methylation at imprinted regions maintains asymmetry. We tested the hypothesis that paternally unmethylated DMRs are occupied by transcription factors (TFs) present during male gametogenesis. Meta-analysis of mouse RNA data to identify DNA-binding proteins expressed in male gametes and motif enrichment analysis of active promoters yielded a list of candidate TFs. We then asked whether imprinted or nonimprinted paternally unmethylated DMRs harbored motifs for these TFs, and found many shared motifs between the two groups. However, DMRs that are methylated in the male germ cells also share motifs with DMRs that remain unmethylated. There are recognition sequences exclusive to the unmethylated DMRs, whether imprinted or not, that correspond with cell-cycle regulators, such as p53. Thus, at least with the current available data, our results indicate a complex scenario in which TF occupancy alone is not likely to play a role in protecting unmethylated DMRs, at least during male gametogenesis. Rather, the epigenetic features of DMRs, regulatory sequences other than DMRs, and the role of DNA-binding proteins capable of endowing sequence specificity to DNA-methylating enzymes are feasible mechanisms and further investigation is needed to answer this question.

Bioinformatics Analysis of Estrogen-Responsive Genes

Estrogen is a steroid hormone that plays critical roles in a myriad of intracellular pathways. The expression of many genes is regulated through the steroid hormone receptors ESR1 and ESR2. These bind to DNA and modulate the expression of target genes. Identification of estrogen target genes is greatly facilitated by the use of transcriptomic methods, such as RNA-seq and expression microarrays, and chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq). Combining transcriptomic and ChIP-seq data enables a distinction to be drawn between direct and indirect estrogen target genes. This chapter discusses some methods of identifying estrogen target genes that do not require any expertise in programming languages or complex bioinformatics.

Automated amplicon design suitable for analysis of DNA variants by melting techniques

Background

The technological development of DNA analysis has had tremendous development in recent years, and the present deep sequencing techniques present unprecedented opportunities for detailed and high-throughput DNA variant detection. Although DNA sequencing has had an exponential decrease in cost per base pair analyzed, focused and target-specific methods are however still much in use for analysis of DNA variants. With increasing capacity in the analytical procedures, an equal demand in automated amplicon and primer design has emerged.

Results

We have constructed a web-based tool that is able to batch design DNA variant assay suitable for analysis by denaturing gel/capillary electrophoresis and high resolution melting. The tool is developed as a computational workflow that implements one of the most widely used primer design tools, followed by validation of primer specificity, as well as calculation and visualization of the melting properties of the resulting amplicon, with or without an artificial high melting domain attached. The tool will be useful for scientists applying DNA melting techniques in analysis of DNA variations. The tool is freely available at http://meltprimer.ous-research.no/.

Conclusion

Herein, we demonstrate a novel tool with respect to covering the whole amplicon design workflow necessary for groups that use melting equilibrium techniques to separate DNA variants.

Generation of a synthetic GlcNAcylated nucleosome reveals regulation of stability by H2A-Thr101 GlcNAcylation

O-GlcNAcylation is a newly discovered histone modification implicated in transcriptional regulation, but no structural information on the physical effect of GlcNAcylation on chromatin exists. Here, we generate synthetic, pure GlcNAcylated histones and nucleosomes and reveal that GlcNAcylation can modulate structure through direct destabilization of H2A/H2B dimers in the nucleosome, thus promoting an 'open' chromatin state. The results suggest that a plausible molecular basis for one role of histone O-GlcNAcylation in epigenetic regulation is to lower the barrier for RNA polymerase passage and hence increase transcription.

VisRseq: R-based visual framework for analysis of sequencing data

BACKGROUND

Several tools have been developed to enable biologists to perform initial browsing and exploration of sequencing data. However the computational tool set for further analyses often requires significant computational expertise to use and many of the biologists with the knowledge needed to interpret these data must rely on programming experts.

RESULTS

We present VisRseq, a framework for analysis of sequencing datasets that provides a computationally rich and accessible framework for integrative and interactive analyses without requiring programming expertise. We achieve this aim by providing R apps, which offer a semi-auto generated and unified graphical user interface for computational packages in R and repositories such as Bioconductor. To address the interactivity limitation inherent in R libraries, our framework includes several native apps that provide exploration and brushing operations as well as an integrated genome browser. The apps can be chained together to create more powerful analysis workflows.

CONCLUSIONS

To validate the usability of VisRseq for analysis of sequencing data, we present two case studies performed by our collaborators and report their workflow and insights.

An integrative analysis of post-translational histone modifications in the marine diatom Phaeodactylum tricornutum

BACKGROUND

Nucleosomes are the building blocks of chromatin where gene regulation takes place. Chromatin landscapes have been profiled for several species, providing insights into the fundamental mechanisms of chromatin-mediated transcriptional regulation of gene expression. However, knowledge is missing for several major and deep-branching eukaryotic groups, such as the Stramenopiles, which include the diatoms. Diatoms are highly diverse and ubiquitous species of phytoplankton that play a key role in global biogeochemical cycles. Dissecting chromatin-mediated regulation of genes in diatoms will help understand the ecological success of these organisms in contemporary oceans.

RESULTS

Here, we use high resolution mass spectrometry to identify a full repertoire of post-translational modifications on histones of the marine diatom Phaeodactylum tricornutum, including eight novel modifications. We map five histone marks coupled with expression data and show that P. tricornutum displays both unique and broadly conserved chromatin features, reflecting the chimeric nature of its genome. Combinatorial analysis of histone marks and DNA methylation demonstrates the presence of an epigenetic code defining activating or repressive chromatin states. We further profile three specific histone marks under conditions of nitrate depletion and show that the histone code is dynamic and targets specific sets of genes.

CONCLUSIONS

This study is the first genome-wide characterization of the histone code from a stramenopile and a marine phytoplankton. The work represents an important initial step for understanding the evolutionary history of chromatin and how epigenetic modifications affect gene expression in response to environmental cues in marine environments.

Hidden among the crowd: differential DNA methylation-expression correlations in cancer occur at important oncogenic pathways

DNA methylation is a frequent epigenetic mechanism that participates in transcriptional repression. Variations in DNA methylation with respect to gene expression are constant, and, for unknown reasons, some genes with highly methylated promoters are sometimes overexpressed. In this study we have analyzed the expression and methylation patterns of thousands of genes in five groups of cancer and normal tissue samples in order to determine local and genome-wide differences. We observed significant changes in global methylation-expression correlation in all the neoplasms, which suggests that differential correlation events are frequent in cancer. A focused analysis in the breast cancer cohort identified 1662 genes whose correlation varies significantly between normal and cancerous breast, but whose DNA methylation and gene expression patterns do not change substantially. These genes were enriched in cancer-related pathways and repressive chromatin features across various model cell lines, such as PRC2 binding and H3K27me3 marks. Substantial changes in methylation-expression correlation indicate that these genes are subject to epigenetic remodeling, where the differential activity of other factors break the expected relationship between both variables. Our findings suggest a complex regulatory landscape where a redistribution of local and large-scale chromatin repressive domains at differentially correlated genes (DCGs) creates epigenetic hotspots that modulate cancer-specific gene expression.

ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets

Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.

EBNA2 binds to genomic intervals associated with multiple sclerosis and overlaps with vitamin D receptor occupancy

Epstein-Barr virus (EBV) is a non-heritable factor that associates with multiple sclerosis (MS). However its causal relationship with the disease is still unclear. The virus establishes a complex co-existence with the host that includes regulatory influences on gene expression. Hence, if EBV contributes to the pathogenesis of MS it may do so by interacting with disease predisposing genes. To verify this hypothesis we evaluated EBV nuclear antigen 2 (EBNA2, a protein that recent works by our and other groups have implicated in disease development) binding inside MS associated genomic intervals. We found that EBNA2 binding occurs within MS susceptibility sites more than expected by chance (factor of observed vs expected overlap [O/E] = 5.392-fold, p < 2.0e-05). This remains significant after controlling for multiple genomic confounders. We then asked whether this observation is significant per se or should also be viewed in the context of other disease relevant gene-environment interactions, such as those attributable to vitamin D. We therefore verified the overlap between EBNA2 genomic occupancy and vitamin D receptor (VDR) binding sites. EBNA2 shows a striking overlap with VDR binding sites (O/E = 96.16-fold, p < 2.0e-05), even after controlling for the chromatin accessibility state of shared regions (p <0.001). Furthermore, MS susceptibility regions are preferentially targeted by both EBNA2 and VDR than by EBNA2 alone (enrichment difference = 1.722-fold, p = 0.0267). Taken together, these findings demonstrate that EBV participates in the gene-environment interactions that predispose to MS.

Transcriptionally active regions are the preferred targets for chromosomal HPV integration in cervical carcinogenesis

Integration of human papillomavirus (HPV) into the host genome is regarded as a determining event in cervical carcinogenesis. However, the exact mechanism for integration, and the role of integration in stimulating cancer progression, is not fully characterized. Although integration sites are reported to appear randomly distributed over all chromosomes, fragile sites, translocation break points and transcriptionally active regions have all been suggested as being preferred sites for integration. In addition, more recent studies have reported integration events occurring within or surrounding essential cancer-related genes, raising the question whether these may reflect key events in the molecular genesis of HPV induced carcinomas. In a search for possible common denominators of the integration sites, we utilized the chromosomal coordinates of 121 viral-cellular fusion transcripts, and examined for statistical overrepresentation of integration sites with various features of ENCODE chromatin information data, using the Genomic HyperBrowser. We find that integration sites coincide with DNA that is transcriptionally active in mucosal epithelium, as judged by the relationship of integration sites to DNase hypersensitivity and H3K4me3 methylation data. Finding an association between integration and transcription is highly informative with regard to the spatio-temporal characteristics of the integration process. These results suggest that integration is an early event in carcinogenesis, more than a late product of chromosomal instability. If the viral integrations were more likely to occur in destabilized regions of the DNA, a completely random distribution of the integration sites would be expected. As a by-product of integration in actively transcribing DNA, a tendency of integration in or close to genes is likely to be observed. This increases the possibility of viral signals to modulate the expression of these genes, potentially contributing to the progression towards cancer.

Online resources for genomic analysis using high-throughput sequencing

The availability of high-throughput sequencing has created enormous possibilities for scientific discovery. However, the massive amount of data being generated has resulted in a severe informatics bottleneck. A large number of tools exist for analyzing next-generation sequencing (NGS) data, yet often there remains a disconnect between these research tools and the ability of many researchers to use them. As a consequence, several online resources and communities have been developed to assist researchers with both the management and the analysis of sequencing data sets. Here we describe the use and applications of common file formats for coding and storing genomic data, consider several web-accessible open-source resources for the visualization and analysis of NGS data, and provide examples of typical analyses with links to further detailed exercises.

Genome-wide study of correlations between genomic features and their relationship with the regulation of gene expression

The broad class of tasks in genetics and epigenetics can be reduced to the study of various features that are distributed over the genome (genome tracks). The rapid and efficient processing of the huge amount of data stored in the genome-scale databases cannot be achieved without the software packages based on the analytical criteria. However, strong inhomogeneity of genome tracks hampers the development of relevant statistics. We developed the criteria for the assessment of genome track inhomogeneity and correlations between two genome tracks. We also developed a software package, Genome Track Analyzer, based on this theory. The theory and software were tested on simulated data and were applied to the study of correlations between CpG islands and transcription start sites in the Homo sapiens genome, between profiles of protein-binding sites in chromosomes of Drosophila melanogaster, and between DNA double-strand breaks and histone marks in the H. sapiens genome. Significant correlations between transcription start sites on the forward and the reverse strands were observed in genomes of D. melanogaster, Caenorhabditis elegans, Mus musculus, H. sapiens, and Danio rerio. The observed correlations may be related to the regulation of gene expression in eukaryotes. Genome Track Analyzer is freely available at http://ancorr.eimb.ru/.

Sequencing of the human IG light chain loci from a hydatidiform mole BAC library reveals locus-specific signatures of genetic diversity

Germline variation at immunoglobulin (IG) loci is critical for pathogen-mediated immunity, but establishing complete haplotype sequences in these regions has been problematic because of complex sequence architecture and diploid source DNA. We sequenced BAC clones from the effectively haploid human hydatidiform mole cell line, CHM1htert, across the light chain IG loci, kappa (IGK) and lambda (IGL), creating single haplotype representations of these regions. The IGL haplotype generated here is 1.25 Mb of contiguous sequence, including four novel IGLV alleles, one novel IGLC allele, and an 11.9-kb insertion. The CH17 IGK haplotype consists of two 644 kb proximal and 466 kb distal contigs separated by a large gap of unknown size; these assemblies added 49 kb of unique sequence extending into this gap. Our analysis also resulted in the characterization of seven novel IGKV alleles and a 16.7-kb region exhibiting signatures of interlocus sequence exchange between distal and proximal IGKV gene clusters. Genetic diversity in IGK/IGL was compared with that of the IG heavy chain (IGH) locus within the same haploid genome, revealing threefold (IGK) and sixfold (IGL) higher diversity in the IGH locus, potentially associated with increased levels of segmental duplication and the telomeric location of IGH.

Ring chromosomes, breakpoint clusters, and neocentromeres in sarcomas

Gene amplification is relatively common in tumors. In certain subtypes of sarcoma, it often occurs in the form of ring and/or giant rod-shaped marker (RGM) chromosomes whose mitotic stability is frequently rescued by ectopic novel centromeres (neocentromeres). Little is known about the origin and structure of these RGM chromosomes, including how they arise, their internal organization, and which sequences underlie the neocentromeres. To address these questions, 42 sarcomas with RGM chromosomes were investigated to detect regions prone to double strand breaks and possible functional or structural constraints driving the amplification process. We found nine breakpoint cluster regions potentially involved in the genesis of RGM chromosomes, which turned out to be significantly enriched in poly-pyrimidine traits. Some of the clusters were located close to genes already known to be relevant for sarcomas, thus indicating a potential functional constraint, while others mapped to transcriptionally inactive chromatin domains enriched in heterochromatic sites. Of note, five neocentromeres were identified after analyzing 13 of the cases by fluorescent in situ hybridization. ChIP-on-chip analysis with antibodies against the centromeric protein CENP-A showed that they were a patchwork of small genomic segments derived from different chromosomes, likely joint to form a contiguous sequence during the amplification process.

Hot spots of DNA double-strand breaks and genomic contacts of human rDNA units are involved in epigenetic regulation

DNA double-strand breaks (DSBs) are involved in many cellular mechanisms, including replication, transcription, and genome rearrangements. The recent observation that hot spots of DSBs in human chromosomes delimit DNA domains that possess coordinately expressed genes suggests a strong relationship between the organization of transcription patterns and hot spots of DSBs. In this study, we performed mapping of hot spots of DSBs in a human 43-kb ribosomal DNA (rDNA) repeated unit. We observed that rDNA units corresponded to the most fragile sites in human chromosomes and that these units possessed at least nine specific regions containing clusters of extremely frequently occurring DSBs, which were located exclusively in non-coding intergenic spacer (IGS) regions. The hot spots of DSBs corresponded to only a specific subset of DNase-hypersensitive sites, and coincided with CTCF, PARP1, and HNRNPA2B1 binding sites, and H3K4me3 marks. Our rDNA-4C data indicate that the regions of IGS containing the hot spots of DSBs often form contacts with specific regions in different chromosomes, including the pericentromeric regions, as well as regions that are characterized by H3K27ac and H3K4me3 marks, CTCF binding sites, ChIA-PET and RIP signals, and high levels of DSBs. The data suggest a strong link between chromosome breakage and several different mechanisms of epigenetic regulation of gene expression.

GenPlay Multi-Genome, a tool to compare and analyze multiple human genomes in a graphical interface

SUMMARY

Parallel visualization of multiple individual human genomes is a complex endeavor that is rapidly gaining importance with the increasing number of personal, phased and cancer genomes that are being generated. It requires the display of variants such as SNPs, indels and structural variants that are unique to specific genomes and the introduction of multiple overlapping gaps in the reference sequence. Here, we describe GenPlay Multi-Genome, an application specifically written to visualize and analyze multiple human genomes in parallel. GenPlay Multi-Genome is ideally suited for the comparison of allele-specific expression and functional genomic data obtained from multiple phased genomes in a graphical interface with access to multiple-track operation. It also allows the analysis of data that have been aligned to custom genomes rather than to a standard reference and can be used as a variant calling format file browser and as a tool to compare different genome assembly, such as hg19 and hg38.

AVAILABILITY AND IMPLEMENTATION

GenPlay is available under the GNU public license (GPL-3) from http://genplay.einstein.yu.edu. The source code is available at https://github.com/JulienLajugie/GenPlay.

The ETS family member GABPα modulates androgen receptor signalling and mediates an aggressive phenotype in prostate cancer

In prostate cancer (PC), the androgen receptor (AR) is a key transcription factor at all disease stages, including the advanced stage of castrate-resistant prostate cancer (CRPC). In the present study, we show that GABPα, an ETS factor that is up-regulated in PC, is an AR-interacting transcription factor. Expression of GABPα enables PC cell lines to acquire some of the molecular and cellular characteristics of CRPC tissues as well as more aggressive growth phenotypes. GABPα has a transcriptional role that dissects the overlapping cistromes of the two most common ETS gene fusions in PC: overlapping significantly with ETV1 but not with ERG target genes. GABPα bound predominantly to gene promoters, regulated the expression of one-third of AR target genes and modulated sensitivity to AR antagonists in hormone responsive and castrate resistant PC models. This study supports a critical role for GABPα in CRPC and reveals potential targets for therapeutic intervention.

Identifying pathogenic processes by integrating microarray data with prior knowledge

BACKGROUND

It is of great importance to identify molecular processes and pathways that are involved in disease etiology. Although there has been an extensive use of various high-throughput methods for this task, pathogenic pathways are still not completely understood. Often the set of genes or proteins identified as altered in genome-wide screens show a poor overlap with canonical disease pathways. These findings are difficult to interpret, yet crucial in order to improve the understanding of the molecular processes underlying the disease progression. We present a novel method for identifying groups of connected molecules from a set of differentially expressed genes. These groups represent functional modules sharing common cellular function and involve signaling and regulatory events. Specifically, our method makes use of Bayesian statistics to identify groups of co-regulated genes based on the microarray data, where external information about molecular interactions and connections are used as priors in the group assignments. Markov chain Monte Carlo sampling is used to search for the most reliable grouping.

RESULTS

Simulation results showed that the method improved the ability of identifying correct groups compared to traditional clustering, especially for small sample sizes. Applied to a microarray heart failure dataset the method found one large cluster with several genes important for the structure of the extracellular matrix and a smaller group with many genes involved in carbohydrate metabolism. The method was also applied to a microarray dataset on melanoma cancer patients with or without metastasis, where the main cluster was dominated by genes related to keratinocyte differentiation.

CONCLUSION

Our method found clusters overlapping with known pathogenic processes, but also pointed to new connections extending beyond the classical pathways.

Chromatin states reveal functional associations for globally defined transcription start sites in four human cell lines

BACKGROUND

Deciphering the most common modes by which chromatin regulates transcription, and how this is related to cellular status and processes is an important task for improving our understanding of human cellular biology. The FANTOM5 and ENCODE projects represent two independent large scale efforts to map regulatory and transcriptional features to the human genome. Here we investigate chromatin features around a comprehensive set of transcription start sites in four cell lines by integrating data from these two projects.

RESULTS

Transcription start sites can be distinguished by chromatin states defined by specific combinations of both chromatin mark enrichment and the profile shapes of these chromatin marks. The observed patterns can be associated with cellular functions and processes, and they also show association with expression level, location relative to nearby genes, and CpG content. In particular we find a substantial number of repressed inter- and intra-genic transcription start sites enriched for active chromatin marks and Pol II, and these sites are strongly associated with immediate-early response processes and cell signaling. Associations between start sites with similar chromatin patterns are validated by significant correlations in their global expression profiles.

CONCLUSIONS

The results confirm the link between chromatin state and cellular function for expressed transcripts, and also indicate that active chromatin states at repressed transcripts may poise transcripts for rapid activation during immune response.

Human-specific epigenetic variation in the immunological Leukotriene B4 Receptor (LTB4R/BLT1) implicated in common inflammatory diseases

BACKGROUND

Common human diseases are caused by the complex interplay of genetic susceptibility as well as environmental factors. Due to the environment's influence on the epigenome, and therefore genome function, as well as conversely the genome's facilitative effect on the epigenome, analysis of this level of regulation may increase our knowledge of disease pathogenesis.

METHODS

In order to identify human-specific epigenetic influences, we have performed a novel genome-wide DNA methylation analysis comparing human, chimpanzee and rhesus macaque.

RESULTS

We have identified that the immunological Leukotriene B4 receptor (LTB4R, BLT1 receptor) is the most epigenetically divergent human gene in peripheral blood in comparison with other primates. This difference is due to the co-ordinated active state of human-specific hypomethylation in the promoter and human-specific increased gene body methylation. This gene is significant in innate immunity and the LTB4/LTB4R pathway is involved in the pathogenesis of the spectrum of human inflammatory diseases. This finding was confirmed by additional neutrophil-only DNA methylome and lymphoblastoid H3K4me3 chromatin comparative data. Additionally we show through functional analysis that this receptor has increased expression and a higher response to the LTB4 ligand in human versus rhesus macaque peripheral blood mononuclear cells. Genome-wide we also find human species-specific differentially methylated regions (human s-DMRs) are more prevalent in CpG island shores than within the islands themselves, and within the latter are associated with the CTCF motif.

CONCLUSIONS

This result further emphasises the exclusive nature of the human immunological system, its divergent adaptation even from very closely related primates, and the power of comparative epigenomics to identify and understand human uniqueness.

HiBrowse: multi-purpose statistical analysis of genome-wide chromatin 3D organization

Summary: Recently developed methods that couple next-generation sequencing with chromosome conformation capture-based techniques, such as Hi-C and ChIA-PET, allow for characterization of genome-wide chromatin 3D structure. Understanding the organization of chromatin in three dimensions is a crucial next step in the unraveling of global gene regulation, and methods for analyzing such data are needed. We have developed HiBrowse, a user-friendly web-tool consisting of a range of hypothesis-based and descriptive statistics, using realistic assumptions in null-models.

Availability and implementation: HiBrowse is supported by all major browsers, and is freely available at http://hyperbrowser.uio.no/3d. Software is implemented in Python, and source code is available for download by following instructions on the main site.

Contact:jonaspau@ifi.uio.no

Supplementary Information:Supplementary data are available at Bioinformatics online.

Effects of sulforaphane and 3,3'-diindolylmethane on genome-wide promoter methylation in normal prostate epithelial cells and prostate cancer cells

Epigenetic changes, including aberrant DNA methylation, result in altered gene expression and play an important role in carcinogenesis. Phytochemicals such as sulforaphane (SFN) and 3,3'-diindolylmethane (DIM) are promising chemopreventive agents for the treatment of prostate cancer. Both have been shown to induce re-expression of genes, including tumor suppressor genes silenced in cancer cells, via modulation of epigenetic marks including DNA methylation. However, it remained unclear the effects SFN and DIM on DNA methylation at a genomic scale. The goal of this study was to determine the genome-wide effects of SFN and DIM on promoter methylation in normal prostate epithelial cells and prostate cancer cells. Both SFN and DIM treatment decreased DNA methyltransferase expression in normal prostate epithelial cells (PrEC), and androgen-dependent (LnCAP) and androgen-independent (PC3) prostate cancer cells. The effects of SFN and DIM on promoter methylation profiles in normal PrEC, LnCAP and PC3 prostate cancer cells were determined using methyl-DNA immunoprecipitation followed by genome-wide DNA methylation array. We showed widespread changes in promoter methylation patterns, including both increased and decreased methylation, in all three prostate cell lines in response to SFN or DIM treatments. In particular, SFN and DIM altered promoter methylation in distinct sets of genes in PrEC, LnCAP, and PC3 cells, but shared similar gene targets within a single cell line. We further showed that SFN and DIM reversed many of the cancer-associated methylation alterations, including aberrantly methylated genes that are dysregulated or are highly involved in cancer progression. Overall, our data suggested that both SFN and DIM are epigenetic modulators that have broad and complex effects on DNA methylation profiles in both normal and cancerous prostate epithelial cells. Results from our study may provide new insights into the epigenetic mechanisms by which SFN and DIM exert their cancer chemopreventive effects.