FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map

O-GlcNAcylation controls pro-fibrotic transcriptional regulatory signaling in myofibroblasts

Tissue injury causes activation of mesenchymal lineage cells into wound-repairing myofibroblasts (MFs), whose uncontrolled activity ultimately leads to fibrosis. Although this process is triggered by deep metabolic and transcriptional reprogramming, functional links between these two key events are not yet understood. Here, we report that the metabolic sensor post-translational modification O-linked β-D-N-acetylglucosaminylation (O-GlcNAcylation) is increased and required for myofibroblastic activation. Inhibition of protein O-GlcNAcylation impairs archetypal myofibloblast cellular activities including extracellular matrix gene expression and collagen secretion/deposition as defined in vitro and using ex vivo and in vivo murine liver injury models. Mechanistically, a multi-omics approach combining proteomic, epigenomic, and transcriptomic data mining revealed that O-GlcNAcylation controls the MF transcriptional program by targeting the transcription factors Basonuclin 2 (BNC2) and TEA domain transcription factor 4 (TEAD4) together with the Yes-associated protein 1 (YAP1) co-activator. Indeed, inhibition of protein O-GlcNAcylation impedes their stability leading to decreased functionality of the BNC2/TEAD4/YAP1 complex towards promoting activation of the MF transcriptional regulatory landscape. We found that this involves O-GlcNAcylation of BNC2 at Thr455 and Ser490 and of TEAD4 at Ser69 and Ser99. Altogether, this study unravels protein O-GlcNAcylation as a key determinant of myofibroblastic activation and identifies its inhibition as an avenue to intervene with fibrogenic processes.

NCAD v1.0: a database for non-coding variant annotation and interpretation

The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.

MotifHub: Detection of trans-acting DNA motif group with probabilistic modeling algorithm

BACKGROUND

Trans-acting factors are of special importance in transcription regulation, which is a group of proteins that can directly or indirectly recognize or bind to the 8-12 bp core sequence of cis-acting elements and regulate the transcription efficiency of target genes. The progressive development in high-throughput chromatin capture technology (e.g., Hi-C) enables the identification of chromatin-interacting sequence groups where trans-acting DNA motif groups can be discovered. The problem difficulty lies in the combinatorial nature of DNA sequence pattern matching and its underlying sequence pattern search space.

METHOD

Here, we propose to develop MotifHub for trans-acting DNA motif group discovery on grouped sequences. Specifically, the main approach is to develop probabilistic modeling for accommodating the stochastic nature of DNA motif patterns.

RESULTS

Based on the modeling, we develop global sampling techniques based on EM and Gibbs sampling to address the global optimization challenge for model fitting with latent variables. The results reflect that our proposed approaches demonstrate promising performance with linear time complexities.

CONCLUSION

MotifHub is a novel algorithm considering the identification of both DNA co-binding motif groups and trans-acting TFs. Our study paves the way for identifying hub TFs of stem cell development (OCT4 and SOX2) and determining potential therapeutic targets of prostate cancer (FOXA1 and MYC). To ensure scientific reproducibility and long-term impact, its matrix-algebra-optimized source code is released at http://bioinfo.cs.cityu.edu.hk/MotifHub.

CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms

Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2 Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidate cis-regulatory elements (cCREs), CREaTor can model cell type-specific cis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of only RNA-seq and ChIP-seq data, allows for the ready generalization of CREaTor to a broad range of cell types.

Integrative approaches based on genomic techniques in the functional studies on enhancers

With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.

Enhancer target prediction: state-of-the-art approaches and future prospects

Enhancers are genomic regions that regulate gene transcription and are located far away from the transcription start sites of their target genes. Enhancers are highly enriched in disease-associated variants and thus deciphering the interactions between enhancers and genes is crucial to understanding the molecular basis of genetic predispositions to diseases. Experimental validations of enhancer targets can be laborious. Computational methods have thus emerged as a valuable alternative for studying enhancer-gene interactions. A variety of computational methods have been developed to predict enhancer targets by incorporating genomic features (e.g. conservation, distance, and sequence), epigenomic features (e.g. histone marks and chromatin contacts) and activity measurements (e.g. covariations of enhancer activity and gene expression). With the recent advances in genome perturbation and chromatin conformation capture technologies, data on experimentally validated enhancer targets are becoming available for supervised training of these methods and evaluation of their performance. In this review, we categorize enhancer target prediction methods based on their rationales and approaches. Then we discuss their merits and limitations and highlight the future directions for enhancer targets prediction.

A unifying statistical framework to discover disease genes from GWASs

Genome-wide association studies (GWASs) identify genomic loci associated with complex traits, but it remains a challenge to identify the genes affected by causal genetic variants in these loci. Attempts to solve this challenge are frustrated by a number of compounding problems. Here, we show how to combine solutions to these problems into a unified mathematical framework. From this synthesis, it becomes possible to compute the probability that each gene in the genome is affected by a causal variant, given a particular trait, without making assumptions about the relevant cell types or tissues. We validate each component of the framework individually and in combination. When applied to large GWASs of human disease, the resulting paradigm can rediscover the majority of well-known disease genes. Moreover, it establishes human genetics support for many genes previously implicated only by clinical or preclinical evidence, and it uncovers a plethora of novel disease genes with compelling biological rationale.

Prediction of Enhancers in DNA Sequence Data using a Hybrid CNN-DLSTM Model

Enhancer, a distal cis-regulatory element controls gene expression. Experimental prediction of enhancer elements is time-consuming and expensive. Consequently, various inexpensive deep learning-based fast methods have been developed for predicting the enhancers and determining their strength. In this paper, we have proposed a two-stage deep learning-based framework leveraging DNA structural features, natural language processing, convolutional neural network, and long short-term memory to predict the enhancer elements accurately in the genomics data. In the first stage, we extracted the features from DNA sequence data by using three feature representation techniques viz., k-mer based feature extraction along with word2vector based interpretation of underlined patterns, one-hot encoding, and the DNAshape technique. In the second stage, strength of enhancers is predicted from the extracted features using a hybrid deep learning model. The method is capable of adapting itself to varying sizes of datasets. Also, as proposed model can capture long-range sequencing patterns, the robustness of the method remains unaffected against minor variations in the genomics sequence. The method outperforms the other state-of-the-art methods at both stages in terms of performance metrics of prediction accuracy, specificity, Mathew's correlation coefficient, and area under the ROC curve. In summary, the proposed method is a reliable method for enhancer prediction.

Enhancer/gene relationships: Need for more reliable genome-wide reference sets

Differences in cells' functions arise from differential activity of regulatory elements, including enhancers. Enhancers are cis-regulatory elements that cooperate with promoters through transcription factors to activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. There is increasing evidence that genetic variants associated with common diseases are enriched in enhancers active in cell types relevant to these diseases. Identifying the enhancers associated with genes and conversely, the sets of genes activated by each enhancer (the so-called enhancer/gene or E/G relationships) across cell types, can help understanding the genetic mechanisms underlying human diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: 1) genetic link methods or eQTL, 2) functional link methods based on 1D functional data such as open chromatin, histone mark or gene expression and 3) spatial link methods based on 3D data such as HiC. Since 1) and 3) are costly, the current strategy is to develop functional link methods and to use data from 1) and 3) as reference to evaluate them. However, there is still no consensus on the best functional link method to date, and method comparison remain seldom. Here, we compared the relative performances of three recent methods for the identification of enhancer-gene links, TargetFinder, Average-Rank, and the ABC model, using the three latest benchmarks from the field: a reference that combines 3D and eQTL data, called BENGI, and two genetic screening references, called CRiFF and CRiSPRi. Overall, none of the three methods performed best on the three references. CRiFF and CRISPRi reference sets are likely more reliable, but CRiFF is not genome-wide and CRiFF and CRISPRi are mostly available on the K562 cancer cell line. The BENGI reference set is genome-wide but likely contains many false positives. This study therefore calls for new reliable and genome-wide E/G reference data rather than new functional link E/G identification methods.

Synthesizing genome regulation data with vote-counting

The increasing availability of high-throughput datasets allows amalgamating research information across a large body of genome regulation studies. Given the recent success of meta-analyses on transcriptional regulators, epigenetic marks, and enhancer:gene associations, we expect that such surveys will continue to provide novel and reproducible insights. However, meta-analyses are severely hampered by the diversity of available data, concurring protocols, an eclectic amount of bioinformatics tools, and myriads of conceivable parameter combinations. Such factors can easily bar life scientists from synthesizing omics data and substantially curb their interpretability. Despite statistical challenges of the method, we would like to emphasize the advantages of joining data from different sources through vote-counting and showcase examples that achieve a simple but highly intuitive data integration.

Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning

Gene transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). As the transcription of ncRNAs, especially of enhancer RNAs, is often low and cell type specific, how the levels of RNA transcription depend on genotype remains largely unexplored. Here we report the development and utility of a machine-learning model (MENTR) that reliably links genome sequence and ncRNA expression at the cell type level. Effects on ncRNA transcription predicted by the model were concordant with estimates from published studies in a cell-type-dependent manner, regardless of allele frequency and genetic linkage. Among 41,223 variants from genome-wide association studies, the model identified 7,775 enhancer RNAs and 3,548 long ncRNAs causally associated with complex traits across 348 major human primary cells and tissues, such as rare variants plausibly altering the transcription of enhancer RNAs to influence the risks of Crohn's disease and asthma. The model may aid the discovery of causal variants and the generation of testable hypotheses for biological mechanisms driving complex traits.

Functional genomics uncovers the transcription factor BNC2 as required for myofibroblastic activation in fibrosis

Tissue injury triggers activation of mesenchymal lineage cells into wound-repairing myofibroblasts, whose unrestrained activity leads to fibrosis. Although this process is largely controlled at the transcriptional level, whether the main transcription factors involved have all been identified has remained elusive. Here, we report multi-omics analyses unraveling Basonuclin 2 (BNC2) as a myofibroblast identity transcription factor. Using liver fibrosis as a model for in-depth investigations, we first show that BNC2 expression is induced in both mouse and human fibrotic livers from different etiologies and decreases upon human liver fibrosis regression. Importantly, we found that BNC2 transcriptional induction is a specific feature of myofibroblastic activation in fibrotic tissues. Mechanistically, BNC2 expression and activities allow to integrate pro-fibrotic stimuli, including TGFβ and Hippo/YAP1 signaling, towards induction of matrisome genes such as those encoding type I collagen. As a consequence, Bnc2 deficiency blunts collagen deposition in livers of mice fed a fibrogenic diet. Additionally, our work establishes BNC2 as potentially druggable since we identified the thalidomide derivative CC-885 as a BNC2 inhibitor. Altogether, we propose that BNC2 is a transcription factor involved in canonical pathways driving myofibroblastic activation in fibrosis.

Myofibroblasts contribute to the development of liver fibrosis. Here, the authors report that the transcription factor Basonuclin 2 (BNC2) integrates fibrogenic signals and drives myofibroblastic transcriptional activation in liver fibrosis.

DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)–to–gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET’s predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation–specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.

Analysis of the landscape of human enhancer sequences in biological databases

The process of gene regulation extends as a network in which both genetic sequences and proteins are involved. The levels of regulation and the mechanisms involved are multiple. Transcription is the main control mechanism for most genes, being the downstream steps responsible for refining the transcription patterns. In turn, gene transcription is mainly controlled by regulatory events that occur at promoters and enhancers. Several studies are focused on analyzing the contribution of enhancers in the development of diseases and their possible use as therapeutic targets. The study of regulatory elements has advanced rapidly in recent years with the development and use of next generation sequencing techniques. All this information has generated a large volume of information that has been transferred to a growing number of public repositories that store this information. In this article, we analyze the content of those public repositories that contain information about human enhancers with the aim of detecting whether the knowledge generated by scientific research is contained in those databases in a way that could be computationally exploited. The analysis will be based on three main aspects identified in the literature: types of enhancers, type of evidence about the enhancers, and methods for detecting enhancer-promoter interactions. Our results show that no single database facilitates the optimal exploitation of enhancer data, most types of enhancers are not represented in the databases and there is need for a standardized model for enhancers. We have identified major gaps and challenges for the computational exploitation of enhancer data.

Classification of non-coding variants with high pathogenic impact

Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribute to a wide range of diseases, most patients who receive a WGS-based diagnosis today harbour a protein-coding mutation. Functional interpretation and prioritization of non-coding variants represents a persistent challenge, and disease-causing non-coding variants remain largely unidentified. Depending on the disease, WGS fails to identify a candidate variant in 20–80% of patients, severely limiting the usefulness of sequencing for personalised medicine. Here we present FINSURF, a machine-learning approach to predict the functional impact of non-coding variants in regulatory regions. FINSURF outperforms state-of-the-art methods, owing in particular to optimized control variants selection during training. In addition to ranking candidate variants, FINSURF breaks down the score for each variant into contributions from individual annotations, facilitating the evaluation of their functional relevance. We applied FINSURF to a diverse set of 30 diseases with described causative non-coding mutations, and correctly identified the disease-causative non-coding variant within the ten top hits in 22 cases. FINSURF is implemented as an online server to as well as custom browser tracks, and provides a quick and efficient solution to prioritize candidate non-coding variants in realistic clinical settings.

Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

BACKGROUND

Revealing the gene targets of distal regulatory elements is challenging yet critical for interpreting regulome data. Experiment-derived enhancer-gene links are restricted to a small set of enhancers and/or cell types, while the accuracy of genome-wide approaches remains elusive due to the lack of a systematic evaluation. We combined multiple spatial and in silico approaches for defining enhancer locations and linking them to their target genes aggregated across >500 cell types, generating 1860 human genome-wide distal enhancer-to-target gene definitions (EnTDefs). To evaluate performance, we used gene set enrichment (GSE) testing on 87 independent ENCODE ChIP-seq datasets of 34 transcription factors (TFs) and assessed concordance of results with known TF Gene Ontology annotations, and other benchmarks.

RESULTS

The top ranked 741 (40%) EnTDefs significantly outperform the common, naïve approach of linking distal regions to the nearest genes, and the top 10 EnTDefs perform well when applied to ChIP-seq data of other cell types. The GSE-based ranking of EnTDefs is highly concordant with ranking based on overlap with curated benchmarks of enhancer-gene interactions. Both our top general EnTDef and cell-type-specific EnTDefs significantly outperform seven independent computational and experiment-based enhancer-gene pair datasets. We show that using our top EnTDefs for GSE with either genome-wide DNA methylation or ATAC-seq data is able to better recapitulate the biological processes changed in gene expression data performed in parallel for the same experiment than our lower-ranked EnTDefs.

CONCLUSIONS

Our findings illustrate the power of our approach to provide genome-wide interpretation regardless of cell type.

A framework to score the effects of structural variants in health and disease

While technological advances improved the identification of structural variants (SVs) in the human genome, their interpretation remains challenging. Several methods utilize individual mechanistic principles like the deletion of coding sequence or 3D genome architecture disruptions. However, a comprehensive tool using the broad spectrum of available annotations is missing. Here, we describe CADD-SV, a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Previously, supervised learning approaches were limited due to a small number and biased set of annotated pathogenic or benign SVs. We overcome this problem by using a surrogate training-objective, the Combined Annotation Dependent Depletion (CADD) of functional variants. We use human and chimpanzee derived SVs as proxy-neutral and contrast them with matched simulated variants as proxy-deleterious, an approach that has proven powerful for short sequence variants. Our tool computes summary statistics over diverse variant annotations and uses random forest models to prioritize deleterious structural variants. The resulting CADD-SV scores correlate with known pathogenic and rare population variants. We further show that we can prioritize somatic cancer variants as well as noncoding variants known to affect gene expression. We provide a website and offline-scoring tool for easy application of CADD-SV.

CT-FOCS: a novel method for inferring cell type-specific enhancer–promoter maps

Spatiotemporal gene expression patterns are governed to a large extent by the activity of enhancer elements, which engage in physical contacts with their target genes. Identification of enhancer–promoter (EP) links that are functional only in a specific subset of cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS (cell type FOCS), a statistical inference method that uses linear mixed effect models to infer EP links that show marked activity only in a single or a small subset of cell types out of a large panel of probed cell types. Analyzing 808 samples from FANTOM5, covering 472 cell lines, primary cells and tissues, CT-FOCS inferred such EP links more accurately than recent state-of-the-art methods. Furthermore, we show that strictly cell type-specific EP links are very uncommon in the human genome.

CharID: a two-step model for universal prediction of interactions between chromatin accessible regions

Open chromatin regions (OCRs) allow direct interaction between cis-regulatory elements and trans-acting factors. Therefore, predicting all potential OCR-mediated loops is essential for deciphering the regulation mechanism of gene expression. However, existing loop prediction tools are restricted to specific anchor types. Here, we present CharID (Chromatin Accessible Region Interaction Detector), a two-step model that combines neural network and ensemble learning to predict OCR-mediated loops. In the first step, CharID-Anchor, an attention-based hybrid CNN-BiGRU network is constructed to discriminate between the anchor and nonanchor OCRs. In the second step, CharID-Loop uses gradient boosting decision tree with chromosome-split strategy to predict the interactions between anchor OCRs. The performance was assessed in three human cell lines, and CharID showed superior prediction performance compared with other algorithms. In contrast to the methods designed to predict a particular type of loops, CharID can detect varieties of chromatin loops not limited to enhancer-promoter loops or architectural protein-mediated loops. We constructed the OCR-mediated interaction network using the predicted loops and identified hub anchors, which are highlighted by their proximity to housekeeping genes. By analyzing loops containing SNPs associated with cardiovascular disease, we identified an SNP-gene loop indicating the regulation mechanism of the GFOD1. Taken together, CharID universally predicts diverse chromatin loops beyond other state-of-the-art methods, which are limited by anchor types, and experimental techniques, which are limited by sensitivities drastically decaying with the genomic distance of anchors. Finally, we hosted Peaksniffer, a user-friendly web server that provides online prediction, query and visualization of OCRs and associated loops.

OUP accepted manuscript

Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-coding regulatory variants that integrates information about regulatory regions with prediction scores and HPO-based prioritization. Firstly, we created a comprehensive collection of annotations for regulatory regions including a database of 2.4 million regulatory elements (GREEN-DB) annotated with controlled gene(s), tissue(s) and associated phenotype(s) where available. Secondly, we calculated a variation constraint metric and showed that constrained regulatory regions associate with disease-associated genes and essential genes from mouse knock-outs. Thirdly, we compared 19 non-coding impact prediction scores providing suggestions for variant prioritization. Finally, we developed a VCF annotation tool (GREEN-VARAN) that can integrate all these elements to annotate variants for their potential regulatory impact. In our evaluation, we show that GREEN-DB can capture previously published disease-associated non-coding variants as well as identify additional candidate disease genes in trio analyses.

Long-range promoter-enhancer contacts are conserved during evolution and contribute to gene expression robustness

Gene expression is regulated through complex molecular interactions, involving cis-acting elements that can be situated far away from their target genes. Data on long-range contacts between promoters and regulatory elements are rapidly accumulating. However, it remains unclear how these regulatory relationships evolve and how they contribute to the establishment of robust gene expression profiles. Here, we address these questions by comparing genome-wide maps of promoter-centered chromatin contacts in mouse and human. We show that there is significant evolutionary conservation of cis-regulatory landscapes, indicating that selective pressures act to preserve not only regulatory element sequences but also their chromatin contacts with target genes. The extent of evolutionary conservation is remarkable for long-range promoter–enhancer contacts, illustrating how the structure of regulatory landscapes constrains large-scale genome evolution. We show that the evolution of cis-regulatory landscapes, measured in terms of distal element sequences, synteny, or contacts with target genes, is significantly associated with gene expression evolution.

Comprehensive multi-omics integration identifies differentially active enhancers during human brain development with clinical relevance

BACKGROUND

Non-coding regulatory elements (NCREs), such as enhancers, play a crucial role in gene regulation, and genetic aberrations in NCREs can lead to human disease, including brain disorders. The human brain is a complex organ that is susceptible to numerous disorders; many of these are caused by genetic changes, but a multitude remain currently unexplained. Understanding NCREs acting during brain development has the potential to shed light on previously unrecognized genetic causes of human brain disease. Despite immense community-wide efforts to understand the role of the non-coding genome and NCREs, annotating functional NCREs remains challenging.

METHODS

Here we performed an integrative computational analysis of virtually all currently available epigenome data sets related to human fetal brain.

RESULTS

Our in-depth analysis unravels 39,709 differentially active enhancers (DAEs) that show dynamic epigenomic rearrangement during early stages of human brain development, indicating likely biological function. Many of these DAEs are linked to clinically relevant genes, and functional validation of selected DAEs in cell models and zebrafish confirms their role in gene regulation. Compared to enhancers without dynamic epigenomic rearrangement, DAEs are subjected to higher sequence constraints in humans, have distinct sequence characteristics and are bound by a distinct transcription factor landscape. DAEs are enriched for GWAS loci for brain-related traits and for genetic variation found in individuals with neurodevelopmental disorders, including autism.

CONCLUSION

This compendium of high-confidence enhancers will assist in deciphering the mechanism behind developmental genetics of human brain and will be relevant to uncover missing heritability in human genetic brain disorders.

Integrative analysis of epigenetics data identifies gene-specific regulatory elements

Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.

Predict long-range enhancer regulation based on protein-protein interactions between transcription factors

Long-range regulation by distal enhancers plays critical roles in cell-type specific transcriptional programs. Computational predictions of genome-wide enhancer-promoter interactions are still challenging due to limited accuracy and the lack of knowledge on the molecular mechanisms. Based on recent biological investigations, the protein-protein interactions (PPIs) between transcription factors (TFs) have been found to participate in the regulation of chromatin loops. Therefore, we developed a novel predictive model for cell-type specific enhancer-promoter interactions by leveraging the information of TF PPI signatures. Evaluated by a series of rigorous performance comparisons, the new model achieves superior performance over other methods. The model also identifies specific TF PPIs that may mediate long-range regulatory interactions, revealing new mechanistic understandings of enhancer regulation. The prioritized TF PPIs are associated with genes in distinct biological pathways, and the predicted enhancer-promoter interactions are strongly enriched with cis-eQTLs. Most interestingly, the model discovers enhancer-mediated trans-regulatory links between TFs and genes, which are significantly enriched with trans-eQTLs. The new predictive model, along with the genome-wide analyses, provides a platform to systematically delineate the complex interplay among TFs, enhancers and genes in long-range regulation. The novel predictions also lead to mechanistic interpretations of eQTLs to decode the genetic associations with gene expression.

Leveraging three-dimensional chromatin architecture for effective reconstruction of enhancer-target gene regulatory interactions

A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer-target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the hierarchy of structural domains which define chromatin three-dimensional (3D) architecture. Here we introduce a change of perspective based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work (i) we develop a computational and statistical framework to reconstruct a comprehensive map of ETG pairs leveraging functional genomics data; (ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy and (iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research.

Differential contribution to gene expression prediction of histone modifications at enhancers or promoters

The ChIP-seq signal of histone modifications at promoters is a good predictor of gene expression in different cellular contexts, but whether this is also true at enhancers is not clear. To address this issue, we develop quantitative models to characterize the relationship of gene expression with histone modifications at enhancers or promoters. We use embryonic stem cells (ESCs), which contain a full spectrum of active and repressed (poised) enhancers, to train predictive models. As many poised enhancers in ESCs switch towards an active state during differentiation, predictive models can also be trained on poised enhancers throughout differentiation and in development. Remarkably, we determine that histone modifications at enhancers, as well as promoters, are predictive of gene expression in ESCs and throughout differentiation and development. Importantly, we demonstrate that their contribution to the predictive models varies depending on their location in enhancers or promoters. Moreover, we use a local regression (LOESS) to normalize sequencing data from different sources, which allows us to apply predictive models trained in a specific cellular context to a different one. We conclude that the relationship between gene expression and histone modifications at enhancers is universal and different from promoters. Our study provides new insight into how histone modifications relate to gene expression based on their location in enhancers or promoters.

Gene expression can be properly predicted by the ChIP-seq signal of histone modifications at promoters, but whether this is also true at enhancers is unclear. In this study we develop predictive models of gene expression that demonstrate the predictive power of histone modifications at enhancers in the context of mouse embryonic stem cells, during differentiation, and in animal development. Moreover, by assessing the contribution of each histone modification, we found that enhancer predictive models and promoter predictive models have different histone modification requirement. Therefore, different histone modifications relate better to enhancer or promoter function(s). Finally, by applying predictive models trained in a specific cellular context to a different one, we concluded that the relationship between gene expression and histone modifications at enhancers is universal.

Eight novel susceptibility loci and putative causal variants in atopic dermatitis

BACKGROUND

Atopic dermatitis (AD) is the most common allergic disease in the world. While genetic components play critical roles in its pathophysiology, a large proportion of its genetic background is still unexplored.

OBJECTIVES

This study sought to illuminate the genetic associations with AD using genome-wide association study (GWAS) and its downstream analyses.

METHODS

This study conducted a GWAS for AD comprising 2,639 cases and 115,648 controls in the Japanese population, followed by a trans-ethnic meta-analysis with UK Biobank data and downstream analyses including partitioning heritability analysis by linkage disequilibrium score regression.

RESULTS

This study identified 17 significant susceptibility loci, among which 4 loci-AFF1, ITGB8, EHMT1, and EGR2-were novel in the Japanese GWAS. The trans-ethnic meta-analysis revealed 4 additional novel loci, namely-ZBTB38,LOC105755953/LOC101928272, TRAF3, andIQGAP1. This study found a missense variant (R243W) with a deleterious functional effect in NLRP10 and a variant altering expression of CCDC80 via enhancer expression as highly likely causal variants. These 2 regions were Asian-specific, and these population-specific associations could be explained by the frequency of causal variants. The gene-based test showed SMAD4 as an additional novel significant locus. Downstream analyses revealed substantial overlap of GWAS significant signals in enhancers of skin cells and immune cells, especially CD4 T cells. A highly shared polygenic architecture of AD between Europeans and Asians was also found.

CONCLUSIONS

This study identified Japanese-specific loci and novel significant loci shared by different populations. Two putative causal variants were illuminated in Japanese-specific loci. Trans-ethnic analyses revealed strong heritability enrichment in immune-related pathways, and relevant cell types shared among populations.

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, "precision medicine," which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.

On the importance of evolutionary constraint for regulatory sequence identification

Regulation of gene expression relies on the activity of specialized genomic elements, enhancers or silencers, distributed over sometimes large distance from their target gene promoters. A significant part of vertebrate genomes consists in such regulatory elements, but their identification and that of their target genes remains challenging, due to the lack of clear signature at the nucleotide level. For many years the main hallmark used for identifying functional elements has been their sequence conservation between genomes of distant species, indicative of purifying selection. More recently, genome-wide biochemical assays have opened new avenues for detecting regulatory regions, shifting attention away from evolutionary constraints. Here, we review the respective contributions of comparative genomics and biochemical assays for the definition of regulatory elements and their targets and advocate that both sequence conservation and preserved synteny, taken as signature of functional constraint, remain essential tools in this task.

LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

BACKGROUND

To investigate the mechanisms driving regulatory evolution across tissues, we experimentally mapped promoters, enhancers, and gene expression in the liver, brain, muscle, and testis from ten diverse mammals.

RESULTS

The regulatory landscape around genes included both tissue-shared and tissue-specific regulatory regions, where tissue-specific promoters and enhancers evolved most rapidly. Genomic regions switching between promoters and enhancers were more common across species, and less common across tissues within a single species. Long Interspersed Nuclear Elements (LINEs) played recurrent evolutionary roles: LINE L1s were associated with tissue-specific regulatory regions, whereas more ancient LINE L2s were associated with tissue-shared regulatory regions and with those switching between promoter and enhancer signatures across species.

CONCLUSIONS

Our analyses of the tissue-specificity and evolutionary stability among promoters and enhancers reveal how specific LINE families have helped shape the dynamic mammalian regulome.

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.

A computational framework for identifying the transcription factors involved in enhancer-promoter loop formation

The pairwise interaction between transcription factors (TFs) plays an important role in enhancer-promoter loop formation. Although thousands of TFs in the human genome have been found, only a few TF pairs have been demonstrated to be related to loop formation. It is still a challenge to determine which TF pairs could be involved in the enhancer-promoter regulation network. This work describes a computational framework to identify TF pairs in enhancer-promoter regulation. By integrating different levels of data derived from Promoter Capture Hi-C, chromatin immunoprecipitation sequencing (ChIP-seq) of histone marks, RNA-seq, protein-protein interaction (PPI), and TF motif, we identified 361 significant TF pairs and constructed a TF interaction network. From the network, we found several hub-TFs, which may have important roles in the regulation of long-range interactions. Our studies extended TF pairs identified in other experimental and computational approaches. These findings will help the further study of long-range interactions between enhancers and promoters.

Data integration for inferring context-specific gene regulatory networks

Transcriptional regulatory networks control context-specific gene expression patterns and play important roles in normal and disease processes. Advances in genomics are rapidly increasing our ability to measure different components of the regulation machinery at the single-cell and bulk population level. An important challenge is to combine different types of regulatory genomic measurements to construct a more complete picture of gene regulatory networks across different disease, environmental, and developmental contexts. In this review, we focus on recent computational methods that integrate regulatory genomic data sets to infer context specificity and dynamics in regulatory networks.

Tumor suppressor p53: from engaging DNA to target gene regulation

The p53 transcription factor confers its potent tumor suppressor functions primarily through the regulation of a large network of target genes. The recent explosion of next generation sequencing protocols has enabled the study of the p53 gene regulatory network (GRN) and underlying mechanisms at an unprecedented depth and scale, helping us to understand precisely how p53 controls gene regulation. Here, we discuss our current understanding of where and how p53 binds to DNA and chromatin, its pioneer-like role, and how this affects gene regulation. We provide an overview of the p53 GRN and the direct and indirect mechanisms through which p53 affects gene regulation. In particular, we focus on delineating the ubiquitous and cell type-specific network of regulatory elements that p53 engages; reviewing our understanding of how, where, and when p53 binds to DNA and the mechanisms through which these events regulate transcription. Finally, we discuss the evolution of the p53 GRN and how recent work has revealed remarkable differences between vertebrates, which are of particular importance to cancer researchers using mouse models.

The intersectional genetics landscape for humans

BACKGROUND

The human body is made up of hundreds-perhaps thousands-of cell types and states, most of which are currently inaccessible genetically. Intersectional genetic approaches can increase the number of genetically accessible cells, but the scope and safety of these approaches have not been systematically assessed. A typical intersectional method acts like an "AND" logic gate by converting the input of 2 or more active, yet unspecific, regulatory elements (REs) into a single cell type specific synthetic output.

RESULTS

Here, we systematically assessed the intersectional genetics landscape of the human genome using a subset of cells from a large RE usage atlas (Functional ANnoTation Of the Mammalian genome 5 consortium, FANTOM5) obtained by cap analysis of gene expression sequencing (CAGE-seq). We developed the heuristics and algorithms to retrieve and quality-rank "AND" gate intersections. Of the 154 primary cell types surveyed, >90% can be distinguished from each other with as few as 3 to 4 active REs, with quantifiable safety and robustness. We call these minimal intersections of active REs with cell-type diagnostic potential "versatile entry codes" (VEnCodes). Each of the 158 cancer cell types surveyed could also be distinguished from the healthy primary cell types with small VEnCodes, most of which were robust to intra- and interindividual variation. Methods for the cross-validation of CAGE-seq-derived VEnCodes and for the extraction of VEnCodes from pooled single-cell sequencing data are also presented.

CONCLUSIONS

Our work provides a systematic view of the intersectional genetics landscape in humans and demonstrates the potential of these approaches for future gene delivery technologies.

Enhancer-gene maps in the human and zebrafish genomes using evolutionary linkage conservation

The spatiotemporal expression of genes is controlled by enhancer sequences that bind transcription factors. Identifying the target genes of enhancers remains difficult because enhancers regulate gene expression over long genomic distances. To address this, we used an evolutionary approach to build two genome-wide maps of predicted enhancer-gene associations in the human and zebrafish genomes. Evolutionary conserved sequences were linked to their predicted target genes using PEGASUS, a bioinformatics method that relies on evolutionary conservation of synteny. The analysis of these maps revealed that the number of predicted enhancers linked to a gene correlate with its expression breadth. Comparison of both maps identified hundreds of putative vertebrate ancestral regulatory relationships from which we could determine that predicted enhancer-gene distances scale with genome size despite strong positional conservation. The two maps represent a resource for further studies, including the prioritization of sequence variants in whole genome sequence of patients affected by genetic diseases.

Exploring 3D chromatin contacts in gene regulation: The evolution of approaches for the identification of functional enhancer-promoter interaction

Mechanisms underlying gene regulation are key to understand how multicellular organisms with various cell types develop from the same genetic blueprint. Dynamic interactions between enhancers and genes are revealed to play central roles in controlling gene transcription, but the determinants to link functional enhancer-promoter pairs remain elusive. A major challenge is the lack of reliable approach to detect and verify functional enhancer-promoter interactions (EPIs). In this review, we summarized the current methods for detecting EPIs and described how developing techniques facilitate the identification of EPI through assessing the merits and drawbacks of these methods. We also reviewed recent state-of-art EPI prediction methods in terms of their rationale, data usage and characterization. Furthermore, we briefly discussed the evolved strategies for validating functional EPIs.

Integrative prediction of gene expression with chromatin accessibility and conformation data

BACKGROUND

Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter-enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.

RESULTS

We have extended our [Formula: see text] framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines.

CONCLUSION

We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.

Enhancer Domains Predict Gene Pathogenicity and Inform Gene Discovery in Complex Disease

Non-coding transcriptional regulatory elements are critical for controlling the spatiotemporal expression of genes. Here, we demonstrate that the sizes and number of enhancers linked to a gene reflect its disease pathogenicity. Moreover, genes with redundant enhancer domains are depleted of cis-acting genetic variants that disrupt gene expression, and they are buffered against the effects of disruptive non-coding mutations. Our results demonstrate that dosage-sensitive genes have evolved a robustness to the disruptive effects of genetic variation by expanding their regulatory domains. This solves a puzzle about why genes associated with human disease are depleted of cis-eQTLs (cis-expression quantitative trait loci), suggesting that this relationship might complicate gene identification in causal genome-wide association studies (GWASs) using eQTL information, and establishes a framework for identifying non-coding regulatory variation with phenotypic consequences.

Quantitative prediction of enhancer-promoter interactions

Recent experimental and computational efforts have provided large data sets describing three-dimensional organization of mouse and human genomes and showed the interconnection between the expression profile, epigenetic state, and spatial interactions of loci. These interconnections were utilized to infer the spatial organization of chromatin, including enhancer–promoter contacts, from one-dimensional epigenetic marks. Here, we show that the predictive power of some of these algorithms is overestimated due to peculiar properties of the biological data. We propose an alternative approach, which provides high-quality predictions of chromatin interactions using information on gene expression and CTCF-binding alone. Using multiple metrics, we confirmed that our algorithm could efficiently predict the three-dimensional architecture of both normal and rearranged genomes.

AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU

Background

The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model’s complexity. In our system Aikyatan, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications’ combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).

Results

We develop a suite of ML models, under the banner Aikyatan, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-‘a-vis the competition. Specifically, Aikyatan-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.

Conclusions

Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our Aikyatan suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3049-1) contains supplementary material, which is available to authorized users.

Novel Bioinformatics Approach Identifies Transcriptional Profiles of Lineage-Specific Transposable Elements at Distinct Loci in the Human Dorsolateral Prefrontal Cortex

Expression of transposable elements (TE) is transiently activated during human preimplantation embryogenesis in a developmental stage- and cell type-specific manner and TE-mediated epigenetic regulation is intrinsically wired in developmental genetic networks in human embryos and embryonic stem cells. However, there are no systematic studies devoted to a comprehensive analysis of the TE transcriptome in human adult organs and tissues, including human neural tissues. To investigate TE expression in the human Dorsolateral Prefrontal Cortex (DLPFC), we developed and validated a straightforward analytical approach to chart quantitative genome-wide expression profiles of all annotated TE loci based on unambiguous mapping of discrete TE-encoded transcripts using a de novo assembly strategy. To initially evaluate the potential regulatory impact of DLPFC-expressed TE, we adopted a comparative evolutionary genomics approach across humans, primates, and rodents to document conservation patterns, lineage-specificity, and colocalizations with transcription factor binding sites mapped within primate- and human-specific TE. We identified 654,665 transcripts expressed from 477,507 distinct loci of different TE classes and families, the majority of which appear to have originated from primate-specific sequences. We discovered 4,687 human-specific and transcriptionally active TEs in DLPFC, of which the prominent majority (80.2%) appears spliced. Our analyses revealed significant associations of DLPFC-expressed TE with primate- and human-specific transcription factor binding sites, suggesting potential cross-talks of concordant regulatory functions. We identified 1,689 TEs differentially expressed in the DLPFC of Schizophrenia patients, a majority of which is located within introns of 1,137 protein-coding genes. Our findings imply that identified DLPFC-expressed TEs may affect human brain structures and functions following different evolutionary trajectories. On one side, hundreds of thousands of TEs maintained a remarkably high conservation for ∼8 My of primates’ evolution, suggesting that they are likely conveying evolutionary-constrained primate-specific regulatory functions. In parallel, thousands of transcriptionally active human-specific TE loci emerged more recently, suggesting that they could be relevant for human-specific behavioral or cognitive functions.

Computational Biology Solutions to Identify Enhancers-target Gene Pairs

Enhancers are non-coding regulatory elements that are distant from their target gene. Their characterization still remains elusive especially due to challenges in achieving a comprehensive pairing of enhancers and target genes. A number of computational biology solutions have been proposed to address this problem leveraging the increasing availability of functional genomics data and the improved mechanistic understanding of enhancer action. In this review we focus on computational methods for genome-wide definition of enhancer-target gene pairs. We outline the different classes of methods, as well as their main advantages and limitations. The types of information integrated by each method, along with details on their applicability are presented and discussed. We especially highlight the technical challenges that are still unresolved and hamper the effective achievement of a satisfactory and comprehensive solution. We expect this field will keep evolving in the coming years due to the ever-growing availability of data and increasing insights into enhancers crucial role in regulating genome functionality.

Epigenetic and transcriptional profiling of triple negative breast cancer

The human HCC1806 cell line is frequently used as a preclinical model for triple negative breast cancer (TNBC). Given that dysregulated epigenetic mechanisms are involved in cancer pathogenesis, emerging therapeutic strategies target chromatin regulators, such as histone deacetylases. A comprehensive understanding of the epigenome and transcription profiling in HCC1806 provides the framework for evaluating efficacy and molecular mechanisms of epigenetic therapies. Thus, to study the interplay of transcription and chromatin in the HCC1806 preclinical model, we performed nascent transcription profiling using Precision Run-On coupled to sequencing (PRO-seq). Additionally, we mapped the genome-wide locations for RNA polymerase II (Pol II), the histone variant H2A.Z, seven histone modifications, and CTCF using ChIP-exo. ChIP-exonuclease (ChIP-exo) is a refined version of ChIP-seq with near base pair precision mapping of protein-DNA interactions. In this Data Descriptor, we present detailed information on experimental design, data generation, quality control analysis, and data validation. We discuss how these data lay the foundation for future analysis to understand the relationship between the nascent transcription and chromatin.