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High-throughput methods such as RNA-seq, ChIP-seq, and ATAC-seq have well-established guidelines, commercial kits, and analysis pipelines that enable consistency and wider adoption for understanding genome function and regulation. STARR-seq, a popular assay for directly quantifying the activities of thousands of enhancer sequences simultaneously, has seen limited standardization across studies. The assay is long, with more than 250 steps, and frequent customization of the protocol and variations in bioinformatics methods raise concerns for reproducibility of STARR-seq studies. Here, we assess each step of the protocol and analysis pipelines from published sources and in-house assays, and identify critical steps and quality control (QC) checkpoints necessary for reproducibility of the assay. We also provide guidelines for experimental design, protocol scaling, customization, and analysis pipelines for better adoption of the assay. These resources will allow better optimization of STARR-seq for specific research needs, enable comparisons and integration across studies, and improve the reproducibility of results.

High-throughput reporter assays such as self-transcribing active regulatory region sequencing (STARR-seq) have made it possible to measure regulatory element activity across the entire human genome at once. The resulting data, however, present substantial analytical challenges. Here, we identify technical biases that explain most of the variance in STARR-seq data. We then develop a statistical model to correct those biases and to improve detection of regulatory elements. This approach substantially improves precision and recall over current methods, improves detection of both activating and repressive regulatory elements, and controls for false discoveries despite strong local correlations in signal.

Genetic engineering of cis-regulatory elements in crop plants is a promising strategy to ensure food security. However, such engineering is currently hindered by our limited knowledge of plant cis-regulatory elements. Here, we adapted STARR-seq — a technology for the high-throughput identification of enhancers — for its use in transiently transformed tobacco leaves. We demonstrate that the optimal placement in the reporter construct of enhancer sequences from a plant virus, pea and wheat was just upstream of a minimal promoter, and that none of these four known enhancers was active in the 3′-UTR of the reporter gene. The optimized assay sensitively identified small DNA regions containing each of the four enhancers, including two whose activity was stimulated by light. Furthermore, we coupled the assay to saturation mutagenesis to pinpoint functional regions within an enhancer, which we recombined to create synthetic enhancers. Our results describe an approach to define enhancer properties that can be performed in potentially any plant species or tissue transformable by Agrobacterium and that can use regulatory DNA derived from any plant genome.One-sentence summaryWe developed a high-throughput assay in transiently transformed tobacco leaves that can identify enhancers, characterize their functional elements and detect condition-specific enhancer activity.

Massively parallel reporter assays (MPRAs) test the capacity of putative gene regulatory elements to drive transcription on a genome-wide scale. Most gene regulatory activity occurs within accessible chromatin, and recently described methods have combined assays that capture these regions—such as assay for transposase-accessible chromatin using sequencing (ATAC-seq)—with self-transcribing active regulatory region sequencing (STARR-seq) to selectively assay the regulatory potential of accessible DNA (ATAC-STARR-seq). Here, we report an integrated approach that quantifies activating and silencing regulatory activity, chromatin accessibility, and transcription factor (TF) occupancy with one assay using ATAC-STARR-seq. Our strategy, including important updates to the ATAC-STARR-seq assay and workflow, enabled high-resolution testing of ∼50 million unique DNA fragments tiling ∼101,000 accessible chromatin regions in human lymphoblastoid cells. We discovered that 30% of all accessible regions contain an activator, a silencer, or both. Although few MPRA studies have explored silencing activity, we demonstrate that silencers occur at similar frequencies to activators, and they represent a distinct functional group enriched for unique TF motifs and repressive histone modifications. We further show that Tn5 cut-site frequencies are retained in the ATAC-STARR plasmid library compared to standard ATAC-seq, enabling TF occupancy to be ascertained from ATAC-STARR data. With this approach, we found that activators and silencers cluster by distinct TF footprint combinations, and these groups of activity represent different gene regulatory networks of immune cell function. Altogether, these data highlight the multilayered capabilities of ATAC-STARR-seq to comprehensively investigate the regulatory landscape of the human genome all from a single DNA fragment source.

STARR-seq and its variants have been widely used to characterize enhancers. However, it has been reported that up to 87% of STARR peaks are located in repressive chromatins and are not functional in the tested cells. While some of the STARR peaks in repressive chromatins might be active in other cell/tissue types, some others might be false positives. Meanwhile, many active enhancers may not be identified by the current STARR-seq methods. However, the prevalence of and underlying causes for the artifacts are not fully understood. Based on predictedcis-regulatory modules (CRMs) and non-CRMs in the human genome as well as predicted active CRMs and non-active CRMs in a few human cell lines with STARR-seq data available, we reveal prevalent false positives and false negatives in STARR peaks and possible underlying causes. Our results will help design strategies to improve STARR-seq methods and interpret the results.

Enhancers are short regions of non-coding DNA that increase transcription rates of genes despite being located distantly from the genes themselves [5]. Enhancers are identified through experimental techniques such as ChIP-Seq or CUT&RUN with H3K4me1 and H3K27ac histone modifications, self-transcribing active regulatory region sequencing (STARR-Seq), and massively parallel reporter assays (MPRA). Machine learning models have been used in conjunction with experimental data to identify enhancer activity from sequences [3], predict enhancer-transcription factor interactions [4], and decode the enhancer regulatory language [2]. We describe a framework that connects peak calling errors to the prediction accuracy of sequence models. The key assumptions of our framework are that (1) enhancers have consistent sequence patterns that can be used to separate enhancers from control sequences, (2) errors in the training data impact prediction accuracies in predictable ways, and (3) prediction accuracy is a useful proxy for evaluating peak calling accuracy. In the framework, data sets are constructed from peak (positive) and randomly sampled (control) sequences. Machine learning models are trained and evaluated on the sequences in a cross-chromosome (cross-fold) setup. Lastly, precision of the originating peaks are evaluated by calculating true and false positive rates. We applied our framework to evaluate peaks for D. melanogaster STARR-Seq data [1] called with the MACS software [6]. Although designed for ChIP-Seq data, MACS can be used to process other types of data, but users must be careful about parameter choices. We evaluated different parameter combinations with our framework and visual comparisons of called peaks. True and false positive rates ranged from a high of 88.0% to a low of 74.7% and from a low of 18.6% to a high of 49.4%, respectively. The default MACS parameters produced the highest true and lowest false positive rates, suggesting that the default parameters are also suitable for STARR-Seq data. Our results demonstrate the utility of our framework through a practical application and provide a base for future development.

Enhancers activate transcription in a distance-, orientation-, and position-independent manner, which makes them difficult to be identified. Self-transcribing active regulatory region sequencing (STARR-seq) measures the enhancer activity of millions of DNA fragments in parallel. Here we used STARR-seq to generate a quantitative global map of rice enhancers. Most enhancers were mapped within genes, especially at the 5′ untranslated regions (5′UTR) and in coding sequences. Enhancers were also frequently mapped proximal to silent and lowly-expressed genes in transposable element (TE)-rich regions. Analysis of the epigenetic features of enhancers at their endogenous loci revealed that most enhancers do not co-localize with DNase I hypersensitive sites (DHSs) and lack the enhancer mark of histone modification H3K4me1. Clustering analysis of enhancers according to their epigenetic marks revealed that about 40% of identified enhancers carried one or more epigenetic marks. Repressive H3K27me3 was frequently enriched with positive marks, H3K4me3 and/or H3K27ac, which together label enhancers. Intergenic enhancers were also predicted based on the location of DHS regions relative to genes, which overlap poorly with STARR-seq enhancers. In summary, we quantitatively identified enhancers by functional analysis in the genome of rice, an important model plant. This work provides a valuable resource for further mechanistic studies in different biological contexts. Keywords: Plant, Enhancer, Functional analysis, Epigenetic modification, Gene expression

Embryonic stem cells (ESCs) can differentiate into any given cell type and therefore represent a versatile model to study the link between gene regulation and differentiation. To quantitatively assess the dynamics of enhancer activity during the early stages of murine ESC differentiation, we analyzed accessible genomic regions using STARR-seq, a massively parallel reporter assay. This resulted in a genome-wide quantitative map of active mESC enhancers, in pluripotency and during the early stages of differentiation. We find that only a minority of accessible regions is active and that such regions are enriched near promoters, characterized by specific chromatin marks, enriched for distinct sequence motifs, and modeling shows that active regions can be predicted from sequence alone. Regions that change their activity upon retinoic acid-induced differentiation are more prevalent at distal intergenic regions when compared to constitutively active enhancers. Further, analysis of differentially active enhancers verified the contribution of individual TF motifs toward activity and inducibility as well as their role in regulating endogenous genes. Notably, the activity of retinoic acid receptor alpha (RARα) occupied regions can either increase or decrease upon the addition of its ligand, retinoic acid, with the direction of the change correlating with spacing and orientation of the RARα consensus motif and the co-occurrence of additional sequence motifs. Together, our genome-wide enhancer activity map elucidates features associated with enhancer activity levels, identifies regulatory regions disregarded by computational prediction tools, and provides a resource for future studies into regulatory elements in mESCs.

Enhancers are important cis-regulatory elements controlling cell-type specific expression patterns of genes. Furthermore, combinations of enhancers and minimal promoters are utilized to construct small, artificial promoters for gene delivery vectors. Large-scale functional screening methodology to construct genomic maps of enhancer activities has been successfully established in cultured cell lines, however, not yet applied to terminally differentiated cells and tissues in a living animal. Here, we transposed the Self-Transcribing Active Regulatory Region Sequencing (STARR-seq) technique to the mouse brain using adeno-associated-viruses (AAV) for the delivery of a highly complex screening library tiling entire genomic regions and covering in total 3 Mb of the mouse genome. We identified 483 sequences with enhancer activity, including sequences that were not predicted by DNA accessibility or histone marks. Characterizing the expression patterns of fluorescent reporters controlled by nine candidate sequences, we observed differential expression patterns also in sparse cell types. Together, our study provides an entry point for the unbiased study of enhancer activities in organisms during health and disease.

Enhancers are DNA sequences that can strengthen transcription initiation. However, the global identification of plant enhancers is complicated due to uncertainty in the distance and orientation of enhancers, especially in species with large genomes. In this study, we performed self-transcribing active regulatory region sequencing (STARR-seq) for the first time to identify enhancers across the barley genome. A total of 7323 enhancers were successfully identified, and among 45 randomly selected enhancers, over 75% were effective as validated by a dual-luciferase reporter assay system in the lower epidermis of tobacco leaves. Interestingly, up to 53.5% of the barley enhancers were repetitive sequences, especially transposable elements (TEs), thus reinforcing the vital role of repetitive enhancers in gene expression. Both the common active transcription marker H3K4me3 and repressive histone marker H3K27me3 were abundant among the barley STARR-seq enhancers. In addition, the functional range of barley STARR-seq enhancers seemed much broader than that of rice or maize and extended to ± 100 KB of the gene body, and this finding was consistent with the high expression levels of genes in the genome. This work specifically depicts the unique features of barley enhancers and provides available barley enhancers for further utilization.

BackgroundEnhancers are distal regulators of gene expression that shape cell identity and control cell fate transitions. In mouse embryonic stem cells (mESCs), the pluripotency network is maintained by the function of a complex network of enhancers, that are drastically altered upon differentiation. Genome-wide chromatin accessibility and histone modification assays are commonly used as a proxy for identifying putative enhancers and for describing their activity levels and dynamics.ResultsHere, we applied STARR-seq, a genome-wide plasmid-based assay, as a read-out for the enhancer landscape in “ground-state” (2i+LIF; 2iL) and “metastable” (serum+LIF; SL) mESCs. This analysis reveals that active STARR-seq loci show modest overlap with enhancer locations derived from peak calling of ChIP-seq libraries for common enhancer marks. We unveil ZIC3-bound loci with significant STARR-seq activity in SL-ESCs. Knock-out of Zic3 removes STARR-seq activity only in SL-ESCs and increases their propensity to differentiate towards the endodermal fate. STARR-seq also reveals enhancers that are not accessible, masked by a repressive chromatin signature. We describe a class of dormant, p53 bound enhancers that gain H3K27ac under specific conditions, such as after treatment with Nocodazol, or transiently during reprogramming from fibroblasts to pluripotency.ConclusionsIn conclusion, loci identified as active by STARR-seq often overlap with those identified by chromatin accessibility and active epigenetic marking, yet a significant fraction is epigenetically repressed or display condition-specific enhancer activity.

The blueprints to development, response to the environment, and cellular function are largely the manifestation of distinct gene expression programs controlled by the spatiotemporal activity ofcis-regulatory elements. Although biochemical methods for identifying accessible chromatin – a hallmark of activecis-regulatory elements – have been developed, approaches capable of measuring and quantifyingcis-regulatory activity are only beginning to be realized. Massively Parallel Reporter Assays coupled to chromatin accessibility profiling present a high-throughput solution for testing the transcription-activating capacity of millions of putatively regulatory DNA sequences in parallel.However, clear computational pipelines for analyzing these high-throughput sequencing-based reporter assays are lacking. In this protocol, I layout and rationalize a computational framework for the processing and analysis of Assay for Transposase Accessible Chromatin profiling followed by Self-Transcribed Active Regulatory Region sequencing (ATAC-STARR-seq) data from a recent study inZea mays. The approach described herein can be adapted to other sequencing-based reporter assays and is largely agnostic to the model organism with the appropriate input substitutions.

STARR-seq assesses millions of fragments in parallel measuring enhancer activity quantitatively. Here we show that STARR-seq is critically flawed with a systematic error in the cells of Arabidopsis thaliana (A. thaliana). Large amount of self-transcripts (STs) is lost during reverse transcription because these STs are polyadenylated after alternative polyadenylation sites (APAS) inside the test sequences. We solved this problem by using specially designed primer and recovered self-transcribed sequences independent from the PAS usage. In A. thaliana, we identified active enhancers and also enhancers quiescent in their endogenous genomic loci. Different from traditional STARR-seq identified enhancers, enhancers identified by new method are highly enriched in sequences proximal to the 5’ and 3’ ends of genes, and their epigenetic states correlate with gene expression levels. Our solution applies to methods based on self-transcript quantification. In addition, our results provide an invaluable functional enhancer activity map and insights into the functional complexity of enhancers in A. thaliana.

Ability to functionally screen gene regulatory sequences, such as promoters and enhancers, in high throughput manner is an important prerequisite for many basic and translational research programs. One of the methods that allow such screening is STARR-seq, or self-transcribing active regulatory region sequencing. It allows to quickly screen millions of candidate sequences in the cell type of interest. However, it does rely on transfection as a delivery method which can be a limiting-step for some hard-to-transfect cells such as senescent cells. Here we show that integration-deficient and integration-competent promoterless lentiviral particles can be used to deliver STARR-seq constructs into cells. These constructs reported CMV enhancer activity both at protein and mRNA level. While further validations are necessary, ability to deliver STARR-seq libraries using lentiviral particles will significantly improve the versatility and usability of such a method.Competing Interest StatementThe authors have declared no competing interest.View Full Text

STARR-Seq is a high-throughput technique for directly identifying genomic regions with enhancer activity [1]. Genomic DNA is sheared, inserted into artificial plasmids designed so that DNA with enhancer activity trigger self-transcription, and transfected into culture cells. The resulting RNA is converted back into cDNA, sequenced, and aligned to a reference genome. "Peaks" are called by comparing observed read depth at each point to an expected read depth from control DNA using a statistical test. Examples of peak calling methods based on read depth include MACS2 [4], basicSTARRSeq, and STARRPeaker [3]. It is challenging to accurately distinguish between real peaks and artifacts in regions where mean read depth is low but the variance is high. Fortunately, enhancer activity is strongly correlated with sequence content. We propose using sequence-based machine learning models in a semi-supervised framework to filter peaks. 501-bp sequences centered on the a11k STARR peaks from [1] were extracted from the Drosophila melanogaster dm3 genome. Randomly-sampled 501-bp sequences were used as a negative set. Peaks were filtered using a Bonferroni-corrected significance value (α = 0.05) to create a "high-confidence" subset of a2.2k peaks. A Logistic Regression model with k-mer count features was trained on the high-confidence peak sequences and their negatives and used to classifying the remaining a8.8k peak sequences. The self-trained, sequenced-based model identified an additional a3.7k candidate enhancers ("medium confidence"). The remaining a5k STARR peaks were considered "low confidence" peaks. We plotted histograms of the read depth log-fold change for the three sets of peaks (high, medium, and low confidence) (see Figure 1). The distributions for the medium- and low-confidence peaks overlapped significantly. The sequence-based model identified enhancer candidates that would otherwise be filtered out using read depth alone. We called peaks for the 4 D. melanogaster FAIRE-Seq data sets from [2]. Sequencing data were cleaned with Trimmomatic, aligned to the dm3 genome with bwa backtrack, and filtered for mapping quality (q

Transcriptional regulatory elements, including promoters and enhancers, play a key role in the cell-type specific regulation of the transcriptome. Application of rapidly evolving genetic tools, such as optogenetic/chemogenetic actuators and fluorescent reporters to elucidate the function of cell subtypes in vivo necessitates cell-type specific promoters or enhancers. In this context, methods for genome-wide functional screening of cis-regulatory elements, including enhancers, are of utmost importance. In this study, we describe a novel method for genome-wide functional screening of enhancer activity in vivo with minimal handling. Application of the method to cells from different brain structures and subsequent differential analysis allow identification of active enhancers in the target tissue or brain structures. To demonstrate proof of concept, we applied this method to samples from the dorsal raphe nucleus (DRN) and the medial prefrontal cortex of the mouse brain and successfully identified six enhancers with highly biased activity towards the dorsal raphe nucleus. Considering that these two structures consist of largely similar cell types whereas serotonin and dopamine neurons exist only in the DRN, our results confirm the validity of this method in identifying cell-type specific and brain-structure specific enhancers. Overall, this method will be helpful in identifying cis-regulatory elements suitable for cell-type specific manipulations.

Massively parallel reporter assays test the capacity of putative gene regulatory elements to drive transcription on a genome-wide scale. Most gene regulatory activity occurs within accessible chromatin, and recently described methods have combined assays that capture these regions—such as assay for transposase-accessible chromatin using sequencing (ATAC-seq)—with self-transcribing active regulatory region sequencing (STARR-seq) to selectively assay the regulatory potential of accessible DNA (ATAC-STARR-seq). Here, we report a multi-omic approach that quantifies regulatory activity, chromatin accessibility, and transcription factor (TF) occupancy with one assay using ATAC-STARR-seq. Our strategy, including important updates to the ATAC-STARR-seq assay design and workflow, enabled high-resolution testing of ∼50 million unique DNA fragments tiling ∼101,000 accessible chromatin regions in human lymphoblastoid cells. We discovered that 30% of all accessible regions contain an activator, a silencer or both. We demonstrate that activators and silencers represent distinct functional groups that are enriched for unique sets of TF motifs and are marked by specific combinations of histone modifications. Using Tn5 cut-sites retained by the ATAC-STARR library, we performed TF footprinting and stratified these groups by the presence of specific TF footprints that are supported by chromatin immunoprecipitation data. We found that activators and silencers clustered by distinct TF footprint combinations are enriched for distinct gene regulatory pathways, and thus, represent distinct gene regulatory networks of human lymphoblastoid cell function. Altogether, these data highlight the multi-faceted capabilities of ATAC-STARR-seq to comprehensively investigate the regulatory landscape of the human genome all from a single DNA fragment source.

Massively parallel reporter assays (MPRAs) test the capacity of putative gene regulatory elements to drive transcription on a genome-wide scale. Most gene regulatory activity occurs within accessible chromatin, and recently described methods have combined assays that capture these regions, such as assay for transposase-accessible chromatin using sequencing (ATAC-seq), with self-transcribing active regulatory region sequencing (STARR-seq) to selectively assay the regulatory potential of accessible DNA (ATAC-STARR-seq). Here, we report an integrated approach that quantifies activating and silencing regulatory activity, chromatin accessibility, and transcription factor (TF) occupancy with one assay using ATAC-STARR-seq. Our strategy, including important updates to the ATAC-STARR-seq assay and workflow, enabled high-resolution testing of ~50 million unique DNA fragments tiling ~101,000 accessible chromatin regions in human lymphoblastoid cells. We discovered that 30% of all accessible regions contain an activator, a silencer or both. Although few MPRA studies have explored silencing activity, we demonstrate silencers occur at similar frequencies to activators, and they represent a distinct functional group enriched for unique TF motifs and repressive histone modifications. We further show that Tn5 cut-site frequencies are retained in the ATAC-STARR plasmid library compared to standard ATAC-seq, enabling TF occupancy to be ascertained from ATAC-STARR data. With this approach, we found that activators and silencers cluster by distinct TF footprint combinations and these groups of activity represent different gene regulatory networks of immune cell function. Altogether, these data highlight the multi-layered capabilities of ATAC-STARR-seq to comprehensively investigate the regulatory landscape of the human genome all from a single DNA fragment source.

When bound to thyroid hormone, the nuclear receptor TRα1 activates the transcription of a number of genes in many cell types. It mainly acts by binding DNA as a heterodimer with retinoid X receptors at specific response elements related to the DR4 consensus sequence. However, the number of DR4-like elements in the genome exceed by far the number of occupied sites, indicating that minor variations in nucleotides composition deeply influence the DNA-binding capacity and transactivation activity of TRα1. An improved protocol of synthetic self-transcribing active regulatory region sequencing was used to quantitatively assess the transcriptional activity of thousands of synthetic sites in parallel. This functional screen highlights a strong correlation between the affinity of the heterodimers for DNA and their capacity to mediate the thyroid hormone response.

Massively parallel reporter assays test the capacity of putative cis-regulatory elements (CREs) to drive transcription on a genome-wide scale. In nearly all cases, chromatin accessibility is necessary to drive activity, so most CREs are inactive due to chromatin context rather than intrinsic DNA sequence properties. Here, we combined assay for transposase-accessible chromatin (ATAC-seq) with self-transcribing active regulatory region sequencing (STARR-seq) to selectively assay the regulatory potential of nucleosome-free DNA genome-wide. Our approach enabled high-resolution testing of ~50 million unique DNA fragments tiling ~101,000 accessible chromatin regions in human lymphoblastoid cells. To illustrate the application of our approach, we show that 30% of all accessible regions contain an activator, a silencer or both. Benchmarking against standard ATAC-seq, our approach faithfully captures chromatin accessibility and transcription factor (TF) footprints with high signal-to-noise. Integrating three layers of genomic information (accessibility, TF occupancy, and activity) provided by ATAC-STARR-seq, we stratified active and silent CREs by the presence of several TF footprints and show that CREs with specific TF combinations are associated with distinct gene regulatory pathways. Altogether, these data highlight the power of ATAC-STARR-seq to comprehensively investigate the regulatory landscape of the human genome from a single DNA source.

Transcriptional enhancers control cell-type specific gene expression in humans and dysfunction can lead to debilitating diseases, including cancer. Identifying bona-fide enhancers is difficult due to a lack of spatial or sequence constraints. In addition, only a small percentage of the genome is accessible in matured cell types; and therefore, most enhancers are inactive due to their chromatin context rather than intrinsic properties of the DNA sequence itself. For this reason, we decided to assay regulatory activity exclusively within accessible chromatin. To do this, we combined assay for transposase-accessible chromatin using sequencing (ATAC-seq) with self-transcribing active regulatory region sequencing (STARR-seq); we call this method ATAC-STARR-seq. With ATAC-STARR-seq, we identify both active and silent regulatory elements in GM12878 B cells; these active and silent elements are enriched for transcription factor motifs and histone modifications associated with activating and repressing regulation, respectively. We also show that ATAC-STARR-seq quantifies chromatin accessibility and transcription factor binding. We integrate this information and subset active regions based on transcription factor binding profiles. Depending on the transcription factors bound, subsets are enriched for distinct reactome pathways. Altogether, this highlights the power of ATAC-STARR-seq to investigate the transcriptional regulatory landscape of the human genome.

A single genome gives rise to different cell types and organs in response to precise temporal and spatial regulation of gene expression, driven by developmental and environmental cues. These expression patterns are orchestrated by cis -regulatory elements, distal enhancers, and gene-proximal

When bound to thyroid hormone, the nuclear receptor TRα1 activates the transcription of a number of genes in many cell types. It mainly acts by binding DNA as an heterodimer with RXRs at specific response elements related to the DR4 consensus sequence. However, the number of DR4-like elements in the genome exceed by far the number of occupied sites, indicating that minor variations in nucleotides composition deeply influence the DNA-binding capacity and transactivation activity of TRα1. An improved protocol of synthetic STARR-seq was used to quantitatively assess the transcriptional activity of thousands of synthetic sites in parallel. This functional screen highlights a strong correlation between the affinity of the heterodimers for DNA and their capacity to mediate the thyroid hormone response.

Identification of enhancers has been a challenge in plants. STARR-seq measures enhancer activity of millions fragments in parallel. Here we present a global map of rice enhancers quantitatively determined using STARR-seq. Most enhancers are mapped within genes, especially at the 5’ untranslated regions (5’ UTR) and the coding sequences. Silent and low expressed genes in genomic regions enriched with transposable elements (TEs) are frequently found containing proximal enhancers. Analysis of enhancer epigenetic features at their endogenous loci revealed that most enhancers do not co-localize with DNase I hypersensitive sites (DHSs) and are lack of H3K4me1. Clustering enhancers by their epigenetic modifications revealed that about 40% of identified enhancers carry one or more epigenetic marks. Repressive H3K27me3 is frequently enriched with positive marks of H3K4m3 and/or H3K27ac, which together may bookmark poised enhancers. Intergenic enhancers were predicted based on the location of DHS relative to genes, which overlap poorly with functionally identified enhancers. In summary, enhancers were quantitatively identified by functional analysis in a model plant genome, which provides a valuable resource for further mechanistic studies in different biological contexts.

The binding of transcription factors to short recognition sequences plays a pivotal role in controlling the expression of genes. The sequence and shape characteristics of binding sites influence DNA binding specificity and have also been implicated in modulating the activity of transcription factors downstream of binding. To quantitatively assess the transcriptional activity of tens of thousands of designed synthetic sites in parallel, we developed a synthetic version of STARR-seq (synSTARR-seq). We used the approach to systematically analyze how variations in the recognition sequence of the glucocorticoid receptor (GR) affect transcriptional regulation. Our approach resulted in the identification of a novel highly active functional GR binding sequence and revealed that sequence variation both within and flanking GR’s core binding site can modulate GR activity without apparent changes in DNA binding affinity. Notably, we found that the sequence composition of variants with similar activity profiles was highly diverse. In contrast, groups of variants with similar activity profiles showed specific DNA shape characteristics indicating that DNA shape may be a better predictor of activity than DNA sequence. Finally, using single cell experiments with individual enhancer variants, we obtained clues indicating that the architecture of the response element can independently tune expression mean and cell-to cell variability in gene expression (noise). Together, our studies establish synSTARR as a powerful method to systematically study how DNA sequence and shape modulate transcriptional output and noise., Author summary The expression level of genes is controlled by transcription factors, which are proteins that bind to genomic response elements that contain their recognition DNA sequence. Importantly, genes are not simply turned on but need to be expressed at the right level. This is, at least in part, assured by the sequence composition of genomic response elements. Here, we studied how the recognition DNA sequence influences gene regulation by a transcription factor called the glucocorticoid receptor. Specifically, we developed a method to test the activity of variants in a highly parallelized setting where everything is kept identical except for the sequence of the binding site. The systematic analysis of tens of thousands of sequence variants facilitated the identification of a previously unknown sequence variant with high activity. Moreover, we report how sequence variation of the response element influences cell-to-cell variability in expression levels. Finally, we observe similar activity profiles for distinct sequence variants that share similar three-dimensional DNA shape characteristics arguing that the three-dimensional perception of DNA by the glucocorticoid receptor, modulates its activity towards individual target genes.

Transcriptional enhancers control cell-type specific gene expression in humans and dysfunction can lead to debilitating diseases, including cancer. Identifying bona-fide enhancers is difficult due to a lack of spatial or sequence constraints. In addition, only a small percentage of the genome is accessible in matured cell types; and therefore, most enhancers are inactive due to their chromatin context rather than intrinsic properties of the DNA sequence itself. For this reason, we decided to assay regulatory activity exclusively within accessible chromatin. To do this, we combined assay for transposase-accessible chromatin using sequencing (ATAC-seq) with self-transcribing active regulatory region sequencing (STARR-seq); we call this method ATAC-STARR-seq. With ATAC-STARR-seq, we identify both active and silent regulatory elements in GM12878 B cells; these active and silent elements are enriched for transcription factor motifs and histone modifications associated with activating and repressing regulation, respectively. We also show that ATAC-STARR-seq quantifies chromatin accessibility and transcription factor binding. We integrate this information and subset active regions based on transcription factor binding profiles. Depending on the transcription factors bound, subsets are enriched for distinct reactome pathways. Altogether, this highlights the power of ATAC-STARR-seq to investigate the transcriptional regulatory landscape of the human genome.

Additional file 1: Supplementary Figure S1. Comparison of STARR-seq output coverage calculated using the center of the fragment to using the start position of the sequencing read. (A) Distribution of shift in final peak locations resulting from using two alternative coverage counting schemes in HepG2. Comparison of (B) overall fold enrichment level, (C) P value, and (D) size of resulting peaks. Supplementary Figure S2. (Shadow figure of Fig. 2) Correlation of STARR-seq output coverage with covariates (STARR-seq input coverage, GC content, mappability, and RNA structure) in various STARR-seq datasets, including (A) K562, (B) HeLa-S3 untreated WG-STARR-seq (Muerdter 2018), (C) GM12878 HiDRA ATAC-STARR-seq (Wang 2018), and (D) K562 Cap-STARR-seq (Rathert 2015). PCC: Pearson Correlation Coefficient. Supplementary Figure S3. Contribution of covariates and model selection. (A) Q-Q plots of residuals for various models with different sets of covariates showing the goodness of fit. (B) Both AIC and BIC measure relative qualities of statistical models considering the trade-off between the goodness of fit and the simplicity of the model. DNA: genomic DNA fragment coverage; GC: GC-bias; MAP: mappability, FOLD: folding free energy; AIC: Akaike information criterion; BIC: Bayesian information criterion. Supplementary Figure S4. (Shadow figure of Fig. 3) Fitting of various STARR-seq datasets using three distribution models: negative binomial, binomial, and Poisson. Datasets included (A) K562, (B) HeLa-S3 untreated WG-STARR-seq (Muerdter 2018), (C) GM12878 HiDRA ATAC-STARR-seq (Wang 2018), and (D) K562 Cap-STARR-seq (Rathert 2015). Supplementary Figure S5. Q-Q plots of the P value distribution. (A) HepG2 and (B) K562. The red line is a reference line where the expected P values match the observed ones. Supplementary Figure S6. Correlation of RNA fragment coverage between replicates for (A) HepG2 or (B) K562 cell lines. Pairwise coverage was calculated using 1000 bp bins across the genome. The X- and Y-axis represent the natural log of fragment counts. A pseudo count of 1 was added to the fragment counts for plotting only. Bins with abnormally high fragment counts were removed to avoid inflation of the Pearson correlation. We used the median absolute deviation method with a scaling factor of 200 to filter extremely large deviations from the median. Supplementary Figure S7. Comparison of peaks called from subsamples of the original STARR-seq library, highlighting the robustness of STARRPeaker using (A) the whole dataset and (B) 5000 subset of peaks. Supplementary Figure S8. Orientation biases analysis for (A-B) HepG2 or (C-D) K562 cell lines. The ratio between forward and reverse stranded fragments was tested for statistical significance using a binomial test. Orange dots represent peaks with significant strand bias (FDR q-value

Additional file 1: Figure S1. Quality assessment and chromatin-based classification of STARR-seq (lacking) loci. Figure S2. TCF3 and ZIC3 are associated with STARR-seq peaks induced in 2iL- and SLESCs respectively. Figure S3. C2-loci are active STARR-seq regions due to the absence of DNA methylation and repressive histone modifications. Figure S4. P53-bound STARR-seq loci are characterized by low chromatin accessibility and are categorized into active (class C1) and inactive enhancers (class C2) based on the presence/absence of active histone modifications.

The identification of transcriptional enhancers and the quantitative assessment of enhancer activities is essential to understanding how regulatory information for gene expression is encoded in animal and human genomes. Further, it is key to understanding how sequence variants affect enhancer function. STARR-seq enables the direct and quantitative assessment of enhancer activity for millions of candidate sequences of arbitrary length and origin in parallel, allowing the screening of entire genomes and the establishment of genome-wide enhancer activity maps. In STARR-seq, the candidate sequences are cloned downstream of the core promoter into a reporter gene's transcription unit (i.e., the 3' UTR). Candidates that function as active enhancers lead to the transcription of reporter mRNAs that harbor the candidates' sequences. This direct coupling of enhancer sequence and enhancer activity in cis enables the straightforward and efficient cloning of complex candidate libraries and the assessment of enhancer activities of millions of candidates in parallel by quantifying the reporter mRNAs by deep sequencing. This article describes how to create focused and genome-wide human STARR-seq libraries and how to perform STARR-seq screens in mammalian cells, and also describes a novel STARR-seq variant (UMI-STARR-seq) that allows the accurate counting of reporter mRNAs for STARR-seq libraries of low complexity. © 2019 The Authors. Basic Protocol 1: STARR-seq plasmid library cloning Basic Protocol 2: Mammalian STARR-seq screening protocol Alternate Protocol: UMI-STARR-seq screening protocol-unique molecular identifier integration Support Protocol: Transfection of human cells using the MaxCyte STX scalable transfection system.

Additional file 10. Review history.

Differential gene expression is the basis for cell type diversity in multicellular organisms and the driving force of development and differentiation. It is achieved by cell type-specific transcriptional enhancers, which are genomic DNA sequences that activate the transcription of their target genes. Their identification and characterization is fundamental to our understanding of gene regulation. Features that are associated with enhancer activity, such as regulatory factor binding or histone modifications can predict the location of enhancers. Nonetheless, enhancer activity can only be assessed by transcriptional reporter assays. Over the past years massively parallel reporter assays have been developed for large scale testing of enhancers. In this review we focus on the principles and applications of STARR-seq, a functional assay that quantifies enhancer strengths in complex candidate libraries and thus allows activity-based enhancer identification in entire genomes. We explain how STARR-seq works, discuss current uses and give an outlook to future applications.

Background. By SNP genotyping and RNA sequencing of 471 normal prostate samples, we recently created a prostate tissue-based eQTL dataset and identified significant eQTL signals at 51 prostate cancer risk loci. To functionally characterize these risk SNPs, we developed a massively parallel sequencing technology to screen SNPs for their allele-dependent protein binding differences. We combined this technology (called SNPs-seq) with another high throughput assay (called STARR-seq) to screen the risk loci with significant prostate-specific eQTL signals. Methods. To select candidate functional SNPs in eQTL regions, we took advantage of existing epigenomic datasets and available tools including ENCODE, HaploReg, and Regulome. For all selected SNPs, we first made allele-specific double-strand oligos and performed DNA-protein binding assays. We then performed sequencing analysis on the protein-bound DNA oligos and determined allele-specific protein binding differences. To evaluate reproducibility of SNPs-seq, we performed each assay in duplicates. We cloned SNPs-seq screened SNP regions showing allele-specific protein binding differences into the STARR-seq vector to further determine allele-specific enhancer activities. Finally, we performed EMSA and luciferase reporter assays to validate a set of promising candidate SNPs. Results. From 51 risk loci with strong eQTL signals, we selected 374 SNPs with strong indication of regulatory potential, as evidenced by overlapping with epigenomic marks. When comparing technical duplicates, sequence read counts from the SNPs-seq showed significant correlation with r2>=0.99. By normalizing input controls, we found 101 of the 374 SNPs showing significant allelic protein binding differences (>=1.5-fold binding difference between variant and reference alleles). Interestingly, three published functional SNPs (rs12769019, rs10993994, and rs4907792) were also among the significant SNPs, validating SNPs-seq as functional SNP screening tool. To further validate the candidate SNPs from SNPs-seq, we applied STARR-seq and tested the 101 SNPs-containing sequences (371-686bp) in LNCaP cell line under androgen treatment. This analysis revealed 11 SNPs that not only demonstrated enhancer/repressor activity but also functioned with allelic differences. EMSA and luciferase reporter assays confirmed 6 SNPs with allele-dependent enhancer/repressor activity. Conclusions. We developed a high throughput sequencing-based technology to screen large number candidate SNPs for their allelic protein binding differences. The SNPs-seq coupled with STARR-seq will provide a powerful strategy for functionally characterizing risk loci in prostate cancer and other common diseases. Further understanding genetic role of prostate cancer etiology may facilitate the translation of population-based discovery into biological mechanisms and eventually benefit clinical practice. Citation Format: Peng Zhang, Jing Zhu, Sufyan Suleman, Yong-Chen Guo, Mei-Jun Du, Li-Dong Wang, Gong-Hong Wei, Liang Wang. Functional characterization of prostate cancer risk loci by SNPs-seq and STARR-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1280. doi:10.1158/1538-7445.AM2017-1280

Almost all regulation of gene expression in eukaryotic genomes is mediated by the action of distant non-coding transcriptional enhancers upon proximal gene promoters. Enhancer locations cannot be accurately predicted bioinformatically because of the absence of a defined sequence code, and thus functional assays are required for their direct detection. Here we used a massively parallel reporter assay, Self-Transcribing Active Regulatory Region sequencing (STARR-seq), to generate the first comprehensive genome-wide map of enhancers in Anopheles coluzzii, a major African malaria vector in the Gambiae species complex. The screen was carried out by transfecting reporter libraries created from the genomic DNA of 60 wild A. coluzzii from Burkina Faso into A. coluzzii 4a3A cells, in order to functionally query enhancer activity of the natural population within the homologous cellular context. We report a catalog of 3,288 active genomic enhancers that were significant across three biological replicates, 74% of them located in intergenic and intronic regions. The STARR-seq enhancer screen is chromatin-free and thus detects inherent activity of a comprehensive catalog of enhancers that may be restricted in vivo to specific cell types or developmental stages. Testing of a validation panel of enhancer candidates using manual luciferase assays confirmed enhancer function in 26 of 28 (93%) of the candidates over a wide dynamic range of activity from two to at least 16-fold activity above baseline. The enhancers occupy only 0.7% of the genome, and display distinct composition features. The enhancer compartment is significantly enriched for 15 transcription factor binding site signatures, and displays divergence for specific dinucleotide repeats, as compared to matched non-enhancer genomic controls. The genome-wide catalog of A. coluzzii enhancers is publicly available in a simple searchable graphic format. This enhancer catalogue will be valuable in linking genetic and phenotypic variation, in identifying regulatory elements that could be employed in vector manipulation, and in better targeting of chromosome editing to minimize extraneous regulation influences on the introduced sequences.Importance: Understanding the role of the non-coding regulatory genome in complex disease phenotypes is essential, but even in well-characterized model organisms, identification of regulatory regions within the vast non-coding genome remains a challenge. We used a large-scale assay to generate a genome wide map of transcriptional enhancers. Such a catalogue for the important malaria vector, Anopheles coluzzii, will be an important research tool as the role of non-coding regulatory variation in differential susceptibility to malaria infection is explored and as a public resource for research on this important insect vector of disease.

Enhancers represent non-coding regulatory regions of the genome located distantly from their target genes. They regulate gene expression programs in a context-specific manner via interacting with promoters of one or more target genes and are generally associated with transcription factor binding sites and epi(genomic)/chromatin features, such as regions of chromatin accessibility and histone modifications. The enhancers are difficult to identify due to the modularity of their associated features. Although enhancers have been studied extensively in human and animals, only a handful of them has been identified in few plant species till date due to non-availability of plant-specific experimental and computational approaches for enhancer identification. Being an important regulatory component of the genome, enhancers represent potential targets for engineering agronomic traits, including salinity stress tolerance in plants. Here, we provide a review of the available experimental and computational approaches along with the associated sequence and chromatin/epigenetic features for the discovery of enhancers in plants. In addition, we provide insights into the challenges and future prospects of enhancer research in plant biology with emphasis on potential applications in engineering salinity stress tolerance in crop plants.

Genetic factors underlying coronary artery disease (CAD) have been widely studied using genome-wide association studies (GWASs). However, the functional understanding of the CAD loci has been limited by the fact that a majority of GWAS variants are located within non-coding regions with no functional role. High cholesterol and dysregulation of the liver metabolism such as non-alcoholic fatty liver disease confer an increased risk of CAD. Here, we studied the function of non-coding single-nucleotide polymorphisms in CAD GWAS loci located within liver-specific enhancer elements by identifying their potential target genes using liver cis-eQTL analysis and promoter Capture Hi-C in HepG2 cells. Altogether, 734 target genes were identified of which 121 exhibited correlations to liver-related traits. To identify potentially causal regulatory SNPs, the allele-specific enhancer activity was analyzed by (1) sequence-based computational predictions, (2) quantification of allele-specific transcription factor binding, and (3) STARR-seq massively parallel reporter assay. Altogether, our analysis identified 1,277 unique SNPs that display allele-specific regulatory activity. Among these, susceptibility enhancers near important cholesterol homeostasis genes (APOB, APOC1, APOE, and LIPA) were identified, suggesting that altered gene regulatory activity could represent another way by which genetic variation regulates serum lipoprotein levels. Using CRISPR-based perturbation, we demonstrate how the deletion/activation of a single enhancer leads to changes in the expression of many target genes located in a shared chromatin interaction domain. Our integrative genomics approach represents a comprehensive effort in identifying putative causal regulatory regions and target genes that could predispose to clinical manifestation of CAD by affecting liver function.

Additional file 8. Review history.

Rationale: Genome-wide association studies have identified a large number of common variants (single-nucleotide polymorphisms) associated with atrial fibrillation (AF). These variants are located mainly in noncoding regions of the genome and likely include variants that modulate the function of transcriptional regulatory elements (REs) such as enhancers. However, the actual REs modulated by variants and the target genes of such REs remain to be identified. Thus, the biological mechanisms by which genetic variation promotes AF has thus far remained largely unexplored. Objective: To identify REs in genome-wide association study loci that are influenced by AF-associated variants. Methods and Results: We screened 2.45 Mbp of human genomic DNA containing 12 strongly AF-associated loci for RE activity using self-transcribing active regulatory region sequencing and a recently generated monoclonal line of conditionally immortalized rat atrial myocytes. We identified 444 potential REs, 55 of which contain AF-associated variants ( P −8 ). Subsequently, using an adaptation of the self-transcribing active regulatory region sequencing approach, we identified 24 variant REs with allele-specific regulatory activity. By mining available chromatin conformation data, the possible target genes of these REs were mapped. To define the physiological function and target genes of such REs, we deleted the orthologue of an RE containing noncoding variants in the Hcn4 (potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel 4) locus of the mouse genome. Mice heterozygous for the RE deletion showed bradycardia, sinus node dysfunction, and selective loss of Hcn4 expression. Conclusions: We have identified REs at multiple genetic loci for AF and found that loss of an RE at the HCN4 locus results in sinus node dysfunction and reduced gene expression. Our approach can be broadly applied to facilitate the identification of human disease-relevant REs and target genes at cardiovascular genome-wide association studies loci.

Hematopoietic stem cell gene therapy is a promising approach for treating disorders of the hematopoietic system. Identifying combinations of cis-regulatory elements that do not impede packaging or transduction efficiency when included in lentiviral vectors has proven challenging. In this study, we deploy LV-MPRA (lentiviral vector-based, massively parallel reporter assay), an approach that simultaneously analyzes thousands of synthetic DNA fragments in parallel to identify sequence-intrinsic and lineage-specific enhancer function at near-base-pair resolution. We demonstrate the power of LV-MPRA in elucidating the boundaries of previously unknown intrinsic enhancer sequences of the human β-globin locus control region. Our approach facilitated the rapid assembly of novel therapeutic βAS3-globin lentiviral vectors harboring strong lineage-specific recombinant control elements capable of correcting a mouse model of sickle cell disease. LV-MPRA can be used to map any genomic locus for enhancer activity and facilitates the rapid development of therapeutic vectors for treating disorders of the hematopoietic system or other specific tissues and cell types.

Summary Enhancers are genetic elements that regulate spatiotemporal gene expression. Enhancer function requires transcription factor (TF) binding and correlates with histone modifications. However, the extent to which TF binding and histone modifications functionally define active enhancers remains unclear. Here, we combine chromatin immunoprecipitation with a massively parallel reporter assay (ChIP-STARR-seq) to identify functional enhancers in human embryonic stem cells (ESCs) genome-wide in a quantitative unbiased manner. Although active enhancers associate with TFs, only a minority of regions marked by NANOG, OCT4, H3K27ac, and H3K4me1 function as enhancers, with activity markedly changing under naive versus primed culture conditions. We identify an enhancer set associated with functions extending to non-ESC-specific processes. Moreover, although transposable elements associate with putative enhancers, only some exhibit activity. Similarly, within super-enhancers, large tracts are non-functional, with activity restricted to small sub-domains. This catalog of validated enhancers provides a valuable resource for further functional dissection of the regulatory genome., Graphical Abstract, Highlights • Massively parallel reporter assay assessed over 350,000 genome regions • ChIP-STARR-seq catalogs functional enhancers in primed and naive hESCs • Identification of transcription factors and transposable elements linked to enhancers • ChIP-STARR-seq dissects super-enhancers into small functional units, Barakat et al. use a combination of chromatin immunoprecipitation and a massively parallel reporter assay to identify functional enhancers in primed and naive human embryonic stem cells. This genome-wide catalog of validated enhancers provides a valuable resource for the further dissection of the regulatory genome.

Molecular Biology pertains to the molecular basis of the regulation of biomolecular processes in the cell, e.g. gene expression or the genome-wide localization of DNA-associated proteins. These molecular quantities are routinely measured by Next Generation Sequencing (NGS)-based tech- niques due to their genome-wide scalability and cost-efficiency. In order to discern background- regions from genomic loci that harbor a biological relevant signal, i.e. difference calling, the NGS measurements need to be corrected for technical biases with the help of a control, i.e. nor- malization. However, the normalization itself requires the knowledge of background regions and, consequently, difference calling and normalization are inseparable. Here, this problem is solved by the data-driven “normR” framework which models the inter- dependency of NGS mea- surements in background- and signal-regions as a multinomial sampling trial with a binomial mixture model. The robust normR normalization accounts for the effect of signal on the overall measurement statistic by modeling treatment and control simultaneously. In this thesis, I used normR in three studies concerning the inference of DNA-protein binding from ChIP-seq data. Firstly, the two-component “enrichR” model is shown to achieve a more sensitive enrichment calling (AUC≥0.93) than six competitor methods (AUC≤0.86) in low, e.g. H3K36me3, and high, e.g. H3K4me3, signal-to- noise ratio (S/N) ChIP-seq data. enrichR’s enrichment calls augment the resolution and comprehensiveness of chromatin segmentations by chromHMM and its normal- ization improves on present in silico and in vitro ChIP-seq normalization methods. Secondly, the three-component “regimeR” model dissects enrichment into two unprecedented regimes of dif- ferent signal levels. A regimeR-based analysis identified two distinct facultative and constitutive heterochromatic enrichment regimes in H3K27me3 and H3K9me3 ChIP-seq data, respectively. The identified peak regions (high enrichment) resemble nucleation sites for heterochromatin embedded in regions of broad (low) enrichment. Lastly, the three-component “diffR” model calls conditional differences in ChIP-seq enrichment between two conditions. The diffR calls in low (H3K27me3) and high (H3K4me3) S/N ChIP-seq data are confirmed by a systematic compari- son to four difference callers. Overall, normR represents a robust and versatile framework for the comprehensive analysis of ChIP-seq data, yet, it can be readily applied to other NGS-based experiments like ATAC- seq, STARR-seq or RNA-seq., Die Molekulare Biologie studiert die molekulare Basis der Regulierung von biomolekularen Pro- zessen wie der Genexpression und der genomweiten Lokalisation von DNS-bindenden Protei- nen. Die molekularen Größen werden mittels Next Generation Sequencing(NGS)-basierten Me- thoden gemessen, da diese genomweit skalierbar und kosteneffizient sind. Um Hintergrundre- gionen von genomischen Regionen mit einem biologisch relevanten Signal zu unterscheiden (Differenzenbestimmung) müssen technische Verzerrungen in den NGS Messungen mit Hilfe einer Kontrolle normalisiert werden. Jedoch benötigt eine korrekte Normalisierung die Identi- tät der Hintergrundregionen und, somit, sind Differenzenbestimmung und Normalisierung un- trennbar miteinander verbunden. Dieses Problem wird mit dem vorgestellten datenbasierten “normR” Modell gelöst, welches die Wechselbeziehung zwischen Zahlenwerten in Hintergrund- und Signalregionen als eine binomiale Mischverteilung modelliert. Die robuste Normalisierung von normR berücksichtigt durch gleichzeitige Modellierung von Experiment und Kontrolle den Einfluss des Signals auf die Messstatistik. In dieser Arbeit wurde normR in drei Analysen von ChIP-seq Daten verwendet um DNS-Bindestellen von Proteinen zu identifizieren. 1. Das “en- richR” Modell erreicht mit einer Mischverteilung aus zwei Komponenten eine Differenzenbe- stimmung, die sensitiver ist (AUC≥0.93) als bei sechs anderen Programmen (AUC≤0.86). Die identifizierten differentiellen Regionen erweitern die Auflösung und den Umfang von Chroma- tinsegmentierungen durch das chromHMM Programm. Die Normalisierung von enrichR ist bes- ser als bekannte in vitro und in silico Normalisierungsansätze. 2. Das “regimeR” Modell mit drei Komponenten teilt die vom ChIP angereicherten Regionen in zwei Klassen mit unterschiedli- cher Signalintensität. Eine Analyse mit regimeR identifiziert zwei Klassen von Anreicherung in fakultativem und konstitutivem Heterochromatin in H3K27me3 and H3K9me3 ChIP-seq Daten- sätzen. Die Regionen mit hoher Signalintensität sind flankiert von breiten Regionen mit nied- rigem Signal und könnten Keimstellen des Heterochromatins darstellen. 3. Das “diffR” Modell identifiziert Unterschiede zwischen ChIP-seq Messungen in zwei zellulären Bedingungen. Die Ergebnisse von diffR wurden mittels eines systematischen Vergleichs zu vier anderen ChIP-seq Differenzbestimmungsprogrammen validiert. normR ist ein robustes und vielseitiges Programm zur umfassenden Analyse von ChIP-seq Daten und vermag in Zukunft eine sensitive Analyse von anderen NGS Datensätzen wie ATAC-seq, STARR-seq und RNA-seq zu ermöglichen.

Enhancers are genetic elements that regulate spatiotemporal gene expression. Enhancer function requires transcription factor (TF) binding and correlates with histone modifications. However, the extent to which TF binding and histone modifications functionally define active enhancers remains unclear. Here, we combine chromatin immunoprecipitation with a massively parallel reporter assay (ChIP-STARR-seq) to identify functional enhancers in human embryonic stem cells (ESCs) genome-wide in a quantitative unbiased manner. Although active enhancers associate with TFs, only a minority of regions marked by NANOG, OCT4, H3K27ac, and H3K4me1 function as enhancers, with activity markedly changing under naive versus primed culture conditions. We identify an enhancer set associated with functions extending to non-ESC-specific processes. Moreover, although transposable elements associate with putative enhancers, only some exhibit activity. Similarly, within super-enhancers, large tracts are non-functional, with activity restricted to small sub-domains. This catalog of validated enhancers provides a valuable resource for further functional dissection of the regulatory genome.

Genome-wide association studies (GWASs) have identified many genetic risk factors for CKD. However, linking common variants to genes that are causal for CKD etiology remains challenging. By adapting self-transcribing active regulatory region sequencing, we evaluated the effect of genetic variation on DNA regulatory elements (DREs). Variants in linkage with the CKD-associated single-nucleotide polymorphism rs11959928 were shown to affect DRE function, illustrating that genes regulated by DREs colocalizing with CKD-associated variation can be dysregulated and therefore, considered as CKD candidate genes. To identify target genes of these DREs, we used circular chromosome conformation capture (4C) sequencing on glomerular endothelial cells and renal tubular epithelial cells. Our 4C analyses revealed interactions of CKD-associated susceptibility regions with the transcriptional start sites of 304 target genes. Overlap with multiple databases confirmed that many of these target genes are involved in kidney homeostasis. Expression quantitative trait loci analysis revealed that mRNA levels of many target genes are genotype dependent. Pathway analyses showed that target genes were enriched in processes crucial for renal function, identifying dysregulated geranylgeranyl diphosphate biosynthesis as a potential disease mechanism. Overall, our data annotated multiple genes to previously reported CKD-associated single-nucleotide polymorphisms and provided evidence for interaction between these loci and target genes. This pipeline provides a novel technique for hypothesis generation and complements classic GWAS interpretation. Future studies are required to specify the implications of our dataset and further reveal the complex roles that common variants have in complex diseases, such as CKD.