STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Mapping putative enhancers in mouse oocytes and early embryos reveals TCF3/12 as key folliculogenesis regulators

Dynamic epigenomic reprogramming occurs during mammalian oocyte maturation and early development. However, the underlying transcription circuitry remains poorly characterized. By mapping cis-regulatory elements using H3K27ac, we identified putative enhancers in mouse oocytes and early embryos distinct from those in adult tissues, enabling global transitions of regulatory landscapes around fertilization and implantation. Gene deserts harbour prevalent putative enhancers in fully grown oocytes linked to oocyte-specific genes and repeat activation. Embryo-specific enhancers are primed before zygotic genome activation and are restricted by oocyte-inherited H3K27me3. Putative enhancers in oocytes often manifest H3K4me3, bidirectional transcription, Pol II binding and can drive transcription in STARR-seq and a reporter assay. Finally, motif analysis of these elements identified crucial regulators of oogenesis, TCF3 and TCF12, the deficiency of which impairs activation of key oocyte genes and folliculogenesis. These data reveal distinctive regulatory landscapes and their interacting transcription factors that underpin the development of mammalian oocytes and early embryos.

CUX1 regulates human hematopoietic stem cell chromatin accessibility via the BAF complex

CUX1 is a homeodomain-containing transcription factor that is essential for the development and differentiation of multiple tissues. CUX1 is recurrently mutated or deleted in cancer, particularly in myeloid malignancies. However, the mechanism by which CUX1 regulates gene expression and differentiation remains poorly understood, creating a barrier to understanding the tumor-suppressive functions of CUX1. Here, we demonstrate that CUX1 directs the BAF chromatin remodeling complex to DNA to increase chromatin accessibility in hematopoietic cells. CUX1 preferentially regulates lineage-specific enhancers, and CUX1 target genes are predictive of cell fate in vivo. These data indicate that CUX1 regulates hematopoietic lineage commitment and homeostasis via pioneer factor activity, and CUX1 deficiency disrupts these processes in stem and progenitor cells, facilitating transformation.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Computationally guided AAV engineering for enhanced gene delivery

Gene delivery vehicles based on adeno-associated viruses (AAVs) are enabling increasing success in human clinical trials, and they offer the promise of treating a broad spectrum of both genetic and non-genetic disorders. However, delivery efficiency and targeting must be improved to enable safe and effective therapies. In recent years, considerable effort has been invested in creating AAV variants with improved delivery, and computational approaches have been increasingly harnessed for AAV engineering. In this review, we discuss how computationally designed AAV libraries are enabling directed evolution. Specifically, we highlight approaches that harness sequences outputted by next-generation sequencing (NGS) coupled with machine learning (ML) to generate new functional AAV capsids and related regulatory elements, pushing the frontier of what vector engineering and gene therapy may achieve.

RepEnTools: an automated repeat enrichment analysis package for ChIP-seq data reveals hUHRF1 Tandem-Tudor domain enrichment in young repeats

BACKGROUND

Repeat elements (REs) play important roles for cell function in health and disease. However, RE enrichment analysis in short-read high-throughput sequencing (HTS) data, such as ChIP-seq, is a challenging task.

RESULTS

Here, we present RepEnTools, a software package for genome-wide RE enrichment analysis of ChIP-seq and similar chromatin pulldown experiments. Our analysis package bundles together various software with carefully chosen and validated settings to provide a complete solution for RE analysis, starting from raw input files to tabular and graphical outputs. RepEnTools implementations are easily accessible even with minimal IT skills (Galaxy/UNIX). To demonstrate the performance of RepEnTools, we analysed chromatin pulldown data by the human UHRF1 TTD protein domain and discovered enrichment of TTD binding on young primate and hominid specific polymorphic repeats (SVA, L1PA1/L1HS) overlapping known enhancers and decorated with H3K4me1-K9me2/3 modifications. We corroborated these new bioinformatic findings with experimental data by qPCR assays using newly developed primate and hominid specific qPCR assays which complement similar research tools. Finally, we analysed mouse UHRF1 ChIP-seq data with RepEnTools and showed that the endogenous mUHRF1 protein colocalizes with H3K4me1-H3K9me3 on promoters of REs which were silenced by UHRF1. These new data suggest a functional role for UHRF1 in silencing of REs that is mediated by TTD binding to the H3K4me1-K9me3 double mark and conserved in two mammalian species.

CONCLUSIONS

RepEnTools improves the previously available programmes for RE enrichment analysis in chromatin pulldown studies by leveraging new tools, enhancing accessibility and adding some key functions. RepEnTools can analyse RE enrichment rapidly, efficiently, and accurately, providing the community with an up-to-date, reliable and accessible tool for this important type of analysis.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

MPRAbase: A Massively Parallel Reporter Assay Database

Massively parallel reporter assays (MPRAs) represent a set of high-throughput technologies that measure the functional effects of thousands of sequences/variants on gene regulatory activity. There are several different variations of MPRA technology and they are used for numerous applications, including regulatory element discovery, variant effect measurement, saturation mutagenesis, synthetic regulatory element generation or characterization of evolutionary gene regulatory differences. Despite their many designs and uses, there is no comprehensive database that incorporates the results of these experiments. To address this, we developed MPRAbase, a manually curated database that currently harbors 129 experiments, encompassing 17,718,677 elements tested across 35 cell types and 4 organisms. The MPRAbase web interface ( http://www.mprabase.com ) serves as a centralized user-friendly repository to download existing MPRA data for independent analysis and is designed with the ability to allow researchers to share their published data for rapid dissemination to the community.

Underlying causes for prevalent false positives and false negatives in STARR-seq data

Self-transcribing active regulatory region sequencing (STARR-seq) and its variants have been widely used to characterize enhancers. However, it has been reported that up to 87% of STARR-seq peaks are located in repressive chromatin and are not functional in the tested cells. While some of the STARR-seq peaks in repressive chromatin 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. Although methods have been proposed to mitigate systematic errors caused by the use of plasmid vectors, the artifacts due to the intrinsic limitations of current STARR-seq methods are still prevalent and the underlying causes are not fully understood. Based on predicted cis-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/tissues with STARR-seq data available, we reveal prevalent false positives and false negatives in STARR-seq peaks generated by major variants of STARR-seq methods and possible underlying causes. Our results will help design strategies to improve STARR-seq methods and interpret the results.

Transposable elements as tissue-specific enhancers in cancers of endodermal lineage

Transposable elements (TE) are repetitive genomic elements that harbor binding sites for human transcription factors (TF). A regulatory role for TEs has been suggested in embryonal development and diseases such as cancer but systematic investigation of their functions has been limited by their widespread silencing in the genome. Here, we utilize unbiased massively parallel reporter assay data using a whole human genome library to identify TEs with functional enhancer activity in two human cancer types of endodermal lineage, colorectal and liver cancers. We show that the identified TE enhancers are characterized by genomic features associated with active enhancers, such as epigenetic marks and TF binding. Importantly, we identify distinct TE subfamilies that function as tissue-specific enhancers, namely MER11- and LTR12-elements in colon and liver cancers, respectively. These elements are bound by distinct TFs in each cell type, and they have predicted associations to differentially expressed genes. In conclusion, these data demonstrate how different cancer types can utilize distinct TEs as tissue-specific enhancers, paving the way for comprehensive understanding of the role of TEs as bona fide enhancers in the cancer genomes.

Integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types

We introduce ChromActivity, a computational framework for predicting and annotating regulatory activity across the genome through integration of multiple epigenomic maps and various functional characterization datasets. ChromActivity generates genomewide predictions of regulatory activity associated with each functional characterization dataset across many cell types based on available epigenomic data. It then for each cell type produces (1) ChromScoreHMM genome annotations based on the combinatorial and spatial patterns within these predictions and (2) ChromScore tracks of overall predicted regulatory activity. ChromActivity provides a resource for analyzing and interpreting the human regulatory genome across diverse cell types.

H4K16ac activates the transcription of transposable elements and contributes to their cis-regulatory function

Mammalian genomes harbor abundant transposable elements (TEs) and their remnants, with numerous epigenetic repression mechanisms enacted to silence TE transcription. However, TEs are upregulated during early development, neuronal lineage, and cancers, although the epigenetic factors contributing to the transcription of TEs have yet to be fully elucidated. Here, we demonstrate that the male-specific lethal (MSL)-complex-mediated histone H4 acetylation at lysine 16 (H4K16ac) is enriched at TEs in human embryonic stem cells (hESCs) and cancer cells. This in turn activates transcription of subsets of full-length long interspersed nuclear elements (LINE1s, L1s) and endogenous retrovirus (ERV) long terminal repeats (LTRs). Furthermore, we show that the H4K16ac-marked L1 and LTR subfamilies display enhancer-like functions and are enriched in genomic locations with chromatin features associated with active enhancers. Importantly, such regions often reside at boundaries of topologically associated domains and loop with genes. CRISPR-based epigenetic perturbation and genetic deletion of L1s reveal that H4K16ac-marked L1s and LTRs regulate the expression of genes in cis. Overall, TEs enriched with H4K16ac contribute to the cis-regulatory landscape at specific genomic locations by maintaining an active chromatin landscape at TEs.

An unbiased AAV-STARR-seq screen revealing the enhancer activity map of genomic regions in the mouse brain in vivo

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.

Challenges and considerations for reproducibility of STARR-seq assays

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.

Leveraging massively parallel reporter assays for evolutionary questions

A long-standing goal of evolutionary biology is to decode how gene regulation contributes to organismal diversity. Doing so is challenging because it is hard to predict function from non-coding sequence and to perform molecular research with non-model taxa. Massively parallel reporter assays (MPRAs) enable the testing of thousands to millions of sequences for regulatory activity simultaneously. Here, we discuss the execution, advantages, and limitations of MPRAs, with a focus on evolutionary questions. We propose solutions for extending MPRAs to rare taxa and those with limited genomic resources, and we underscore MPRA's broad potential for driving genome-scale, functional studies across organisms.

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies

The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

Relative importance of composition structures and biologically meaningful logics in bipartite Boolean models of gene regulation

Boolean networks have been widely used to model gene networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. Alternatively, bipartite Boolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such bipartite models, multiple TFs may simultaneously contribute to gene regulation by forming heteromeric complexes, thus giving rise to composition structures. Since bipartite Boolean models are relatively recent, an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes. Moreover, we present an additional mechanism where composition structures may arise as a result of multiple TFs binding to cis-regulatory regions and provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that though composition structures can severely restrict the number of Boolean functions (BFs) driving a gene, the two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are comparatively more restrictive. Finally, we find that composition structures are highly enriched in real networks, but this enrichment most likely comes from NCFs and RoFs.

Focus on your locus with a massively parallel reporter assay

A growing number of variants associated with risk for neurodevelopmental disorders have been identified by genome-wide association and whole genome sequencing studies. As common risk variants often fall within large haplotype blocks covering long stretches of the noncoding genome, the causal variants within an associated locus are often unknown. Similarly, the effect of rare noncoding risk variants identified by whole genome sequencing on molecular traits is seldom known without functional assays. A massively parallel reporter assay (MPRA) is an assay that can functionally validate thousands of regulatory elements simultaneously using high-throughput sequencing and barcode technology. MPRA has been adapted to various experimental designs that measure gene regulatory effects of genetic variants within cis- and trans-regulatory elements as well as posttranscriptional processes. This review discusses different MPRA designs that have been or could be used in the future to experimentally validate genetic variants associated with neurodevelopmental disorders. Though MPRA has limitations such as it does not model genomic context, this assay can help narrow down the underlying genetic causes of neurodevelopmental disorders by screening thousands of sequences in one experiment. We conclude by describing future directions of this technique such as applications of MPRA for gene-by-environment interactions and pharmacogenetics.

ATAC-STARR-seq reveals transcription factor-bound activators and silencers within chromatin-accessible regions of the human genome

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.

AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain

Enhancers are binding platforms for a diverse array of transcription factors that drive specific expression patterns of tissue- and cell-type-specific genes. Multiple means of assessing non-coding DNA and various chromatin states have proven useful in predicting the presence of enhancer sequences in the genome, but validating the activity of these sequences and finding the organs and developmental stages they are active in is a labor-intensive process. Recent advances in adeno-associated virus (AAV) vectors have enabled the widespread delivery of transgenes to mouse tissues, enabling in vivo enhancer testing without necessitating a transgenic animal. This protocol shows how a reporter construct that expresses EGFP under the control of a minimal promoter, which does not drive significant expression on its own, can be used to study the activity patterns of candidate enhancer sequences in the mouse brain. An AAV-packaged reporter construct is delivered to the mouse brain and incubated for 1-4 weeks, after which the animal is sacrificed, and brain sections are observed under a microscope. EGFP appears in cells in which the tested enhancer is sufficient to initiate gene expression, pinpointing the location and developmental stage in which the enhancer is active in the brain. Standard cloning methods, low-cost AAV packaging, and expanding AAV serotypes and methods for in vivo delivery and standard imaging readout make this an accessible approach for the study of how gene expression is regulated in the brain.

Integration of Count Difference and Curve Similarity in Negative Regulatory Element Detection

Negative regulatory elements (NREs) down-regulate gene expression by inhibiting the activities of promoters or enhancers. The repressing activity of NREs can be measured globally by massively parallel reporter assays (MPRAs). However, most existing algorithms are designed for the statistical detection of positively enriched signals in MPRA datasets. To identify reduced signals in MPRA experiments, we designed a NRE identification program, fast-NR, by integrating the count and graphic features of sequenced reads to detect NREs using datasets generated by experiments of self-transcribing active regulatory region sequencing (STARR-seq). Fast-NR identified hundreds of silencers in human K562 cells that can be validated by independent methods.

A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers

Mounting evidence supports the idea that transcriptional patterns serve as more specific identifiers of active enhancers than histone marks; however, the optimal strategy to identify active enhancers both experimentally and computationally has not been determined. Here, we compared 13 genome-wide RNA sequencing assays in K562 cells and showed that the nuclear run-on followed by cap-selection assay (GRO/PRO-cap) has advantages in eRNA detection and active enhancer identification. We also introduced a tool, Peak Identifier for Nascent Transcript Starts (PINTS), to identify active promoters and enhancers genome-wide and pinpoint the precise location of the 5′ transcription start sites. Finally, we compiled a comprehensive enhancer candidate compendium based on the detected eRNA TSSs available in 120 cell and tissue types that can be accessed at https://pints.yulab.org. With the knowledge of the best available assays and pipelines, this large-scale annotation of candidate enhancers will pave the way for selection and characterization of their functions in a time- and labor-efficient manner in the future.

STL-seq reveals pause-release and termination kinetics for promoter-proximal paused RNA polymerase II transcripts

Despite the critical regulatory function of promoter-proximal pausing, the influence of pausing kinetics on transcriptional control remains an active area of investigation. Here, we present Start-TimeLapse-seq (STL-seq), a method that captures the genome-wide kinetics of short, capped RNA turnover and reveals principles of regulation at the pause site. By measuring the rates of release into elongation and premature termination through the inhibition of pause release, we determine that pause-release rates are highly variable, and most promoter-proximal paused RNA polymerase II molecules prematurely terminate (∼80%). The preferred regulatory mechanism upon a hormonal stimulus (20-hydroxyecdysone) is to influence pause-release rather than termination rates. Transcriptional shutdown occurs concurrently with the induction of promoter-proximal termination under hyperosmotic stress, but paused transcripts from TATA box-containing promoters remain stable, demonstrating an important role for cis-acting DNA elements in pausing. STL-seq dissects the kinetics of pause release and termination, providing an opportunity to identify mechanisms of transcriptional regulation.

Integrative epigenomic and high-throughput functional enhancer profiling reveals determinants of enhancer heterogeneity in gastric cancer

BACKGROUND

Enhancers are distal cis-regulatory elements required for cell-specific gene expression and cell fate determination. In cancer, enhancer variation has been proposed as a major cause of inter-patient heterogeneity-however, most predicted enhancer regions remain to be functionally tested.

METHODS

We analyzed 132 epigenomic histone modification profiles of 18 primary gastric cancer (GC) samples, 18 normal gastric tissues, and 28 GC cell lines using Nano-ChIP-seq technology. We applied Capture-based Self-Transcribing Active Regulatory Region sequencing (CapSTARR-seq) to assess functional enhancer activity. An Activity-by-contact (ABC) model was employed to explore the effects of histone acetylation and CapSTARR-seq levels on enhancer-promoter interactions.

RESULTS

We report a comprehensive catalog of 75,730 recurrent predicted enhancers, the majority of which are GC-associated in vivo (> 50,000) and associated with lower somatic mutation rates inferred by whole-genome sequencing. Applying CapSTARR-seq to the enhancer catalog, we observed significant correlations between CapSTARR-seq functional activity and H3K27ac/H3K4me1 levels. Super-enhancer regions exhibited increased CapSTARR-seq signals compared to regular enhancers, even when decoupled from native chromatin contexture. We show that combining histone modification and CapSTARR-seq functional enhancer data improves the prediction of enhancer-promoter interactions and pinpointing of germline single nucleotide polymorphisms (SNPs), somatic copy number alterations (SCNAs), and trans-acting TFs involved in GC expression. We identified cancer-relevant genes (ING1, ARL4C) whose expression between patients is influenced by enhancer differences in genomic copy number and germline SNPs, and HNF4α as a master trans-acting factor associated with GC enhancer heterogeneity.

CONCLUSIONS

Our results indicate that combining histone modification and functional assay data may provide a more accurate metric to assess enhancer activity than either platform individually, providing insights into the relative contribution of genetic (cis) and regulatory (trans) mechanisms to GC enhancer functional heterogeneity.

A universal framework for detecting cis-regulatory diversity in DNA regulatory regions

High-throughput sequencing-based assays measure different biochemical activities pertaining to gene regulation, genome-wide. These activities include transcription factor (TF)–DNA binding, enhancer activity, open chromatin, and more. A major goal is to understand underlying sequence components, or motifs, that can explain the measured activity. It is usually not one motif but a combination of motifs bound by cooperatively acting proteins that confers activity to such regions. Furthermore, regions can be diverse, governed by different combinations of TFs/motifs. Current approaches do not take into account this issue of combinatorial diversity. We present a new statistical framework, cisDIVERSITY, which models regions as diverse modules characterized by combinations of motifs while simultaneously learning the motifs themselves. Because cisDIVERSITY does not rely on knowledge of motifs, modules, cell type, or organism, it is general enough to be applied to regions reported by most high-throughput assays. For example, in enhancer predictions resulting from different assays—GRO-cap, STARR-seq, and those measuring chromatin structure—cisDIVERSITY discovers distinct modules and combinations of TF binding sites, some specific to the assay. From protein–DNA binding data, cisDIVERSITY identifies potential cofactors of the profiled TF, whereas from ATAC-seq data, it identifies tissue-specific regulatory modules. Finally, analysis of single-cell ATAC-seq data suggests that regions open in one cell-state encode information about future states, with certain modules staying open and others closing down in the next time point.

DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays

Motivation

Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping.

Results

Our DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization.

Availability and implementation

DECODE source code and pre-processing scripts are available at decode.gersteinlab.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Annotating the Insect Regulatory Genome

An ever-growing number of insect genomes is being sequenced across the evolutionary spectrum. Comprehensive annotation of not only genes but also regulatory regions is critical for reaping the full benefits of this sequencing. Driven by developments in sequencing technologies and in both empirical and computational discovery strategies, the past few decades have witnessed dramatic progress in our ability to identify cis-regulatory modules (CRMs), sequences such as enhancers that play a major role in regulating transcription. Nevertheless, providing a timely and comprehensive regulatory annotation of newly sequenced insect genomes is an ongoing challenge. We review here the methods being used to identify CRMs in both model and non-model insect species, and focus on two tools that we have developed, REDfly and SCRMshaw. These resources can be paired together in a powerful combination to facilitate insect regulatory annotation over a broad range of species, with an accuracy equal to or better than that of other state-of-the-art methods.

Correcting signal biases and detecting regulatory elements in STARR-seq data

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.

Filtering the Junk: Assigning Function to the Mosquito Non-Coding Genome

The portion of the mosquito genome that does not code for proteins contains regulatory elements that likely underlie variation for important phenotypes including resistance and susceptibility to infection with arboviruses and Apicomplexan parasites. Filtering the non-coding genome to uncover these functional elements is an expanding area of research, though identification of non-coding regulatory elements is challenging due to the lack of an amino acid-like code for the non-coding genome and a lack of sequence conservation across species. This review focuses on three types of non-coding regulatory elements: (1) microRNAs (miRNAs), (2) long non-coding RNAs (lncRNAs), and (3) enhancers, and summarizes current advances in technical and analytical approaches for measurement of each of these elements on a genome-wide scale. The review also summarizes and highlights novel findings following application of these techniques in mosquito-borne disease research. Looking beyond the protein-coding genome is essential for understanding the complexities that underlie differential gene expression in response to arboviral or parasite infection in mosquito disease vectors. A comprehensive understanding of the regulation of gene and protein expression will inform transgenic and other vector control methods rooted in naturally segregating genetic variation.

Parallel functional testing identifies enhancers active in early postnatal mouse brain

Enhancers are cis-regulatory elements that play critical regulatory roles in modulating developmental transcription programs and driving cell-type-specific and context-dependent gene expression in the brain. The development of massively parallel reporter assays (MPRAs) has enabled high-throughput functional screening of candidate DNA sequences for enhancer activity. Tissue-specific screening of in vivo enhancer function at scale has the potential to greatly expand our understanding of the role of non-coding sequences in development, evolution, and disease. Here, we adapted a self-transcribing regulatory element MPRA strategy for delivery to early postnatal mouse brain via recombinant adeno-associated virus (rAAV). We identified and validated putative enhancers capable of driving reporter gene expression in mouse forebrain, including regulatory elements within an intronic CACNA1C linkage disequilibrium block associated with risk in neuropsychiatric disorder genetic studies. Paired screening and single enhancer in vivo functional testing, as we show here, represents a powerful approach towards characterizing regulatory activity of enhancers and understanding how enhancer sequences organize gene expression in the brain.

Massively Parallel Reporter Assays: Defining Functional Psychiatric Genetic Variants Across Biological Contexts

Neuropsychiatric phenotypes have long been known to be influenced by heritable risk factors, directly confirmed by the past decade of genetic studies that have revealed specific genetic variants enriched in disease cohorts. However, the initial hope that a small set of genes would be responsible for a given disorder proved false. The more complex reality is that a given disorder may be influenced by myriad small-effect noncoding variants and/or by rare but severe coding variants, many de novo. Noncoding genomic sequences-for which molecular functions cannot usually be inferred-harbor a large portion of these variants, creating a substantial barrier to understanding higher-order molecular and biological systems of disease. Fortunately, novel genetic technologies-scalable oligonucleotide synthesis, RNA sequencing, and CRISPR (clustered regularly interspaced short palindromic repeats)-have opened novel avenues to experimentally identify biologically significant variants en masse. Massively parallel reporter assays (MPRAs) are an especially versatile technique resulting from such innovations. MPRAs are powerful molecular genetics tools that can be used to screen thousands of untranscribed or untranslated sequences and their variants for functional effects in a single experiment. This approach, though underutilized in psychiatric genetics, has several useful features for the field. We review methods for assaying putatively functional genetic variants and regions, emphasizing MPRAs and the opportunities they hold for dissection of psychiatric polygenicity. We discuss literature applying functional assays in neurogenetics, highlighting strengths, caveats, and design considerations-especially regarding disease-relevant variables (cell type, neurodevelopment, and sex), and we ultimately propose applications of MPRA to both computational and experimental neurogenetics of polygenic disease risk.

Transcriptionally active enhancers in human cancer cells

The growth of human cancer cells is driven by aberrant enhancer and gene transcription activity. Here, we use transient transcriptome sequencing (TT-seq) to map thousands of transcriptionally active putative enhancers in fourteen human cancer cell lines covering seven types of cancer. These enhancers were associated with cell type-specific gene expression, enriched for genetic variants that predispose to cancer, and included functionally verified enhancers. Enhancer-promoter (E-P) pairing by correlation of transcription activity revealed ~ 40,000 putative E-P pairs, which were depleted for housekeeping genes and enriched for transcription factors, cancer-associated genes, and 3D conformational proximity. The cell type specificity and transcription activity of target genes increased with the number of paired putative enhancers. Our results represent a rich resource for future studies of gene regulation by enhancers and their role in driving cancerous cell growth.