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Noncoding DNA is central to our understanding of human gene regulation and complex diseases1,2, and measuring the evolutionary sequence constraint can establish the functional relevance of putative regulatory elements in the human genome3–9. Identifying the genomic elements that have become constrained specifically in primates has been hampered by the faster evolution of noncoding DNA compared to protein-coding DNA10, the relatively short timescales separating primate species11, and the previously limited availability of whole-genome sequences12. Here we construct a whole-genome alignment of 239 species, representing nearly half of all extant species in the primate order. Using this resource, we identified human regulatory elements that are under selective constraint across primates and other mammals at a 5% false discovery rate. We detected 111,318 DNase I hypersensitivity sites and 267,410 transcription factor binding sites that are constrained specifically in primates but not across other placental mammals and validate their cis-regulatory effects on gene expression. These regulatory elements are enriched for human genetic variants that affect gene expression and complex traits and diseases. Our results highlight the important role of recent evolution in regulatory sequence elements differentiating primates, including humans, from other placental mammals.

Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context1–5. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.

High-throughput experimental platforms have revolutionized the ability to profile biochemical and functional properties of biological sequences such as DNA, RNA and proteins. By collating several data modalities with customizable tracks rendered using intuitive visualizations, genome browsers enable an interactive and interpretable exploration of diverse types of genome profiling experiments and derived annotations. However, existing genome browser tracks are not well suited for intuitive visualization of high-resolution DNA sequence features such as transcription factor motifs. Typically, motif instances in regulatory DNA sequences are visualized as BED-based annotation tracks, which highlight the genomic coordinates of the motif instances but do not expose their specific sequences. Instead, a genome sequence track needs to be cross-referenced with the BED track to identify sequences of motif hits. Even so, quantitative information about the motif instances such as affinity or conservation as well as differences in base resolution from the consensus motif are not immediately apparent. This makes interpretation slow and challenging. This problem is compounded when analyzing several cellular states and/or molecular readouts (such as ATAC-seq and ChIP–seq) simultaneously, as coordinates of enriched regions (peaks) and the set of active transcription factor motifs vary across cell states.

During mammalian development, differences in chromatin state coincide with cellular differentiation and reflect changes in the gene regulatory landscape1. In the developing brain, cell fate specification and topographic identity are important for defining cell identity2and confer selective vulnerabilities to neurodevelopmental disorders3. Here, to identify cell-type-specific chromatin accessibility patterns in the developing human brain, we used a single-cell assay for transposase accessibility by sequencing (scATAC-seq) in primary tissue samples from the human forebrain. We applied unbiased analyses to identify genomic loci that undergo extensive cell-type- and brain-region-specific changes in accessibility during neurogenesis, and an integrative analysis to predict cell-type-specific candidate regulatory elements. We found that cerebral organoids recapitulate most putative cell-type-specific enhancer accessibility patterns but lack many cell-type-specific open chromatin regions that are found in vivo. Systematic comparison of chromatin accessibility across brain regions revealed unexpected diversity among neural progenitor cells in the cerebral cortex and implicated retinoic acid signalling in the specification of neuronal lineage identity in the prefrontal cortex. Together, our results reveal the important contribution of chromatin state to the emerging patterns of cell type diversity and cell fate specification and provide a blueprint for evaluating the fidelity and robustness of cerebral organoids as a model for cortical development.

Interactive graphical genome browsers are essential tools in genomics, but they do not contain all the recent genome assemblies. We create Genome Archive (GenArk) collection of UCSC Genome Browsers from NCBI assemblies. Built on our established track hub system, this enables fast visualization of annotations. Assemblies come with gene models, repeat masks, BLAT, and in silico PCR. Users can add annotations via track hubs and custom tracks. We can bulk-import third-party resources, demonstrated with TOGA and Ensembl gene models for hundreds of assemblies.

The human cortex comprises diverse cell types that emerge from an initially uniform neuroepithelium that gives rise to radial glia, the neural stem cells of the cortex. To characterize the earliest stages of human brain development, we performed single-cell RNA-sequencing across regions of the developing human brain, including the telencephalon, diencephalon, midbrain, hindbrain and cerebellum. We identify nine progenitor populations physically proximal to the telencephalon, suggesting more heterogeneity than previously described, including a highly prevalent mesenchymal-like population that disappears once neurogenesis begins. Comparison of human and mouse progenitor populations at corresponding stages identifies two progenitor clusters that are enriched in the early stages of human cortical development. We also find that organoid systems display low fidelity to neuroepithelial and early radial glia cell types, but improve as neurogenesis progresses. Overall, we provide a comprehensive molecular and spatial atlas of early stages of human brain and cortical development.

The UCSC SARS-CoV-2 Genome Browser (https://genome.ucsc.edu/covid19.html) is an adaptation of our popular genome-browser visualization tool for this virus, containing many annotation tracks and new features, including conservation with similar viruses, immune epitopes, RT–PCR and sequencing primers and CRISPR guides. We invite all investigators to contribute to this resource to accelerate research and development activities globally.

Cortical organoids are self-organizing three-dimensional cultures that model features of the developing human cerebral cortex1,2. However, the fidelity of organoid models remains unclear3–5. Here we analyse the transcriptomes of individual primary human cortical cells from different developmental periods and cortical areas. We find that cortical development is characterized by progenitor maturation trajectories, the emergence of diverse cell subtypes and areal specification of newborn neurons. By contrast, organoids contain broad cell classes, but do not recapitulate distinct cellular subtype identities and appropriate progenitor maturation. Although the molecular signatures of cortical areas emerge in organoid neurons, they are not spatially segregated. Organoids also ectopically activate cellular stress pathways, which impairs cell-type specification. However, organoid stress and subtype defects are alleviated by transplantation into the mouse cortex. Together, these datasets and analytical tools provide a framework for evaluating and improving the accuracy of cortical organoids as models of human brain development.

Multiple sclerosis (MS) is a neuroinflammatory disease with a relapsing–remitting disease course at early stages, distinct lesion characteristics in cortical grey versus subcortical white matter and neurodegeneration at chronic stages. Here we used single-nucleus RNA sequencing to assess changes in expression in multiple cell lineages in MS lesions and validated the results using multiplex in situ hybridization. We found selective vulnerability and loss of excitatory CUX2-expressing projection neurons in upper-cortical layers underlying meningeal inflammation; such MS neuron populations exhibited upregulation of stress pathway genes and long non-coding RNAs. Signatures of stressed oligodendrocytes, reactive astrocytes and activated microglia mapped most strongly to the rim of MS plaques. Notably, single-nucleus RNA sequencing identified phagocytosing microglia and/or macrophages by their ingestion and perinuclear import of myelin transcripts, confirmed by functional mouse and human culture assays. Our findings indicate lineage- and region-specific transcriptomic changes associated with selective cortical neuron damage and glial activation contributing to progression of MS lesions. Single-cell RNA sequencing was used to construct a map of gene expression in lesions from brains of patients with multiple sclerosis, revealing distinct lineage- and region-specific transcriptomic changes associated with selective cortical neuron damage and glial activation.

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model—“registered access”—to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.

CRISPR (clustered regularly interspaced short palindromic repeats) genome-editing experiments offer enormous potential for the evaluation of genomic loci using arrayed single guide RNAs (sgRNAs) or pooled sgRNA libraries. Numerous computational tools are available to help design sgRNAs with optimal on-target efficiency and minimal off-target potential. In addition, computational tools have been developed to analyze deep-sequencing data resulting from genome-editing experiments. However, these tools are typically developed in isolation and oftentimes are not readily translatable into laboratory-based experiments. Here, we present a protocol that describes in detail both the computational and benchtop implementation of an arrayed and/or pooled CRISPR genome-editing experiment. This protocol provides instructions for sgRNA design with CRISPOR (computational tool for the design, evaluation, and cloning of sgRNA sequences), experimental implementation, and analysis of the resulting high-throughput sequencing data with CRISPResso (computational tool for analysis of genome-editing outcomes from deep-sequencing data). This protocol allows for design and execution of arrayed and pooled CRISPR experiments in 4–5 weeks by non-experts, as well as computational data analysis that can be performed in 1–2 d by both computational and noncomputational biologists alike using web-based and/or command-line versions.

Alternative pre-mRNA splicing plays a major role in expanding the transcript output of human genes. This process is regulated, in part, by the interplay of trans-acting RNA binding proteins (RBPs) with myriad cis-regulatory elements scattered throughout pre-mRNAs. These molecular recognition events are critical for defining the protein-coding sequences (exons) within pre-mRNAs and directing spliceosome assembly on noncoding regions (introns). One of the earliest events in this process is recognition of the 3′ splice site (3′ss) by U2 small nuclear RNA auxiliary factor 2 (U2AF2). Splicing regulators, such as the heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1), influence spliceosome assembly both in vitro and in vivo, but their mechanisms of action remain poorly described on a global scale. HNRNPA1 also promotes proofreading of 3′ss sequences though a direct interaction with the U2AF heterodimer. To determine how HNRNPA1 regulates U2AF–RNA interactions in vivo, we analyzed U2AF2 RNA binding specificity using individual-nucleotide resolution crosslinking immunoprecipitation (iCLIP) in control and HNRNPA1 overexpression cells. We observed changes in the distribution of U2AF2 crosslinking sites relative to the 3′ss of alternative cassette exons but not constitutive exons upon HNRNPA1 overexpression. A subset of these events shows a concomitant increase of U2AF2 crosslinking at distal intronic regions, suggesting a shift of U2AF2 to “decoy” binding sites. Of the many noncanonical U2AF2 binding sites, Alu-derived RNA sequences represented one of the most abundant classes of HNRNPA1-dependent decoys. We propose that one way HNRNPA1 regulates exon definition is to modulate the interaction of U2AF2 with decoy or bona fide 3′ss.

The CRISPR-Cas revolution is taking place in virtually all fields of life sciences. Harnessing DNA cleavage with the CRISPR-Cas9 system of Streptococcus pyogeneshas proven to be extraordinarily simple and efficient, relying only on the design of a synthetic single guide RNA (sgRNA) and its co-expression with Cas9. Here, we review the progress in the design of sgRNA from the original dual RNA guide for S. pyogenesand Staphylococcus aureusCas9 (SpCas9 and SaCas9). New assays for genome-wide identification of off-targets have provided important insights into the issue of cleavage specificity in vivo. At the same time, the on-target activity of thousands of guides has been determined. These data have led to numerous online tools that facilitate the selection of guide RNAs in target sequences. It appears that for most basic research applications, cleavage activity can be maximized and off-targets minimized by carefully choosing guide RNAs based on computational predictions. Moreover, recent studies of Cas proteins have further improved the flexibility and precision of the CRISPR-Cas toolkit for genome editing. Inspired by the crystal structure of the complex of sgRNA-SpCas9 bound to target DNA, several variants of SpCas9 have recently been engineered, either with novel protospacer adjacent motifs (PAMs) or with drastically reduced off-targets. Novel Cas9 and Cas9-like proteins called Cpf1 have also been characterized from other bacteria and will benefit from the insights obtained from SpCas9. Genome editing with CRISPR-Cas9 may also progress with better understanding and control of cellular DNA repair pathways activated after Cas9-induced DNA cleavage.

Despite widespread use of CRISPR, comprehensive data on the frequency and impact of Cas9-mediated off-targets in modified rodents are limited. Here we present deep-sequencing data from 81 genome-editing projects on mouse and rat genomes at 1,423 predicted off-target sites, 32 of which were confirmed, and show that high-fidelity Cas9 versions reduced off-target mutation rates in vivo. Using whole-genome sequencing data from ten mouse embryos, treated with a single guide RNA (sgRNA), and from their genetic parents, we found 43 off-targets, 30 of which were predicted by an adapted version of GUIDE-seq. Off-targets identified by whole-genome sequencing of CRISPR-treated mouse embryos and their genetic parents are compared to predictions from GUIDE-seq and computational methods.

The gene regulatory network (GRN) that supports neural stem cell (NS cell) self-renewal has so far been poorly characterized. Knowledge of the central transcription factors (TFs), the noncoding gene regulatory regions that they bind to, and the genes whose expression they modulate will be crucial in unlocking the full therapeutic potential of these cells. Here, we use DNase-seq in combination with analysis of histone modifications to identify multiple classes of epigenetically and functionally distinct cis-regulatory elements (CREs). Through motif analysis and ChIP-seq, we identify several of the crucial TF regulators of NS cells. At the core of the network are TFs of the basic helix-loop-helix (bHLH), nuclear factor I (NFI), SOX, and FOX families, with CREs often densely bound by several of these different TFs. We use machine learning to highlight several crucial regulatory features of the network that underpin NS cell self-renewal and multipotency. We validate our predictions by functional analysis of the bHLH TF OLIG2. This TF makes an important contribution to NS cell self-renewal by concurrently activating pro-proliferation genes and preventing the untimely activation of genes promoting neuronal differentiation and stem cell quiescence.

Microglia are resident macrophages in the brain that emerge in early development and respond to the local environment by altering their molecular and phenotypic states. Fundamental questions about microglia diversity and function during development remain unanswered because we lack experimental strategies to interrogate their interactions with other cell types and responses to perturbations ex vivo. We compared human microglia states across culture models, including cultured primary and pluripotent stem cell-derived microglia. We developed a “report card” of gene expression signatures across these distinct models to facilitate characterization of their responses across experimental models, perturbations, and disease conditions. Xenotransplantation of human microglia into cerebral organoids allowed us to characterize key transcriptional programs of developing microglia in vitroand reveal that microglia induce transcriptional changes in neural stem cells and decrease interferon signaling response genes. Microglia additionally accelerate the emergence of synchronized oscillatory network activity in brain organoids by modulating synaptic density.

An automated pipeline parses primary literature about Mendelian diseases to rank patient candidate causative genes, thereby accelerating diagnosis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background: The success of the CRISPR/Cas9 genome editing technique depends on the choice of the guide RNA sequence, which is facilitated by various websites. Despite the importance and popularity of these algorithms, it is unclear to which extent their predictions are in agreement with actual measurements. Results: We conduct the first independent evaluation of CRISPR/Cas9 predictions. To this end, we collect data from eight SpCas9 off-target studies and compare them with the sites predicted by popular algorithms. We identify problems in one implementation but found that sequence-based off-target predictions are very reliable, identifying most off-targets with mutation rates superior to 0.1 %, while the number of false positives can be largely reduced with a cutoff on the off-target score. We also evaluate on-target efficiency prediction algorithms against available datasets. The correlation between the predictions and the guide activity varied considerably, especially for zebrafish. Together with novel data from our labs, we find that the optimal on-target efficiency prediction model strongly depends on whether the guide RNA is expressed from a U6 promoter or transcribed in vitro. We further demonstrate that the best predictions can significantly reduce the time spent on guide screening. Conclusions: To make these guidelines easily accessible to anyone planning a CRISPR genome editing experiment, we built a new website (http://crispor.org) that predicts off-targets and helps select and clone efficient guide sequences for more than 120 genomes using different Cas9 proteins and the eight efficiency scoring systems evaluated here.

Background Decoding transcriptional regulatory networks and the genomic cis-regulatory logic implemented in their control nodes is a fundamental challenge in genome biology. High-throughput computational and experimental analyses of regulatory networks and sequences rely heavily on positive control data from prior small-scale experiments, but the vast majority of previously discovered regulatory data remains locked in the biomedical literature.Results We develop text-mining strategies to identify relevant publications and extract sequence information to assist the regulatory annotation process. Using a vector space model to identify Medline abstracts from papers likely to have high cis-regulatory content, we demonstrate that document relevance ranking can assist the curation of transcriptional regulatory networks and estimate that, minimally, 30,000 papers harbor unannotated cis-regulatory data. In addition, we show that DNA sequences can be extracted from primary text with high cis-regulatory content and mapped to genome sequences as a means of identifying the location, organism and target gene information that is critical to the cis-regulatory annotation process.Conclusion Our results demonstrate that text-mining technologies can be successfully integrated with genome annotation systems, thereby increasing the availability of annotated cis-regulatory data needed to catalyze advances in the field of gene regulation.