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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.

Inbred mouse strains reveal the molecular basis of mammalian traits and diseases, particularly recessive ones. We utilized mouse community curated resources to set up an automated screen to discover novel testable gene function hypotheses. Using 11,832 community contributed strain-differentiating experiments and trait presence/absence scoring, we searched for all experiments where strains can be split by their phenotypic values (e.g., high vs. low responders). Then, using 48 sequenced strains, we found one or more candidate gene for each experiment where homozygous high-impact variants (such as stopgain, frameshifts) segregate strains into these same binary grouping. Our approach rediscovered 212 known gene-phenotype relationships, almost always highlighting potentially novel causal variants, as well as thousands of gene function hypotheses. To help find the most exciting hypotheses, we improved the state of the art in machine learning driven literature-based discovery (LBD). Reading on our top 3 ranked candidate genes per experiment reveals 80% of rediscovered relationships, compared to 5% reading at random. We proposed 1,842 novel gene-phenotype testable hypotheses using our approach. We built a web portal at aimhigh.stanford.edu to allow researchers to view all our testable hypotheses in detail. Our open-source code can be rerun as more sequenced strains and phenotyping experiments become available.

We introduce the dynseq genome browser track, which displays DNA nucleotide characters scaled by user-specified, base-resolution scores provided in the BigWig file format. The dynseq track enables visualization of context-specific, informative genomic sequence features. We demonstrate its utility in three popular genome browsers for interpreting cis-regulatory sequence syntax and regulatory variant interpretation by visualizing nucleotide importance scores derived from machine learning models of regulatory DNA trained on protein-DNA binding and chromatin accessibility experiments.

Background: Smooth muscle cells (SMCs) transition into a number of different phenotypes during atherosclerosis, including those that resemble fibroblasts and chondrocytes, and make up the majority of cells in the atherosclerotic plaque. To better understand the epigenetic and transcriptional mechanisms that mediate these cell state changes, and how they relate to risk for coronary artery disease (CAD), we have investigated the causality and function of transcription factors at genome-wide associated loci. Methods: We used CRISPR-Cas 9 genome and epigenome editing to identify the causal gene and cells for a complex CAD genome-wide association study signal at 2q22.3. Single-cell epigenetic and transcriptomic profiling in murine models and human coronary artery smooth muscle cells were used to understand the cellular and molecular mechanism by which this CAD risk gene exerts its function. Results: CRISPR-Cas 9 genome and epigenome editing showed that the complex CAD genetic signals within a genomic region at 2q22.3 lie within smooth muscle long-distance enhancers for ZEB2 , a transcription factor extensively studied in the context of epithelial mesenchymal transition in development of cancer. Zeb2 regulates SMC phenotypic transition through chromatin remodeling that obviates accessibility and disrupts both Notch and transforming growth factor β signaling, thus altering the epigenetic trajectory of SMC transitions. SMC-specific loss of Zeb2 resulted in an inability of transitioning SMCs to turn off contractile programing and take on a fibroblast-like phenotype, but accelerated the formation of chondromyocytes, mirroring features of high-risk atherosclerotic plaques in human coronary arteries. Conclusions: These studies identify ZEB2 as a new CAD genome-wide association study gene that affects features of plaque vulnerability through direct effects on the epigenome, providing a new therapeutic approach to target vascular disease.

Data availability: No new data were generated or analysed in support of this research. Copyright © The Author(s) 2022. GENCODE produces high quality gene and transcript annotation for the human and mouse genomes. All GENCODE annotation is supported by experimental data and serves as a reference for genome biology and clinical genomics. The GENCODE consortium generates targeted experimental data, develops bioinformatic tools and carries out analyses that, along with externally produced data and methods, support the identification and annotation of transcript structures and the determination of their function. Here, we present an update on the annotation of human and mouse genes, including developments in the tools, data, analyses and major collaborations which underpin this progress. For example, we report the creation of a set of non-canonical ORFs identified in GENCODE transcripts, the LRGASP collaboration to assess the use of long transcriptomic data to build transcript models, the progress in collaborations with RefSeq and UniProt to increase convergence in the annotation of human and mouse protein-coding genes, the propagation of GENCODE across the human pan-genome and the development of new tools to support annotation of regulatory features by GENCODE. Our annotation is accessible via Ensembl, the UCSC Genome Browser and https://www.gencodegenes.org. National Human Genome Research Institute of the National Institutes of Health [U41HG007234, R01HG004037]; Wellcome Trust [WT222155/Z/20/Z]; European Molecular Biology Laboratory. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Funding for open access charge: National Institutes of Health.

SummaryCongenital heart defects, the most common birth disorders, are the clinical manifestation of anomalies in fetal heart development - a complex process involving dynamic spatiotemporal coordination among various precursor cell lineages. This complexity underlies the incomplete understanding of the genetic architecture of congenital heart diseases (CHDs). To define the multi-cellular epigenomic and transcriptional landscape of cardiac cellular development, we generated single-cell chromatin accessibility maps of human fetal heart tissues. We identified eight major differentiation trajectories involving primary cardiac cell types, each associated with dynamic transcription factor (TF) activity signatures. We identified similarities and differences of regulatory landscapes of iPSC-derived cardiac cell types and their in vivo counterparts. We interpreted deep learning models that predict cell-type resolved, base-resolution chromatin accessibility profiles from DNA sequence to decipher underlying TF motif lexicons and infer the regulatory impact of non-coding variants. De novo mutations predicted to affect chromatin accessibility in arterial endothelium were enriched in CHD cases versus controls. We used CRISPR-based perturbations to validate an enhancer harboring a nominated regulatory CHD mutation, linking it to effects on the expression of a known CHD gene JARID2. Together, this work defines the cell-type resolved cis-regulatory sequence determinants of heart development and identifies disruption of cell type-specific regulatory elements as a component of the genetic etiology of CHD.

Genome-wide association studies (GWAS) of eye disorders have identified hundreds of genetic variants associated with ocular disease. However, the vast majority of these variants are noncoding, making it challenging to interpret their function. Here, we present a joint single-cell atlas of gene expression and chromatin accessibility of the adult human retina with >50,000 cells, which we used to analyze noncoding single-nucleotide polymorphisms (SNPs) implicated by GWAS of age-related macular degeneration, glaucoma, diabetic retinopathy, myopia, and type 2 macular telangiectasia. We integrate this atlas with a HiChIP enhancer connectome, expression quantitative trait loci (eQTL) data, and base-resolution deep learning models to predict noncoding SNPs with causal roles in eye disease, assess SNP impact on transcription factor binding, and define their known and novel target genes. Our efforts nominate pathogenic SNP-target gene interactions for multiple vision disorders and provide a potentially powerful resource for interpreting noncoding variation in the eye.