The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE¹ and Roadmap Epigenomics² data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

The Encylopedia of DNA Elements (ENCODE) Project launched in 2003 with the long-term goal of developing a comprehensive map of functional elements in the human genome. These included genes, biochemical regions associated with gene regulation (for example, transcription factor binding sites, open chromatin, and histone marks) and transcript isoforms. The marks serve as sites for candidate cis-regulatory elements (cCREs) that may serve functional roles in regulating gene expression¹. The project has been extended to model organisms, particularly the mouse. In the third phase of ENCODE, nearly a million and more than 300,000 cCRE annotations have been generated for human and mouse, respectively, and these have provided a valuable resource for the scientific community.

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.

Background: Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes. Results: To facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. We use BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the TargetFinder and PEP supervised learning methods. We find that while TargetFinder is the best-performing method, it is only modestly better than a baseline distance method for most benchmark datasets when trained and tested with the same cell type and that TargetFinder often does not outperform the distance method when applied across cell types. Conclusions: Our results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.

Background & Aims It has been a challenge to identify liver tumor suppressors or oncogenes due to the genetic heterogeneity of these tumors. We performed a genome-wide screen to identify suppressors of liver tumor formation in mice, using CRISPR-mediated genome editing. Methods We performed a genome-wide CRISPR/Cas9-based knockout screen of P53-null mouse embryonic liver progenitor cells that overexpressed MYC. We infected p53^−/−;Myc;Cas9 hepatocytes with the mGeCKOa lentiviral library of 67,000 single-guide RNAs (sgRNAs), targeting 20,611 mouse genes, and transplanted the transduced cells subcutaneously into nude mice. Within 1 month, all the mice that received the sgRNA library developed subcutaneous tumors. We performed high-throughput sequencing of tumor DNA and identified sgRNAs increased at least 8-fold compared to the initial cell pool. To validate the top 10 candidate tumor suppressors from this screen, we collected data from patients with hepatocellular carcinoma (HCC) using the Cancer Genome Atlas and COSMIC databases. We used CRISPR to inactivate candidate tumor suppressor genes in p53^−/−;Myc;Cas9 cells and transplanted them subcutaneously into nude mice; tumor formation was monitored and tumors were analyzed by histology and immunohistochemistry. Mice with liver-specific disruption of p53 were given hydrodynamic tail-vein injections of plasmids encoding Myc and sgRNA/Cas9 designed to disrupt candidate tumor suppressors; growth of tumors and metastases was monitored. We compared gene expression profiles of liver cells with vs without tumor suppressor gene disrupted by sgRNA/Cas9. Genes found to be up-regulated after tumor suppressor loss were examined in liver cancer cell lines; their expression was knocked down using small hairpin RNAs, and tumor growth was examined in nude mice. Effects of the MEK inhibitors AZD6244, U0126, and trametinib, or the multi-kinase inhibitor sorafenib, were examined in human and mouse HCC cell lines. Results We identified 4 candidate liver tumor suppressor genes not previously associated with liver cancer (Nf1, Plxnb1, Flrt2, and B9d1). CRISPR-mediated knockout of Nf1, a negative regulator of RAS, accelerated liver tumor formation in mice. Loss of Nf1 or activation of RAS up-regulated the liver progenitor cell markers HMGA2 and SOX9. RAS pathway inhibitors suppressed the activation of the Hmga2 and Sox9 genes that resulted from loss of Nf1 or oncogenic activation of RAS. Knockdown of HMGA2 delayed formation of xenograft tumors from cells that expressed oncogenic RAS. In human HCCs, low levels of NF1 messenger RNA or high levels of HMGA2 messenger RNA were associated with shorter patient survival time. Liver cancer cells with inactivation of Plxnb1, Flrt2, and B9d1 formed more tumors in mice and had increased levels of mitogen-activated protein kinase phosphorylation. Conclusions Using a CRISPR-based strategy, we identified Nf1, Plxnb1, Flrt2, and B9d1 as suppressors of liver tumor formation. We validated the observation that RAS signaling, via mitogen-activated protein kinase, contributes to formation of liver tumors in mice. We associated decreased levels of NF1 and increased levels of its downstream protein HMGA2 with survival times of patients with HCC. Strategies to inhibit or reduce HMGA2 might be developed to treat patients with liver cancer.