We present here an extensive epigenetic analysis of a 500 kb region, which encompasses the human desmin gene (DES) and its 5' locus control region (LCR), the only muscle-specific transcriptional regulatory element of this type described to date. These data complement and extend Encyclopaedia of DNA Elements (ENCODE) studies on region ENr133. We analysed histone modifications and underlying DNA methylation patterns in physiologically relevant DES expressing (myoblast/myotube) and non-expressing (peripheral blood mononuclear) primary human cells.

The Ensembl project (http://www.ensembl.org) seeks to enable genomic science by providing high quality, integrated annotation on chordate and selected eukaryotic genomes within a consistent and accessible infrastructure. All supported species include comprehensive, evidence-based gene annotations and a selected set of genomes includes additional data focused on variation, comparative, evolutionary, functional and regulatory annotation. The most advanced resources are provided for key species including human, mouse, rat and zebrafish reflecting the popularity and importance of these species in biomedical research. As of Ensembl release 59 (August 2010), 56 species are supported of which 5 have been added in the past year. Since our previous report, we have substantially improved the presentation and integration of both data of disease relevance and the regulatory state of different cell types.

RNA inhibition by siRNAs is a frequently used approach to identify genes required for specific biological processes. However RNAi screening using siRNAs is hampered by non-specific or off target effects of the siRNAs, making it difficult to separate genuine hits from false positives. It is thought that many of the off-target effects seen in RNAi experiments are due to siRNAs acting as microRNAs (miRNAs), causing a reduction in gene expression of unintended targets via matches to the 6 or 7 nt 'seed' sequence. We have conducted a careful examination of off-target effects during an siRNA screen for novel regulators of the TRAIL apoptosis induction pathway(s).

Identification of regulatory elements and their target genes is complicated by the fact that regulatory elements can act over large genomic distances. Identification of long-range acting elements is particularly important in the case of disease genes as mutations in these elements can result in human disease. It is becoming increasingly clear that long-range control of gene expression is facilitated by chromatin looping interactions. These interactions can be detected by chromosome conformation capture (3C). Here, we employed 3C as a discovery tool for identification of long-range regulatory elements that control the cystic fibrosis transmembrane conductance regulator gene, CFTR. We identified four elements in a 460-kb region around the locus that loop specifically to the CFTR promoter exclusively in CFTR expressing cells. The elements are located 20 and 80 kb upstream; and 109 and 203 kb downstream of the CFTR promoter. These elements contain DNase I hypersensitive sites and histone modification patterns characteristic of enhancers. The elements also interact with each other and the latter two activate the CFTR promoter synergistically in reporter assays. Our results reveal novel long-range acting elements that control expression of CFTR and suggest that 3C-based approaches can be used for discovery of novel regulatory elements.

miRNAs are a class of small non-coding RNAs that regulate gene expression and have critical functions in various biological processes. Hundreds of miRNAs have been identified in mammalian genomes but only a small number of them have been functionally characterized. Recent studies also demonstrate that some miRNAs have important roles in reprogramming somatic cells to induced pluripotent stem cells (iPSCs).

In an effort to locate susceptibility genes for type 1 diabetes (T1D) several genome-wide linkage scans have been undertaken. A chromosomal region designated IDDM10 retained genome-wide significance in a combined analysis of the main linkage scans. Here, we studied sequence polymorphisms in 23 Mb on chromosome 10p12-q11, including the putative IDDM10 region, to identify genes associated with T1D.

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

The Open Targets Platform integrates different data sources in order to facilitate identification of potential therapeutic drug targets to treat human diseases. It currently provides evidence for nearly 2.6 million potential target-disease pairs. G-protein coupled receptors are a drug target class of high interest because of the number of successful drugs being developed against them over many years. Here we describe a systematic approach utilizing the Open Targets Platform data to uncover and prioritize potential new disease indications for the G-protein coupled receptors and their ligands.

The virus SARS-CoV-2 can exploit biological vulnerabilities (e.g. host proteins) in susceptible hosts that predispose to the development of severe COVID-19.

Genome-wide association studies (GWASs) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. In the present study, we present an open resource that provides systematic fine mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine mapped to a single-coding causal variant and colocalized with a single gene. We trained a machine-learning model using the fine-mapped genetics and functional genomics data and 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring genes, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (odds ratio = 8.1, 95% confidence interval = 5.7, 11.5). These results are publicly available through a web portal ( http://genetics.opentargets.org ), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

The Centre for Therapeutic Target Validation (CTTV - https://www.targetvalidation.org/) was established to generate therapeutic target evidence from genome-scale experiments and analyses. CTTV aims to support the validity of therapeutic targets by integrating existing and newly-generated data. Data integration has been achieved in some resources by mapping metadata such as disease and phenotypes to the Experimental Factor Ontology (EFO). Additionally, the relationship between ontology descriptions of rare and common diseases and their phenotypes can offer insights into shared biological mechanisms and potential drug targets. Ontologies are not ideal for representing the sometimes associated type relationship required. This work addresses two challenges; annotation of diverse big data, and representation of complex, sometimes associated relationships between concepts.

Small RNAs such as microRNAs play important roles in embryonic stem cell maintenance and differentiation. A broad range of microRNAs is expressed in embryonic stem cells while only a fraction of their targets have been identified. We have performed large-scale identification of embryonic stem cell microRNA targets using a murine embryonic stem cell line deficient in the expression of Dgcr8. These cells are heavily depleted for microRNAs, allowing us to reintroduce specific microRNA duplexes and identify refined target sets. We used deep sequencing of small RNAs, mRNA expression profiling and bioinformatics analysis of microRNA seed matches in 3' UTRs to identify target transcripts. Consequently, we have identified a network of microRNAs that converge on the regulation of several important cellular pathways. Additionally, our experiments have revealed a novel candidate for Dgcr8-independent microRNA genesis and highlighted the challenges currently facing miRNA annotation.

The Ensembl project (http://www.ensembl.org) provides genome resources for chordate genomes with a particular focus on human genome data as well as data for key model organisms such as mouse, rat and zebrafish. Five additional species were added in the last year including gibbon (Nomascus leucogenys) and Tasmanian devil (Sarcophilus harrisii) bringing the total number of supported species to 61 as of Ensembl release 64 (September 2011). Of these, 55 species appear on the main Ensembl website and six species are provided on the Ensembl preview site (Pre!Ensembl; http://pre.ensembl.org) with preliminary support. The past year has also seen improvements across the project.

Since Haldane first noticed an excess of paternally derived mutations, it has been considered that most mutations derive from errors during germ line replication. Miyata et al. (1987) proposed that differences in the rate of neutral evolution on X, Y, and autosome can be employed to measure the extent of this male bias. This commonly applied method assumes replication to be the sole source of between-chromosome variation in substitution rates. We propose a simple test of this assumption: If true, estimates of the male bias should be independent of which two chromosomal classes are compared. Prior evidence from rodents suggested that this might not be true, but conclusions were limited by a lack of rat Y-linked sequence. We therefore sequenced two rat Y-linked bacterial artificial chromosomes and determined evolutionary rate by comparison with mouse. For estimation of rates we consider both introns and synonymous rates. Surprisingly, for both data sets the prediction of congruent estimates of alpha is strongly rejected. Indeed, some comparisons suggest a female bias with autosomes evolving faster than Y-linked sequence. We conclude that the method of Miyata et al. (1987) has the potential to provide incorrect estimates. Correcting the method requires understanding of the other causes of substitution that might differ between chromosomal classes. One possible cause is recombination-associated substitution bias for which we find some evidence. We note that if, as some suggest, this association is dominantly owing to male recombination, the high estimates of alpha seen in birds is to be expected as Z chromosomes recombine in males.

Although the human genome sequence was declared complete in 2004, the sequence was interrupted by 341 gaps of which 308 lay in an estimated approximately 28 Mb of euchromatin. While these gaps constitute only approximately 1% of the sequence, knowledge of the full complement of human genes and regulatory elements is incomplete without their sequences.

The Open Targets Platform integrates evidence from genetics, genomics, transcriptomics, drugs, animal models and scientific literature to score and rank target-disease associations for drug target identification. The associations are displayed in an intuitive user interface (https://www.targetvalidation.org), and are available through a REST-API (https://api.opentargets.io/v3/platform/docs/swagger-ui) and a bulk download (https://www.targetvalidation.org/downloads/data). In addition to target-disease associations, we also aggregate and display data at the target and disease levels to aid target prioritisation. Since our first publication two years ago, we have made eight releases, added new data sources for target-disease associations, started including causal genetic variants from non genome-wide targeted arrays, added new target and disease annotations, launched new visualisations and improved existing ones and released a new web tool for batch search of up to 200 targets. We have a new URL for the Open Targets Platform REST-API, new REST endpoints and also removed the need for authorisation for API fair use. Here, we present the latest developments of the Open Targets Platform, expanding the evidence and target-disease associations with new and improved data sources, refining data quality, enhancing website usability, and increasing our user base with our training workshops, user support, social media and bioinformatics forum engagement.

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

Genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are known to preferentially co-locate to active regulatory elements in tissues and cell types relevant to disease aetiology. Further characterisation of associated cell type-specific regulation can broaden our understanding of how GWAS signals may contribute to disease risk.

Advancing age is the greatest risk factor for developing multiple age-related diseases. Therapeutic approaches targeting the underlying pathways of ageing, rather than individual diseases, may be an effective way to treat and prevent age-related morbidity while reducing the burden of polypharmacy. We harness the Open Targets Genetics Portal to perform a systematic analysis of nearly 1,400 genome-wide association studies (GWAS) mapped to 34 age-related diseases and traits, identifying genetic signals that are shared between two or more of these traits. Using locus-to-gene (L2G) mapping, we identify 995 targets with shared genetic links to age-related diseases and traits, which are enriched in mechanisms of ageing and include known ageing and longevity-related genes. Of these 995 genes, 128 are the target of an approved or investigational drug, 526 have experimental evidence of binding pockets or are predicted to be tractable, and 341 have no existing tractability evidence, representing underexplored genes which may reveal novel biological insights and therapeutic opportunities. We present these candidate targets for exploration and prioritisation in a web application.

Imprinted genes show expression from one parental allele only and are important for development and behaviour. This extreme mode of allelic imbalance has been described for approximately 56 human genes. Imprinting status is often disrupted in cancer and dysmorphic syndromes. More subtle variation of gene expression, that is not parent-of-origin specific, termed 'allele-specific gene expression' (ASE) is more common and may give rise to milder phenotypic differences. Using two allele-specific high-throughput technologies alongside bioinformatics predictions, normal term human placenta was screened to find new imprinted genes and to ascertain the extent of ASE in this tissue.