Enhancer evolution across 20 mammalian species.

The mammalian radiation has corresponded with rapid changes in noncoding regions of the genome, but we lack a comprehensive understanding of regulatory evolution in mammals. Here, we track the evolution of promoters and enhancers active in liver across 20 mammalian species from six diverse orders by profiling genomic enrichment of H3K27 acetylation and H3K4 trimethylation. We report that rapid evolution of enhancers is a universal feature of mammalian genomes. Most of the recently evolved enhancers arise from ancestral DNA exaptation, rather than lineage-specific expansions of repeat elements. In contrast, almost all liver promoters are partially or fully conserved across these species. Our data further reveal that recently evolved enhancers can be associated with genes under positive selection, demonstrating the power of this approach for annotating regulatory adaptations in genomic sequences. These results provide important insight into the functional genetics underpinning mammalian regulatory evolution.

Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets.

CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR-Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score database currently allows users to investigate the fitness effect of 18 009 genes tested across 323 cancer cell models. Through interactive interfaces, users can investigate data by selecting a specific gene, cancer cell model or tissue type, as well as browsing all gene fitness scores. Additionally, users can identify and rank candidate drug targets based on an established oncology target prioritization pipeline, incorporating genetic biomarkers and clinical datasets for each target, and including suitability for drug development based on pharmaceutical tractability. Data are freely available and downloadable. To enhance analyses, links to other key resources including Open Targets, COSMIC, the Cell Model Passports, UniProt and the Genomics of Drug Sensitivity in Cancer are provided. The Project Score database is a valuable new tool for investigating genetic dependencies in cancer cells and the identification of candidate oncology targets.

Open Targets Platform: supporting systematic drug-target identification and prioritisation.

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

Open Targets Platform: new developments and updates two years on.

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.

iHam and pyHam: visualizing and processing hierarchical orthologous groups.

SUMMARY: The evolutionary history of gene families can be complex due to duplications and losses. This complexity is compounded by the large number of genomes simultaneously considered in contemporary comparative genomic analyses. As provided by several orthology databases, hierarchical orthologous groups (HOGs) are sets of genes that are inferred to have descended from a common ancestral gene within a species clade. This implies that the set of HOGs defined for a particular clade correspond to the ancestral genes found in its last common ancestor. Furthermore, by keeping track of HOG composition along the species tree, it is possible to infer the emergence, duplications and losses of genes within a gene family of interest. However, the lack of tools to manipulate and analyse HOGs has made it difficult to extract, display and interpret this type of information. To address this, we introduce interactive HOG analysis method, an interactive JavaScript widget to visualize and explore gene family history encoded in HOGs and python HOG analysis method, a python library for programmatic processing of genes families. These complementary open source tools greatly ease adoption of HOGs as a scalable and interpretable concept to relate genes across multiple species. AVAILABILITY AND IMPLEMENTATION: iHam's code is available at https://github.com/DessimozLab/iHam or can be loaded dynamically. pyHam's code is available at https://github.com/DessimozLab/pyHam and or via the pip package 'pyham'.

Open Targets: a platform for therapeutic target identification and validation.

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.

Ensembl 2016.

The Ensembl project (http://www.ensembl.org) is a system for genome annotation, analysis, storage and dissemination designed to facilitate the access of genomic annotation from chordates and key model organisms. It provides access to data from 87 species across our main and early access Pre! websites. This year we introduced three newly annotated species and released numerous updates across our supported species with a concentration on data for the latest genome assemblies of human, mouse, zebrafish and rat. We also provided two data updates for the previous human assembly, GRCh37, through a dedicated website (http://grch37.ensembl.org). Our tools, in particular the VEP, have been improved significantly through integration of additional third party data. REST is now capable of larger-scale analysis and our regulatory data BioMart can deliver faster results. The website is now capable of displaying long-range interactions such as those found in cis-regulated datasets. Finally we have launched a website optimized for mobile devices providing views of genes, variants and phenotypes. Our data is made available without restriction and all code is available from our GitHub organization site (http://github.com/Ensembl) under an Apache 2.0 license.

TnT: a set of libraries for visualizing trees and track-based annotations for the web.

UNLABELLED: There is an increasing need for rich and dynamic biological data visualizations in bioinformatic web applications. New standards in web technologies, like SVG or Canvas, are now supported by most modern web browsers allowing the blossoming of powerful visualizations in biological data analysis. The exploration of different ways to visualize genomic data is still challenging due to the lack of flexible tools to develop them. Here, I present a set of libraries aimed at creating powerful tree- and track-based visualizations for the web. Its modularity and rich API facilitate the development of many different visualizations ranging from simple species trees to complex visualizations comprising per-node data annotations or even simple genome browsers. AVAILABILITY AND IMPLEMENTATION: The TnT libraries have been written in Javascript, licensed under the APACHE 2.0 license and hosted at https://github.com/tntvis CONTACT: mp@ebi.ac.uk.

Ensembl 2015.

Ensembl (http://www.ensembl.org) is a genomic interpretation system providing the most up-to-date annotations, querying tools and access methods for chordates and key model organisms. This year we released updated annotation (gene models, comparative genomics, regulatory regions and variation) on the new human assembly, GRCh38, although we continue to support researchers using the GRCh37.p13 assembly through a dedicated site (http://grch37.ensembl.org). Our Regulatory Build has been revamped to identify regulatory regions of interest and to efficiently highlight their activity across disparate epigenetic data sets. A number of new interfaces allow users to perform large-scale comparisons of their data against our annotations. The REST server (http://rest.ensembl.org), which allows programs written in any language to query our databases, has moved to a full service alongside our upgraded website tools. Our online Variant Effect Predictor tool has been updated to process more variants and calculate summary statistics. Lastly, the WiggleTools package enables users to summarize large collections of data sets and view them as single tracks in Ensembl. The Ensembl code base itself is more accessible: it is now hosted on our GitHub organization page (https://github.com/Ensembl) under an Apache 2.0 open source license.

The Ensembl REST API: Ensembl Data for Any Language.

MOTIVATION: We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. AVAILABILITY AND IMPLEMENTATION: The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest.

TreeFam v9: a new website, more species and orthology-on-the-fly.

TreeFam (http://www.treefam.org) is a database of phylogenetic trees inferred from animal genomes. For every TreeFam family we provide homology predictions together with the evolutionary history of the genes. Here we describe an update of the TreeFam database. The TreeFam project was resurrected in 2012 and has seen two releases since. The latest release (TreeFam 9) was made available in March 2013. It has orthology predictions and gene trees for 109 species in 15,736 families covering ∼2.2 million sequences. With release 9 we made modifications to our production pipeline and redesigned our website with improved gene tree visualizations and Wikipedia integration. Furthermore, we now provide an HMM-based sequence search that places a user-provided protein sequence into a TreeFam gene tree and provides quick orthology prediction. The tool uses Mafft and RAxML for the fast insertion into a reference alignment and tree, respectively. Besides the aforementioned technical improvements, we present a new approach to visualize gene trees and alternative displays that focuses on showing homology information from a species tree point of view. From release 9 onwards, TreeFam is now hosted at the EBI.

Ensembl 2014.

Ensembl (http://www.ensembl.org) creates tools and data resources to facilitate genomic analysis in chordate species with an emphasis on human, major vertebrate model organisms and farm animals. Over the past year we have increased the number of species that we support to 77 and expanded our genome browser with a new scrollable overview and improved variation and phenotype views. We also report updates to our core datasets and improvements to our gene homology relationships from the addition of new species. Our REST service has been extended with additional support for comparative genomics and ontology information. Finally, we provide updated information about our methods for data access and resources for user training.

Ensembl 2013.

The Ensembl project (http://www.ensembl.org) provides genome information for sequenced chordate genomes with a particular focus on human, mouse, zebrafish and rat. Our resources include evidenced-based gene sets for all supported species; large-scale whole genome multiple species alignments across vertebrates and clade-specific alignments for eutherian mammals, primates, birds and fish; variation data resources for 17 species and regulation annotations based on ENCODE and other data sets. Ensembl data are accessible through the genome browser at http://www.ensembl.org and through other tools and programmatic interfaces.

Comparison of different assembly and annotation tools on analysis of simulated viral metagenomic communities in the gut.

BACKGROUND: The main limitations in the analysis of viral metagenomes are perhaps the high genetic variability and the lack of information in extant databases. To address these issues, several bioinformatic tools have been specifically designed or adapted for metagenomics by improving read assembly and creating more sensitive methods for homology detection. This study compares the performance of different available assemblers and taxonomic annotation software using simulated viral-metagenomic data. RESULTS: We simulated two 454 viral metagenomes using genomes from NCBI's RefSeq database based on the list of actual viruses found in previously published metagenomes. Three different assembly strategies, spanning six assemblers, were tested for performance: overlap-layout-consensus algorithms Newbler, Celera and Minimo; de Bruijn graphs algorithms Velvet and MetaVelvet; and read probabilistic model Genovo. The performance of the assemblies was measured by the length of resulting contigs (using N50), the percentage of reads assembled and the overall accuracy when comparing against corresponding reference genomes. Additionally, the number of chimeras per contig and the lowest common ancestor were estimated in order to assess the effect of assembling on taxonomic and functional annotation. The functional classification of the reads was evaluated by counting the reads that correctly matched the functional data previously reported for the original genomes and calculating the number of over-represented functional categories in chimeric contigs. The sensitivity and specificity of tBLASTx, PhymmBL and the k-mer frequencies were measured by accurate predictions when comparing simulated reads against the NCBI Virus genomes RefSeq database. CONCLUSIONS: Assembling improves functional annotation by increasing accurate assignations and decreasing ambiguous hits between viruses and bacteria. However, the success is limited by the chimeric contigs occurring at all taxonomic levels. The assembler and its parameters should be selected based on the focus of each study. Minimo's non-chimeric contigs and Genovo's long contigs excelled in taxonomy assignation and functional annotation, respectively.tBLASTx stood out as the best approach for taxonomic annotation for virus identification. PhymmBL proved useful in datasets in which no related sequences are present as it uses genomic features that may help identify distant taxa. The k-frequencies underperformed in all viral datasets.

Ensembl 2012.

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.

Evolutionary convergence and nitrogen metabolism in Blattabacterium strain Bge, primary endosymbiont of the cockroach Blattella germanica.

Bacterial endosymbionts of insects play a central role in upgrading the diet of their hosts. In certain cases, such as aphids and tsetse flies, endosymbionts complement the metabolic capacity of hosts living on nutrient-deficient diets, while the bacteria harbored by omnivorous carpenter ants are involved in nitrogen recycling. In this study, we describe the genome sequence and inferred metabolism of Blattabacterium strain Bge, the primary Flavobacteria endosymbiont of the omnivorous German cockroach Blattella germanica. Through comparative genomics with other insect endosymbionts and free-living Flavobacteria we reveal that Blattabacterium strain Bge shares the same distribution of functional gene categories only with Blochmannia strains, the primary Gamma-Proteobacteria endosymbiont of carpenter ants. This is a remarkable example of evolutionary convergence during the symbiotic process, involving very distant phylogenetic bacterial taxa within hosts feeding on similar diets. Despite this similarity, different nitrogen economy strategies have emerged in each case. Both bacterial endosymbionts code for urease but display different metabolic functions: Blochmannia strains produce ammonia from dietary urea and then use it as a source of nitrogen, whereas Blattabacterium strain Bge codes for the complete urea cycle that, in combination with urease, produces ammonia as an end product. Not only does the cockroach endosymbiont play an essential role in nutrient supply to the host, but also in the catabolic use of amino acids and nitrogen excretion, as strongly suggested by the stoichiometric analysis of the inferred metabolic network. Here, we explain the metabolic reasons underlying the enigmatic return of cockroaches to the ancestral ammonotelic state.

Genome sequence of Lactococcus garvieae UNIUD074, isolated in Italy from a lactococcosis outbreak.

Lactococcus garvieae is the etiological agent of lactococcosis disease, affecting many cultured fish species worldwide. In addition, this bacterium is currently considered a potential zoonotic microorganism since it is known to cause several opportunistic human infections. Here we present the draft genome sequence of the L. garvieae strain UNIUD074.

CROC: finding chromosomal clusters in eukaryotic genomes.

SUMMARY: There is increasing evidence showing that co-expression of genes that cluster along the genome is a common characteristic of eukaryotic transcriptomes. Several algorithms have been used to date in the identification of these kinds of gene organization. Here, we present a web tool called CROC that aims to help in the identification and analysis of genomic gene clusters. This method has been successfully used before in the identification of chromosomal clusters in different eukaryotic species. AVAILABILITY: The web server is freely available to non-commercial users at the following address: http://metagenomics.uv.es/CROC/.

Environmental distribution of prokaryotic taxa.

BACKGROUND: The increasing availability of gene sequences of prokaryotic species in samples extracted from all kind of locations allows addressing the study of the influence of environmental patterns in prokaryotic biodiversity. We present a comprehensive study to address the potential existence of environmental preferences of prokaryotic taxa and the commonness of the specialist and generalist strategies. We also assessed the most significant environmental factors shaping the environmental distribution of taxa. RESULTS: We used 16S rDNA sequences from 3,502 sampling experiments in natural and artificial sources. These sequences were taxonomically assigned, and the corresponding samples were also classified into a hierarchical classification of environments. We used several statistical methods to analyze the environmental distribution of taxa. Our results indicate that environmental specificity is not very common at the higher taxonomic levels (phylum to family), but emerges at lower taxonomic levels (genus and species). The most selective environmental characteristics are those of animal tissues and thermal locations. Salinity is another very important factor for constraining prokaryotic diversity. On the other hand, soil and freshwater habitats are the less restrictive environments, harboring the largest number of prokaryotic taxa. All information on taxa, samples and environments is provided at the envDB online database, http://metagenomics.uv.es/envDB. CONCLUSIONS: This is, as far as we know, the most comprehensive assessment of the distribution and diversity of prokaryotic taxa and their associations with different environments. Our data indicate that we are still far from characterizing prokaryotic diversity in any environment, except, perhaps, for human tissues such as the oral cavity and the vagina.

Designing an intuitive web application for drug discovery scientists.

We discuss how we designed the Open Targets Platform (www.targetvalidation.org), an intuitive application for bench scientists working in early drug discovery. To meet the needs of our users, we applied lean user experience (UX) design methods: we started engaging with users very early and carried out research, design and evaluation activities within an iterative development process. We also emphasize the collaborative nature of applying lean UX design, which we believe is a foundation for success in this and many other scientific projects.