GA4GH Passport standard for digital identity and access permissions.

The Global Alliance for Genomics and Health (GA4GH) supports international standards that enable a federated data sharing model for the research community while respecting data security, ethical and regulatory frameworks, and data authorization and access processes for sensitive data. The GA4GH Passport standard (Passport) defines a machine-readable digital identity that conveys roles and data access permissions (called "visas") for individual users. Visas are issued by data stewards, including data access committees (DACs) working with public databases, the entities responsible for the quality, integrity, and access arrangements for the datasets in the management of human biomedical data. Passports streamline management of data access rights across data systems by using visas that present a data user's digital identity and permissions across organizations, tools, environments, and services. We describe real-world implementations of the GA4GH Passport standard in use cases from ELIXIR Europe, National Institutes of Health, and the Autism Sharing Initiative. These implementations demonstrate that the Passport standard has provided transparent mechanisms for establishing permissions and authorizing data access across platforms.

International federation of genomic medicine databases using GA4GH standards.

We promote a shared vision and guide for how and when to federate genomic and health-related data sharing, enabling connections and insights across independent, secure databases. The GA4GH encourages a federated approach wherein data providers have the mandate and resources to share, but where data cannot move for legal or technical reasons. We recommend a federated approach to connect national genomics initiatives into a global network and precision medicine resource.

Empirical validation of an automated approach to data use oversight.

The current paradigm for data use oversight of biomedical datasets is onerous, extending the timescale and resources needed to obtain access for secondary analyses, thus hindering scientific discovery. For a researcher to utilize a controlled-access dataset, a data access committee must review her research plans to determine whether they are consistent with the data use limitations (DULs) specified by the informed consent form. The newly created GA4GH data use ontology (DUO) holds the potential to streamline this process by making data use oversight computable. Here, we describe an open-source software platform, the Data Use Oversight System (DUOS), that connects with DUO terminology to enable automated data use oversight. We analyze dbGaP data acquired since 2006, finding an exponential increase in data access requests, which will not be sustainable with current manual oversight review. We perform an empirical evaluation of DUOS and DUO on selected datasets from the Broad Institute's data repository. We were able to structure 118/123 of the evaluated DULs (96%) and 52/52 (100%) of research proposals using DUO terminology, and we find that DUOS' automated data access adjudication in all cases agreed with the DAC manual review. This first empirical evaluation of the feasibility of automated data use oversight demonstrates comparable accuracy to human-based data access oversight in real-world data governance.

Author Correction: Leveraging European infrastructures to access 1 million human genomes by 2022.

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

The bio.tools registry of software tools and data resources for the life sciences.

Bioinformaticians and biologists rely increasingly upon workflows for the flexible utilization of the many life science tools that are needed to optimally convert data into knowledge. We outline a pan-European enterprise to provide a catalogue ( https://bio.tools ) of tools and databases that can be used in these workflows. bio.tools not only lists where to find resources, but also provides a wide variety of practical information.

Leveraging European infrastructures to access 1 million human genomes by 2022.

Human genomics is undergoing a step change from being a predominantly research-driven activity to one driven through health care as many countries in Europe now have nascent precision medicine programmes. To maximize the value of the genomic data generated, these data will need to be shared between institutions and across countries. In recognition of this challenge, 21 European countries recently signed a declaration to transnationally share data on at least 1 million human genomes by 2022. In this Roadmap, we identify the challenges of data sharing across borders and demonstrate that European research infrastructures are well-positioned to support the rapid implementation of widespread genomic data access.

Common ELIXIR Service for Researcher Authentication and Authorisation.

A common Authentication and Authorisation Infrastructure (AAI) that would allow single sign-on to services has been identified as a key enabler for European bioinformatics. ELIXIR AAI is an ELIXIR service portfolio for authenticating researchers to ELIXIR services and assisting these services on user privileges during research usage. It relieves the scientific service providers from managing the user identities and authorisation themselves, enables the researcher to have a single set of credentials to all ELIXIR services and supports meeting the requirements imposed by the data protection laws. ELIXIR AAI was launched in late 2016 and is part of the ELIXIR Compute platform portfolio. By the end of 2017 the number of users reached 1000, while the number of relying scientific services was 36. This paper presents the requirements and design of the ELIXIR AAI and the policies related to its use, and how it can be used for serving some example services, such as document management, social media, data discovery, human data access, cloud compute and training services.

Registered access: authorizing data access.

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.

Orchestrating differential data access for translational research: a pilot implementation.

BACKGROUND: Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data. METHODS: In this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements. RESULTS: Open source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility. CONCLUSIONS: Common open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.

Future opportunities and trends for e-infrastructures and life sciences: going beyond the grid to enable life science data analysis.

With the increasingly rapid growth of data in life sciences we are witnessing a major transition in the way research is conducted, from hypothesis-driven studies to data-driven simulations of whole systems. Such approaches necessitate the use of large-scale computational resources and e-infrastructures, such as the European Grid Infrastructure (EGI). EGI, one of key the enablers of the digital European Research Area, is a federation of resource providers set up to deliver sustainable, integrated and secure computing services to European researchers and their international partners. Here we aim to provide the state of the art of Grid/Cloud computing in EU research as viewed from within the field of life sciences, focusing on key infrastructures and projects within the life sciences community. Rather than focusing purely on the technical aspects underlying the currently provided solutions, we outline the design aspects and key characteristics that can be identified across major research approaches. Overall, we aim to provide significant insights into the road ahead by establishing ever-strengthening connections between EGI as a whole and the life sciences community.

iAnn: an event sharing platform for the life sciences.

SUMMARY: We present iAnn, an open source community-driven platform for dissemination of life science events, such as courses, conferences and workshops. iAnn allows automatic visualisation and integration of customised event reports. A central repository lies at the core of the platform: curators add submitted events, and these are subsequently accessed via web services. Thus, once an iAnn widget is incorporated into a website, it permanently shows timely relevant information as if it were native to the remote site. At the same time, announcements submitted to the repository are automatically disseminated to all portals that query the system. To facilitate the visualization of announcements, iAnn provides powerful filtering options and views, integrated in Google Maps and Google Calendar. All iAnn widgets are freely available. AVAILABILITY: http://iann.pro/iannviewer CONTACT: manuel.corpas@tgac.ac.uk.

Best practices in bioinformatics training for life scientists.

The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists.

Computational and functional analysis of the androgen receptor antagonist atraric acid and its derivatives.

Androgen receptor (AR) antagonists are important compounds for the treatment of prostate cancer (PCa). The atraric acid (AA), a natural compound, binds to the AR and acts as a specific AR antagonist. Interestingly, AA represents a novel chemical platform that could serve as a potential basis for new AR antagonists. Therefore, one objective of this study was to analyze the chemical/structural requirements for AR antagonism and to obtain predictions of where and how AA binds to the AR. Further, this study describes the chemical synthesis of 12 AA derivatives and their analysis using a combination of computational and functional assays. Functional analysis of AA derivatives indicated that none activated the AR. Both the para-hydroxyl group and the benzene ortho- and the meta-methyl groups of AA appeared to be essential to antagonize androgen-activated AR activity. Furthermore, extension of the hydrophobic side chain of AA led to slightly stronger AR antagonism. In silico data suggest that modifications to the basic AA structure change the hydrogen-bonding network with the AR ligand binding domain (LBD), so that the para-hydroxyl group of AA forms a hydrogen bond with the LBD, confirming the functional importance of this group for AR antagonism. Moreover, in silico modeling also suggested that the ortho- and meta- methyl groups of AA interact with hydrophobic residues of the ligand pocket of AR, which might explain their functional importance for antagonism. Thus, these studies identify the chemical groups of AA that play key roles in allowing the AA-based chemical platform to act as an AR antagonist.

Bioinformatics Training Network (BTN): a community resource for bioinformatics trainers.

Funding bodies are increasingly recognizing the need to provide graduates and researchers with access to short intensive courses in a variety of disciplines, in order both to improve the general skills base and to provide solid foundations on which researchers may build their careers. In response to the development of 'high-throughput biology', the need for training in the field of bioinformatics, in particular, is seeing a resurgence: it has been defined as a key priority by many Institutions and research programmes and is now an important component of many grant proposals. Nevertheless, when it comes to planning and preparing to meet such training needs, tension arises between the reward structures that predominate in the scientific community which compel individuals to publish or perish, and the time that must be devoted to the design, delivery and maintenance of high-quality training materials. Conversely, there is much relevant teaching material and training expertise available worldwide that, were it properly organized, could be exploited by anyone who needs to provide training or needs to set up a new course. To do this, however, the materials would have to be centralized in a database and clearly tagged in relation to target audiences, learning objectives, etc. Ideally, they would also be peer reviewed, and easily and efficiently accessible for downloading. Here, we present the Bioinformatics Training Network (BTN), a new enterprise that has been initiated to address these needs and review it, respectively, to similar initiatives and collections.

Structural basis for computational screening of non-steroidal androgen receptor ligands.

IMPORTANCE OF THE FIELD: Deep structural and chemical understanding of the protein target and computational methods for detection of receptor-selective ligands are important for the early drug discovery in the steroid receptor field. AREAS COVERED IN THIS REVIEW: This review focuses on the use of currently available structural information of the androgen receptor (AR) and known AR ligands to make computational strategies for the discovery of AR ligands in order to offer new chemical platforms for drug development. WHAT THE READER WILL GAIN: AR is a challenging target for drug discovery and modeling even if there is a wealth of experimental data available. First, only the active structure of AR is currently known, which hampers the design of AR antagonists. Second, the structural similarity between the ligand-binding sites of AR and its mutated forms and closely related steroid receptors (SRs) such as progesterone receptors presents challenges for the development of drugs with receptor-selective function. TAKE HOME MESSAGE: Research indicates that a very small chemical change in the structure of a non-steroidal ligand can cause a complete change in its activity. One source of this effect arises from binding to similar binding sites in related SRs and other proteins in the signaling pathway. Currently, computational methods are not able to predict the subtle differences between AR ligand activities but modeling does offer the possibility of generating new lead structures that might have the desired properties.

Computationally identified novel diphenyl- and phenylpyridine androgen receptor antagonist structures.

We have identified and profiled a set of androgen receptor (AR) binding compounds representing two nonsteroidal scaffolds from a public chemical database supplied by Asinex with virtual screening procedure incorporating our recently published 3D QSAR model of AR ligands. The diphenyl- and phenylpyridine-based compounds act as antagonists in wild-type AR in CV1 cells and also retain this antagonistic character in CV1 cells expressing T877A mutant receptor. This mutation is frequently associated with prostate cancer. Two of the compounds repress the androgen-dependent cell growth of LNCaP prostate cancer cells expressing the T877A AR mutant. Molecular modeling of the observed in vitro antagonism with induced fit docking suggests that W741 and M895 could be mechanistically involved in the initiation of the antagonism. The results indicate finding of nonsteroidal AR antagonist compounds from a public chemical database with computational methods. Compounds could serve as a novel platform to develop more potent AR antagonists with inhibitory activity in both wild-type and T877A mutant AR.

Small molecule designed to target metal binding site in the alpha2I domain inhibits integrin function.

Integrin alpha2beta1 is a potential target molecule in drug development. We have established "design" criteria for molecules that bind to the "closed" conformation of alpha2I domain via Mg(2+) in MIDAS (metal ion dependent adhesion site) while simultaneously forming interactions with neighboring amino acid residues. Specific tetracyclic Streptomyces products belonging to the group of aromatic polyketides fulfill our criteria and inhibit alpha2beta1 integrin. All previously described inhibitors of alphaI domain integrins act in an allosteric manner.

Docking and three-dimensional quantitative structure-activity relationship (3D QSAR) analyses of nonsteroidal progesterone receptor ligands.

We report a docking and comparative molecular similarity indices analysis (CoMSIA) study of progesterone receptor (PR) ligands with an emphasis on nonsteroids including tanaproget. The ligand alignment generation, a critical part of model building, comprised two stages. First, thorough conformational sampling of docking poses within the PR binding pocket was made with the program GOLD. Second, a strategy to select representative poses for CoMSIA was developed utilizing the FlexX scoring function. After manual replacement of five poses where this approach had problems, a significant correlation (r(2) = 0.878) between the experimental affinities and electrostatic, hydrophobic, and hydrogen bond donor properties of the aligned ligands was found. Extensive model validation was made using random-group cross-validations, external test set predictions (r(pred)(2) = 0.833), and consistency check between the CoMSIA model and the PR binding site structure. Robustness, predictive ability, and automated alignment generation make the model a potential tool for virtual screening.

SOMA--workflow for small molecule property calculations on a multiplatform computing grid.

We present the concept of the SOMA workflow developed at the Finnish IT Center for Science CSC. The SOMA workflow unites multiplatform UNIX/LINUX computing resources and third-party software for calculating molecular structure and properties. The presented workflow components consist of the computing program XML descriptions, the core workflow program Grape, the toolkit for parsing program input and output, and the extranet interface. The program Grape and the developed XML descriptions of scientific programs allow researchers to link molecular modeling software into highly sophisticated computational workflows. SOMA collects the calculated data produced by the workflow and stores the computed information in the Chemical Markup Language (CML) format. The extranet interface is used for user authentication, building of the program interfaces and the workflows, and for sorting, filtering, and visualizing the results.

Molecular evolution of adrenoceptors and dopamine receptors: implications for the binding of catecholamines.

We derived homology models for all human catecholamine-binding GPCRs (CABRs; the alpha-1, alpha-2, and beta-adrenoceptors and the D1-type and D2-type dopamine receptor) using the bovine rhodopsin-11-cis-retinal X-ray structure. Interactions were predicted from the endogenous ligands norepinephrine or dopamine and from the binding site and were used to optimize receptor-ligand interactions. Similar binding modes in the complexes agree with a large "binding core" conserved across the CABRs, that is, D3.32, V(I)3.33, T3.37, S5.42, S(A/C)5.43, S5.46, F6.51, F6.52, and W6.48. Model structures and docking simulations suggest that extracellular loop 2 could provide a common attachment point for the ligands' beta-hydroxyl via a hydrogen bond donated by the main-chain NH group of residue xl2.52. The modeled CABRs and docking modes are in good agreement with published experimental studies. Complementarity between the ligand and the binding site suggests that the bovine rhodopsin structure is a suitable template for modeling agonist-bound CABRs.