"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling.

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.

Community curation of bioinformatics software and data resources.

The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools.

Disentangling the complexity of low complexity proteins.

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. SHORT ABSTRACT: There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

PlaToLoCo: the first web meta-server for visualization and annotation of low complexity regions in proteins.

Low complexity regions (LCRs) in protein sequences are characterized by a less diverse amino acid composition compared to typically observed sequence diversity. Recent studies have shown that LCRs may co-occur with intrinsically disordered regions, are highly conserved in many organisms, and often play important roles in protein functions and in diseases. In previous decades, several methods have been developed to identify regions with LCRs or amino acid bias, but most of them as stand-alone applications and currently there is no web-based tool which allows users to explore LCRs in protein sequences with additional functional annotations. We aim to fill this gap by providing PlaToLoCo - PLAtform of TOols for LOw COmplexity-a meta-server that integrates and collects the output of five different state-of-the-art tools for discovering LCRs and provides functional annotations such as domain detection, transmembrane segment prediction, and calculation of amino acid frequencies. In addition, the union or intersection of the results of the search on a query sequence can be obtained. By developing the PlaToLoCo meta-server, we provide the community with a fast and easily accessible tool for the analysis of LCRs with additional information included to aid the interpretation of the results. The PlaToLoCo platform is available at: http://platoloco.aei.polsl.pl/.

BioCIDER: a Contextualisation InDEx for biological Resources discovery.

SUMMARY: The vast, uncoordinated proliferation of bioinformatics resources (databases, software tools, training materials etc.) makes it difficult for users to find them. To facilitate their discovery, various services are being developed to collect such resources into registries. We have developed BioCIDER, which, rather like online shopping 'recommendations', provides a contextualization index to help identify biological resources relevant to the content of the sites in which it is embedded. AVAILABILITY AND IMPLEMENTATION: BioCIDER (www.biocider.org) is an open-source platform. Documentation is available online (https://goo.gl/Klc51G), and source code is freely available via GitHub (https://github.com/BioCIDER). The BioJS widget that enables websites to embed contextualization is available from the BioJS registry (http://biojs.io/). All code is released under an MIT licence. CONTACT: carlos.horro@earlham.ac.uk or rafael.jimenez@elixir-europe.org or manuel@repositive.io.

The functional annotation of mammalian genomes: the challenge of phenotyping.

The mouse is central to the goal of establishing a comprehensive functional annotation of the mammalian genome that will help elucidate various human disease genes and pathways. The mouse offers a unique combination of attributes, including an extensive genetic toolkit that underpins the creation and analysis of models of human disease. An international effort to generate mutations for every gene in the mouse genome is a first and essential step in this endeavor. However, the greater challenge will be the determination of the phenotype of every mutant. Large-scale phenotyping for genome-wide functional annotation presents numerous scientific, infrastructural, logistical, and informatics challenges. These include the use of standardized approaches to phenotyping procedures for the population of unified databases with comparable data sets. The ultimate goal is a comprehensive database of molecular interventions that allows us to create a framework for biological systems analysis in the mouse on which human biology and disease networks can be revealed.

Human Variome Project Quality Assessment Criteria for Variation Databases.

Numerous databases containing information about DNA, RNA, and protein variations are available. Gene-specific variant databases (locus-specific variation databases, LSDBs) are typically curated and maintained for single genes or groups of genes for a certain disease(s). These databases are widely considered as the most reliable information source for a particular gene/protein/disease, but it should also be made clear they may have widely varying contents, infrastructure, and quality. Quality is very important to evaluate because these databases may affect health decision-making, research, and clinical practice. The Human Variome Project (HVP) established a Working Group for Variant Database Quality Assessment. The basic principle was to develop a simple system that nevertheless provides a good overview of the quality of a database. The HVP quality evaluation criteria that resulted are divided into four main components: data quality, technical quality, accessibility, and timeliness. This report elaborates on the developed quality criteria and how implementation of the quality scheme can be achieved. Examples are provided for the current status of the quality items in two different databases, BTKbase, an LSDB, and ClinVar, a central archive of submissions about variants and their clinical significance.

Post-publication sharing of data and tools.

Despite existing guidelines on access to data and bioresources, good practice is not widespread. A meeting of mouse researchers in Rome proposes ways to promote a culture of sharing.

Analyzing gene expression data in mice with the Neuro Behavior Ontology.

We have applied the Neuro Behavior Ontology (NBO), an ontology for the annotation of behavioral gene functions and behavioral phenotypes, to the annotation of more than 1,000 genes in the mouse that are known to play a role in behavior. These annotations can be explored by researchers interested in genes involved in particular behaviors and used computationally to provide insights into the behavioral phenotypes resulting from differences in gene expression. We developed the OntoFUNC tool and have applied it to enrichment analyses over the NBO to provide high-level behavioral interpretations of gene expression datasets. The resulting increase in the number of gene annotations facilitates the identification of behavioral or neurologic processes by assisting the formulation of hypotheses about the relationships between gene, processes, and phenotypic manifestations resulting from behavioral observations.

NucleoFinder: a statistical approach for the detection of nucleosome positions.

MOTIVATION: The identification of nucleosomes along the chromatin is key to understanding their role in the regulation of gene expression and other DNA-related processes. However, current experimental methods (MNase-ChIP, MNase-Seq) sample nucleosome positions from a cell population and contain biases, making thus the precise identification of individual nucleosomes not straightforward. Recent works have only focused on the first point, where noise reduction approaches have been developed to identify nucleosome positions. RESULTS: In this article, we propose a new approach, termed NucleoFinder, that addresses both the positional heterogeneity across cells and experimental biases by seeking nucleosomes consistently positioned in a cell population and showing a significant enrichment relative to a control sample. Despite the absence of validated dataset, we show that our approach (i) detects fewer false positives than two other nucleosome calling methods and (ii) identifies two important features of the nucleosome organization (the nucleosome spacing downstream of active promoters and the enrichment/depletion of GC/AT dinucleotides at the centre of in vitro nucleosomes) with equal or greater ability than the other two methods.

Functional modelling of planar cell polarity: an approach for identifying molecular function.

BACKGROUND: Cells in some tissues acquire a polarisation in the plane of the tissue in addition to apical-basal polarity. This polarisation is commonly known as planar cell polarity and has been found to be important in developmental processes, as planar polarity is required to define the in-plane tissue coordinate system at the cellular level. RESULTS: We have built an in-silico functional model of cellular polarisation that includes cellular asymmetry, cell-cell signalling and a response to a global cue. The model has been validated and parameterised against domineering non-autonomous wing hair phenotypes in Drosophila. CONCLUSIONS: We have carried out a systematic comparison of in-silico polarity phenotypes with patterns observed in vivo under different genetic manipulations in the wing. This has allowed us to classify the specific functional roles of proteins involved in generating cell polarity, providing new hypotheses about their specific functions, in particular for Pk and Dsh. The predictions from the model allow direct assignment of functional roles of genes from genetic mosaic analysis of Drosophila wings.

Towards BioDBcore: a community-defined information specification for biological databases.

The present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological databases.

An evaluation of von Willebrand factor (recombinant) therapy for adult patients living with severe type 3 von Willebrand disease.

INTRODUCTION: Von Willebrand Factor (VWF) containing concentrates have been used for the treatment of von Willebrand Disease (VWD) for many years. Recently, however, a novel recombinant VWF (rVWF or vonicog alpha, VONVENDI [US], VEYVONDI [Europe]) has arrived to the market for the treatment of VWD. Initially, rVWF was approved by the U.S. Food and Drug Administration (FDA) for the on-demand treatment and control of bleeding episodes and for the perioperative management of bleeding for patients with VWD. More recently, however, the FDA has approved rVWF for routine prophylaxis to prevent bleeding episodes for those patients with severe type 3 VWD receiving on-demand therapy. AREAS COVERED: This review will focus on recent phase III trial results from NCT02973087 regarding the use of long-term routine twice weekly prophylaxis with rVWF for the prevention of bleed events in patients with severe type 3 VWD. EXPERT OPINION: A novel rVWF concentrate may have greater hemostatic potential over prior plasma-derived VWF concentrates and is now FDA approved for use in routine prophylaxis for patients with severe type 3 VWD in the United States. This greater hemostatic potential may be due to the presence of ultra-large VWF multimers and a more favorable high-molecular-weight multimer pattern compared to prior pdVWF concentrates.

Integration of global resources for human genetic variation and disease.

There is an increasing accumulation of data on disease-related mutations and associated phenotypes in a wide variety of databases worldwide. Exploiting these data in the context of whole genome sequencing is inhibited because the phenotype information in these databases is often difficult to search meaningfully or relate between data sets, and automated computational integration is not possible. Key to this integration is the development of ontology-based methods for describing diseases in terms of their component phenotypes. This would allow analysis of variation in disease manifestation, relationships between diseases and phenotypes in model organisms, and linking diseases to gene mutations, pathways, and phenotypes. Building a systematic link to phenotypes manifested in model organisms will be of particular importance with the advent of new, large-scale phenotyping projects such as the International Mouse Phenotyping Consortium. In addition to improved semantic description, funding and organizational innovations are required to support this integration. In particular, a series of national or international hubs to hold genotype and phenotype data are needed which could feed data to a central database. In addition, better coordination of clinical and bioinformatics experts and, crucially, development of a transnational funding and international coordination infrastructure will be required.

MouseBook: an integrated portal of mouse resources.

The MouseBook (http://www.mousebook.org) databases and web portal provide access to information about mutant mouse lines held as live or cryopreserved stocks at MRC Harwell. The MouseBook portal integrates curated information from the MRC Harwell stock resource, and other Harwell databases, with information from external data resources to provide value-added information above and beyond what is available through other routes such as International Mouse Stain Resource (IMSR). MouseBook can be searched either using an intuitive Google style free text search or using the Mammalian Phenotype (MP) ontology tree structure. Text searches can be on gene, allele, strain identifier (e.g. MGI ID) or phenotype term and are assisted by automatic recognition of term types and autocompletion of gene and allele names covered by the database. Results are returned in a tabbed format providing categorized results identified from each of the catalogs in MouseBook. Individual result lines from each catalog include information on gene, allele, chromosomal location and phenotype, and provide a simple click-through link to further information as well as ordering the strain. The infrastructure underlying MouseBook has been designed to be extensible, allowing additional data sources to be added and enabling other sites to make their data directly available through MouseBook.

EuroPhenome: a repository for high-throughput mouse phenotyping data.

The broad aim of biomedical science in the postgenomic era is to link genomic and phenotype information to allow deeper understanding of the processes leading from genomic changes to altered phenotype and disease. The EuroPhenome project (http://www.EuroPhenome.org) is a comprehensive resource for raw and annotated high-throughput phenotyping data arising from projects such as EUMODIC. EUMODIC is gathering data from the EMPReSSslim pipeline (http://www.empress.har.mrc.ac.uk/) which is performed on inbred mouse strains and knock-out lines arising from the EUCOMM project. The EuroPhenome interface allows the user to access the data via the phenotype or genotype. It also allows the user to access the data in a variety of ways, including graphical display, statistical analysis and access to the raw data via web services. The raw phenotyping data captured in EuroPhenome is annotated by an annotation pipeline which automatically identifies statistically different mutants from the appropriate baseline and assigns ontology terms for that specific test. Mutant phenotypes can be quickly identified using two EuroPhenome tools: PhenoMap, a graphical representation of statistically relevant phenotypes, and mining for a mutant using ontology terms. To assist with data definition and cross-database comparisons, phenotype data is annotated using combinations of terms from biological ontologies.

Matching mouse models to specific human liver disease states by comparative functional genomics of mouse and human datasets.

Omics has broadened our view of transcriptional and gene regulatory networks of multifactorial diseases, such as metabolism associated liver disease and its advanced stages including hepatocellular carcinoma. Identifying liver disease biomarkers and potential treatment targets makes use of experimental models, e.g. genetically engineered mice, which show molecular features of human pathologies but are experimentally tractable. We compared gene expression profiling data from human to our studies on transgenic mice with hepatocyte deletion of Cyp51 from cholesterol synthesis with the aim of identifying the human liver disease state best matched by the Cyp51 knockout model. Gene Expression Omnibus was used to identify relevant human datasets. We identified enriched and deregulated genes, pathways and transcription factors of mouse and human disease samples. Analysis showed a closer match of the Cyp51 knockout to the female patient samples. Importantly, CYP51 was depleted in both mouse and female human data. Among the enriched genes were the oxysterol-binding protein-related protein 3 (OSBPL3), which was enriched in all datasets, and the collagen gene COL1A2, which was enriched in both the mouse and one human dataset. KEGG and Reactome analyses revealed the most enriched pathway to be ECM-receptor interaction. Numerous transcription factors were differentially expressed in mice of both sexes and in the human female dataset, while depleted HNF4α and RXRα:PPARα-isoform1 were a hallmark in all cases. Our analysis exposed novel potential biomarkers, which may provide new avenues towards more personalized approaches and different targets in females and males. The analysis was only possible because of availability of open data resources and tools and broadly consistent annotation.

Systems Biology in ELIXIR: modelling in the spotlight.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Protein coalitions in a core mammalian biochemical network linked by rapidly evolving proteins.

BACKGROUND: Cellular ATP levels are generated by glucose-stimulated mitochondrial metabolism and determine metabolic responses, such as glucose-stimulated insulin secretion (GSIS) from the ß-cells of pancreatic islets. We describe an analysis of the evolutionary processes affecting the core enzymes involved in glucose-stimulated insulin secretion in mammals. The proteins involved in this system belong to ancient enzymatic pathways: glycolysis, the TCA cycle and oxidative phosphorylation. RESULTS: We identify two sets of proteins, or protein coalitions, in this group of 77 enzymes with distinct evolutionary patterns. Members of the glycolysis, TCA cycle, metabolite transport, pyruvate and NADH shuttles have low rates of protein sequence evolution, as inferred from a human-mouse comparison, and relatively high rates of evolutionary gene duplication. Respiratory chain and glutathione pathway proteins evolve faster, exhibiting lower rates of gene duplication. A small number of proteins in the system evolve significantly faster than co-pathway members and may serve as rapidly evolving adapters, linking groups of co-evolving genes. CONCLUSIONS: Our results provide insights into the evolution of the involved proteins. We find evidence for two coalitions of proteins and the role of co-adaptation in protein evolution is identified and could be used in future research within a functional context.

ELIXIR and Toxicology: a community in development.

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.