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Web-version: https://w3id.org/ro/crate/1.1 This document specifies a method, known as RO-Crate (Research Object Crate), of aggregating and describing research data with associated metadata. RO-Crates can aggregate and describe any resource including files, URI-addressable resources, or use other addressing schemes to locate digital or physical data. RO-Crates can describe data in aggregate and at the individual resource level, with metadata to aid in discovery, re-use and long term management of data. Metadata includes the ability to describe the context of data and the entities involved in its production, use and reuse. For example: who created it, using which equipment, software and workflows, under what licenses can it be re-used, where was it collected, and/or where is it about. RO-Crate uses JSON-LD to to express this metadata using linked data, describing data resources as well as contextual entities such as people, organizations, software and equipment as a series of linked JSON-LD objects - using common published vocabularies, chiefly schema.org. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.json. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples.

The IFB Catalogue is a centralized database developed as part of the IFB Distributed national environment of services in Bioinformatics. Its aim is to ensure the visibility and accessibility of the Bioinformatics resources provided and maintained by the french community, whether these are research labs or service platforms, in a structured and open database that guarantees their FAIRness. Such resources can be software tools, databases, computing resources, individual expertises, platforms, trainings and training materials. This catalogue stores the metadata describing their properties (e.g. the licence of a software tool) and linking them (e.g. the publication of a database by a given team). The primary role of the Catalogue is to help users of Bioinformatics services. Through the integration of the data in the IFB website (https://www.france-bioinformatique.fr), it provides an overview of the various resources provided by the community, and supports the needs of end-users (who can perform a given kind of analysis? where is a tool available?). It is also synchronized with related international catalogues (e.g. ELIXIR bio.tools[1] , TeSS[2]), and promotes the reusability of the data through the publication of Bioschemas[3] markup and REST APIs as well as the use of the EDAM ontology[4]. The backend server is available at https://catalogue.france-bioinformatique.fr/. The code of the Catalogue database backend is openly available on https://github.com/IFB-ElixirFr/ifbcat

Web-version: https://w3id.org/ro/crate/1.1 This document specifies a method, known as RO-Crate (Research Object Crate), of aggregating and describing research data with associated metadata. RO-Crates can aggregate and describe any resource including files, URI-addressable resources, or use other addressing schemes to locate digital or physical data. RO-Crates can describe data in aggregate and at the individual resource level, with metadata to aid in discovery, re-use and long term management of data. Metadata includes the ability to describe the context of data and the entities involved in its production, use and reuse. For example: who created it, using which equipment, software and workflows, under what licenses can it be re-used, where was it collected, and/or where is it about. RO-Crate uses JSON-LD to to express this metadata using linked data, describing data resources as well as contextual entities such as people, organizations, software and equipment as a series of linked JSON-LD objects - using common published vocabularies, chiefly schema.org. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.json. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples., Recommendation published by researchobject.org - see https://w3id.org/ro/crate/1.1 for web version.

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus.This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years.A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future

Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials., Author summary Genome-wide association studies (GWAS) established numerous associations between genetic variants and human traits. The anonymized summary of GWAS results is generally made publicly available to the scientific community and can be explored further. Amongst the many possible secondary analyses, one is to study the effect of a genetic variant on several traits (multi-trait GWAS) rather than a unique trait. We compared several tests to conduct multi-trait GWAS on simulated and real data. We detected 322 new associations that were not previously reported by standard univariate GWAS. We then detected clusters of genetic variants having a similar effect across several traits. Focusing on two subsets of immunity and metabolism traits, we demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster and success of drug targets in randomized controlled trials. We propose this method for improving the functional interpretation of GWAS results.

International audience; EDAM is an ontology widely used to describe bioinformatics resources (tools, databases, workflows, training, etc.). However, the exploration of the ontology itself by users who might not be ontology experts remains sub-optimal, with most of the usual interfaces being either too complex, too slow, or too generic.Here we present EDAM Browser, which lets users explore EDAM with an interface tailored to its structure and properties. The features of this ontology browser are focused on the needs of EDAM's end-users, who search and explore the ontology to find or annotate bioinformatics resources.A unique feature of EDAM Browser is the visualization of all ""positions"" of a selected concept in the full ontology ""tree"" (DAG), with all paths to the root highlighted. One of EDAM Browser's main added values is the aggregated search across various registries (e.g. bio.tools, TeSS, BioSphere), and its speed.The lightweight architecture makes it easy to download and run EDAM Browser on any server or personal computer, either as a local HTML file or on a web server. EDAM Browser has also been used for other ontologies, and making the reuse smoother and more usable is part of the ongoing work. Contributions are welcome!

Workflows are the keystone of bioimage analysis, and the NEUBIAS (Network of European BioImage AnalystS) community is trying to gather the actors of this field and organize the information around them. One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows and associated resources. In this paper we want to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows. The emergence of "big data” in bioimaging and resource-intensive analysis algorithms make local data storage and computing solutions a limiting factor. At the same time, the need for data sharing with collaborators and a general shift towards remote work, have created new challenges and avenues for the execution and sharing of bioimage analysis workflows. These challenges are to reproducibly run workflows in remote environments, in particular when their components come from different software packages, but also to document them and link their parameters and results by following the FAIR principles (Findable, Accessible, Interoperable, Reusable) to foster open and reproducible science. In this opinion paper, we focus on giving some directions to the reader to tackle these challenges and navigate through this complex ecosystem, in order to find and use workflows, and to compare workflows addressing the same problem. We also discuss tools to run workflows in the cloud and on High Performance Computing resources, and suggest ways to make these workflows FAIR. publishedVersion

Extended abstract, submitted to Special issue on Canonical Workflow Frameworks for Research in the journal Data Intelligence., The practice of performing computational processes using workflows has taken hold in the biosciences as the discipline becomes increasingly computational. The COVID-19 pandemic has spotlighted the importance of systematic and shared analysis of SARS-CoV-2 and its data processing pipelines. This is coupled with a drive in the community towards adopting FAIR practices (Findable, Accessible, Interoperable, and Reusable) not just for data, but also for workflows, and to improve the reproducibility of processes, both manual and computational. EOSC-Life brings together 13 of the Life Science ‘ESFRI’ research infrastructures to create an open, digital and collaborative space for biological and medical research. The project is developing a cloud-based workflow collaboratory to drive implementation of FAIR workflows across disciplines and RI boundaries, and foster tool- focused collaborations and reuse between communities via the sharing of data analysis workflows. The collaboratory aims to provide a framework for researchers and workflow specialists to use and reuse workflows. As such it is an example of the Canonical Workflow Frameworks for Research (CWFR) vision in practice. EOSC-Life is made up of established research infrastructures ranging from biobanking and clinical trial management, through to coordinating biomedical imaging and plant phenotyping to multi-omic and systems-based data analysis. The heterogeneity of the disciplines is reflected in the diversity of their data analysis needs and practices and the variety of workflow management systems they use. Many have specialist platforms developed over years. Workflow management systems in common use include Galaxy, Snakemake, and Nextflow, and more specialist, domain-specific systems such as SCIPION. To serve the needs of this established and diverse community, EOSC-Life has developed WorkflowHub as an inclusive workflow registry, agnostic to any Workflow Management System (WfMS). WorkflowHub aims to incorporate their workflows in partnership with the WfMS, to embed the registration of workflows in the community processes, e.g. based on pre-existing workflow repositories. The registry adopts common practices, e.g.use of GitHub repositories, and supports integration with the ecosystem of tool packages, assisted by registries (bio.tools, biocontainers), and services for testing and benchmarking workflows (OpenEBench, LifeMonitor). As an umbrella registry, the Hub makes workflows Findable and Accessible by indexing workflows across workflow management systems and their native repositories, while providing rich standardized metadata. Interoperability and Reusability is supported by standardized descriptions of workflows and packaging of workflow components, developed in close collaboration with the communities. The WorkflowHub creates a place for registering and discovering libraries of workflows developed by collaborating teams, with suitable features for versioning, credit, analytics, and import/export needed to support the reuse of workflows, the development of sub-workflows as canonical steps and ultimately the identification of common patterns in the workflows. At the heart of the collaboratory is a Digital Object framework for documenting and exchanging workflows annotated with machine processable metadata produced and consumed by the participating platforms. The Digital Object framework is founded on several needs: Describing a workflow and its steps in a canonical, normalised and WfMS independent way: we use the Common Workflow Language (CWL), more specifically the Abstract CWL (non-executable) description variant to accompany the native workflow definitions. This presents the structure, composed tools and external interface in an interoperable way across workflow languages. WfMS can generate abstract CWL, already demonstrated for Galaxy, next to the ‘native’ Galaxy workflow description. This language duality is an important retention aspect of reproducibility, as the structure and metadata of the workflow can be accessed independent of its native format as CWL, even if that may no longer be executable, capturing the canonical workflow in a FAIR format. The co-presence of the native format enables direct reuse in the specific WfMS, benefitting from all its features. Metadata about a workflow and its tools using a minimal information model: we use the Bioschemas profiles Computational Tool, Computational Workflow and Formal Parameter which are discipline independent, opinionated conventions for using schema.org annotations. Bioschemas enables us to capture and publish workflow registrations and their metadata as FAIR Digital Objects. The EDAM Ontology is further used to add bioinformatics-specific metadata, such as strong typing of inputs and outputs, within both Abstract CWL and Bioschemas annotations. Organising and packaging the definitions and components of a workflow with their associated objects such as test data: we use a Workflow profile specialisation of RO-Crate, a community developed standardised approach for research output packaging with rich metadata. RO-Crate provides us the ability to package executable workflows, their components such as example and test data, abstract CWL, diagrams and their documentation. This makes workflows more readily re-usable. RO-Crate is the base unit of upload and download at the WorkflowHub. As CWFR Digital Objects of workflows, RO-Crates are activation-ready and circulated between the different services for execution and testing. Identifiers for all the components: like FAIR Digital Objects, RO-Crates can be metadata-rich bags of identifiers and can themselves be assigned permanent identifiers. This enables the full description of a computational analysis, from input data, over tools and workflows, to final results. Using these components we have built an environment that supports the Workflow Life Cycle, from abstract description, through to a specific rendering in a WfMS to its execution and the documentation of its run provenance, results and continued testing.

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future. publishedVersion

International audience; Abstract Motivation One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets. Results Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets and extended our focus to stabilizers of PPIs as well. Availability and implementation The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files. Supplementary information Supplementary data are available at Bioinformatics online.

Abstrtact Motivation Viruses are ubiquitous in the living world, and their ability to infect more than one host defines their host range. However, information about which virus infects which host, and about which host is infected by which virus, is not readily available. Results We developed a web-based tool called the Viral Host Range database to record, analyze and disseminate experimental host range data for viruses infecting archaea, bacteria and eukaryotes. Availability and implementation The ViralHostRangeDB application is available from https://viralhostrangedb.pasteur.cloud. Its source code is freely available from the Gitlab instance of Institut Pasteur (https://gitlab.pasteur.fr/hub/viralhostrangedb).

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.