FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards.

The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition of specific metrics. However, automatic FAIRness assessment is still difficult because computational evaluations frequently require technical expertise and can be time-consuming. As a first step to address these issues, we propose FAIR-Checker, a web-based tool to assess the FAIRness of metadata presented by digital resources. FAIR-Checker offers two main facets: a "Check" module providing a thorough metadata evaluation and recommendations, and an "Inspect" module which assists users in improving metadata quality and therefore the FAIRness of their resource. FAIR-Checker leverages Semantic Web standards and technologies such as SPARQL queries and SHACL constraints to automatically assess FAIR metrics. Users are notified of missing, necessary, or recommended metadata for various resource categories. We evaluate FAIR-Checker in the context of improving the FAIRification of individual resources, through better metadata, as well as analyzing the FAIRness of more than 25 thousand bioinformatics software descriptions.

Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems.

Data analysis pipelines are now established as an effective means for specifying and executing bioinformatics data analysis and experiments. While scripting languages, particularly Python, R and notebooks, are popular and sufficient for developing small-scale pipelines that are often intended for a single user, it is now widely recognized that they are by no means enough to support the development of large-scale, shareable, maintainable and reusable pipelines capable of handling large volumes of data and running on high performance computing clusters. This review outlines the key requirements for building large-scale data pipelines and provides a mapping of existing solutions that fulfill them. We then highlight the benefits of using scientific workflow systems to get modular, reproducible and reusable bioinformatics data analysis pipelines. We finally discuss current workflow reuse practices based on an empirical study we performed on a large collection of workflows.

Bioimage analysis workflows: community resources to navigate through a complex ecosystem.

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.

biotoolsSchema: a formalized schema for bioinformatics software description.

BACKGROUND: Life scientists routinely face massive and heterogeneous data analysis tasks and must find and access the most suitable databases or software in a jungle of web-accessible resources. The diversity of information used to describe life-scientific digital resources presents an obstacle to their utilization. Although several standardization efforts are emerging, no information schema has been sufficiently detailed to enable uniform semantic and syntactic description-and cataloguing-of bioinformatics resources. FINDINGS: Here we describe biotoolsSchema, a formalized information model that balances the needs of conciseness for rapid adoption against the provision of rich technical information and scientific context. biotoolsSchema results from a series of community-driven workshops and is deployed in the bio.tools registry, providing the scientific community with >17,000 machine-readable and human-understandable descriptions of software and other digital life-science resources. We compare our approach to related initiatives and provide alignments to foster interoperability and reusability. CONCLUSIONS: biotoolsSchema supports the formalized, rigorous, and consistent specification of the syntax and semantics of bioinformatics resources, and enables cataloguing efforts such as bio.tools that help scientists to find, comprehend, and compare resources. The use of biotoolsSchema in bio.tools promotes the FAIRness of research software, a key element of open and reproducible developments for data-intensive sciences.

Location of intracranial aneurysms is the main factor associated with rupture in the ICAN population.

BACKGROUND AND PURPOSE: The ever-growing availability of imaging led to increasing incidentally discovered unruptured intracranial aneurysms (UIAs). We leveraged machine-learning techniques and advanced statistical methods to provide new insights into rupture intracranial aneurysm (RIA) risks. METHODS: We analysed the characteristics of 2505 patients with intracranial aneurysms (IA) discovered between 2016 and 2019. Baseline characteristics, familial history of IA, tobacco and alcohol consumption, pharmacological treatments before the IA diagnosis, cardiovascular risk factors and comorbidities, headaches, allergy and atopy, IA location, absolute IA size and adjusted size ratio (aSR) were analysed with a multivariable logistic regression (MLR) model. A random forest (RF) method globally assessed the risk factors and evaluated the predictive capacity of a multivariate model. RESULTS: Among 994 patients with RIA (39.7%) and 1511 patients with UIA (60.3 %), the MLR showed that IA location appeared to be the most significant factor associated with RIA (OR, 95% CI: internal carotid artery, reference; middle cerebral artery, 2.72, 2.02-3.58; anterior cerebral artery, 4.99, 3.61-6.92; posterior circulation arteries, 6.05, 4.41-8.33). Size and aSR were not significant factors associated with RIA in the MLR model and antiplatelet-treatment intake patients were less likely to have RIA (OR: 0.74; 95% CI: 0.55-0.98). IA location, age, following by aSR were the best predictors of RIA using the RF model. CONCLUSIONS: The location of IA is the most consistent parameter associated with RIA. The use of 'artificial intelligence' RF helps to re-evaluate the contribution and selection of each risk factor in the multivariate model.

Prediction of Unruptured Intracranial Aneurysm Evolution: The UCAN Project.

BACKGROUND: Management of small (<7 mm) unruptured intracranial aneurysms (UIA) remains controversial. Retrospective studies have suggested that post gadolinium arterial wall enhancement (AWE) of UIA on magnetic resonance imaging (MRI) may reflect aneurysm wall instability, and hence may highlight a higher risk of UIA growth. This trial aims at exploring wall imaging findings of UIAs with consecutive follow-up to substantiate these assumptions. OBJECTIVE: To develop diagnostic and predictive tools for the risk of IA evolution. Our aim is to demonstrate in clinical practice the predictive value of AWE for UIA growth. The growth will be determined by any modification of the UIA measurement. UIA growth and the UIA wall enhancement will be assessed in consensus by 2 expert neuroradiologists. METHODS: The French prospective UCAN project is a noninterventional international wide and multicentric cohort. UIA of bifurcation between 3 and 7 mm for whom a clinical and imaging follow-up without occlusion treatment was scheduled by local multidisciplinary staff will be included. Extensive clinical, biological, and imaging data will be recorded during a 3-yr follow-up. EXPECTED OUTCOMES: Discovering to improve the efficiency of UIA follow-up by identifying additional clinical, imaging, biological, and anatomic risk factors of UIA growth. DISCUSSION: A prospective nationwide recruitment allows for the inclusion of a large cohort of patients with UIA. It will combine clinical phenotyping and specific imaging with AWE screening. It will enable to exploit metadata and to explore some pathophysiological pathways by crossing clinical, genetic, biological, and imaging information.

Whole-exon sequencing of human myeloma cell lines shows mutations related to myeloma patients at relapse with major hits in the DNA regulation and repair pathways.

BACKGROUND: Human myeloma cell lines (HMCLs) are widely used for their representation of primary myeloma cells because they cover patient diversity, although not fully. Their genetic background is mostly undiscovered, and no comprehensive study has ever been conducted in order to reveal those details. METHODS: We performed whole-exon sequencing of 33 HMCLs, which were established over the last 50 years in 12 laboratories. Gene expression profiling and drug testing for the 33 HMCLs are also provided and correlated to exon-sequencing findings. RESULTS: Missense mutations were the most frequent hits in genes (92%). HMCLs harbored between 307 and 916 mutations per sample, with TP53 being the most mutated gene (67%). Recurrent bi-allelic losses were found in genes involved in cell cycle regulation (RB1, CDKN2C), the NFκB pathway (TRAF3, BIRC2), and the p53 pathway (TP53, CDKN2A). Frequency of mutations/deletions in HMCLs were either similar to that of patients (e.g., DIS3, PRDM1, KRAS) or highly increased (e.g., TP53, CDKN2C, NRAS, PRKD2). MAPK was the most altered pathway (82% of HMCLs), mainly by RAS mutants. Surprisingly, HMCLs displayed alterations in epigenetic (73%) and Fanconi anemia (54%) and few alterations in apoptotic machinery. We further identified mutually exclusive and associated mutations/deletions in genes involved in the MAPK and p53 pathways as well as in chromatin regulator/modifier genes. Finally, by combining the gene expression profile, gene mutation, gene deletion, and drug response, we demonstrated that several targeted drugs overcome or bypass some mutations. CONCLUSIONS: With this work, we retrieved genomic alterations of HMCLs, highlighting that they display numerous and unprecedented abnormalities, especially in DNA regulation and repair pathways. Furthermore, we demonstrate that HMCLs are a reliable model for drug screening for refractory patients at diagnosis or at relapse.

Understanding the Pathophysiology of Intracranial Aneurysm: The ICAN Project.

BACKGROUND: Understanding the pathophysiologic mechanism of intracranial aneurysm (IA) formation is a prerequisite to assess the potential risk of rupture. Nowadays, there are neither reliable biomarkers nor diagnostic tools to predict the formation or the evolution of IA. Increasing evidence suggests a genetic component of IA but genetics studies have failed to identify genetic variation causally related to IA. OBJECTIVE: To develop diagnostic and predictive tools for the risk of IA formation and rupture. METHODS: The French ICAN project is a noninterventional nationwide and multicentric research program. Each typical IA of bifurcation will be included. For familial forms, further IA screening will be applied among first-degree relatives. By accurate phenotype description with high-throughput genetic screening, we aim to identify new genes involved in IA. These potential genetic markers will be tested in large groups of patients. Any relevant pathway identified will be further explored in a large cohort of sporadic carriers of IA, which will be well documented with clinical, biological, and imaging data. EXPECTED OUTCOMES: Discovering genetic risk factors, better understanding the pathophysiology, and identifying molecular mechanisms responsible for IA formation will be essential bases for the development of biomarkers and identification of therapeutic targets. DISCUSSION: Our protocol has many assets. A nationwide recruitment allows for the inclusion of large pedigrees with familial forms of IA. It will combine accurate phenotyping and comprehensive imaging with high-throughput genetic screening. Last, it will enable exploiting metadata to explore new pathophysiological pathways of interest by crossing clinical, genetic, biological, and imaging information.

OntoVIP: an ontology for the annotation of object models used for medical image simulation.

This paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology, that facilitates the consistent integration of the various modules extracted from several existing ontologies, i.e. FMA, PATO, MPATH, RadLex and ChEBI. Emphasis is put on methodology for achieving this extraction and integration. The most salient aspects of the ontology are presented, especially the organization in model layers, as well as its use to browse and query the model repository.

A virtual imaging platform for multi-modality medical image simulation.

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workflow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011.

NeuroLOG: sharing neuroimaging data using an ontology-based federated approach.

This paper describes the design of the NeuroLOG middleware data management layer, which provides a platform to share heterogeneous and distributed neuroimaging data using a federated approach. The semantics of shared information is captured through a multi-layer application ontology and a derived Federated Schema used to align the heterogeneous database schemata from different legacy repositories. The system also provides a facility to translate the relational data into a semantic representation that can be queried using a semantic search engine thus enabling the exploitation of knowledge embedded in the ontology. This work shows the relevance of the distributed approach for neurosciences data management. Although more complex than a centralized approach, it is also more realistic when considering the federation of large data sets, and open strong perspectives to implement multi-centric neurosciences studies.

Grid-wide neuroimaging data federation in the context of the NeuroLOG project.

Grid technologies are appealing to deal with the challenges raised by computational neurosciences and support multi-centric brain studies. However, core grids middleware hardly cope with the complex neuroimaging data representation and multi-layer data federation needs. Moreover, legacy neuroscience environments need to be preserved and cannot be simply superseded by grid services. This paper describes the NeuroLOG platform design and implementation, shedding light on its Data Management Layer. It addresses the integration of brain image files, associated relational metadata and neuroscience semantic data in a heterogeneous distributed environment, integrating legacy data managers through a mediation layer.

A distributed security policy for neuroradiology data sharing.

Grids are key technologies to federate data distributed in multiple neuroscience centers, thus enabling large scale multi-centric studies. However, the take up of these technologies is slow due to the difficulty to manipulate sensitive neuroradiological data in an open environment and the recognized risk of federated sites to loose control over their valuable data. In this paper we propose a distributed data access control policy, enabling the federation of existing data stores, where local security policies prevail, to supports multi-centric neuroscience studies. It achieves a compromise between enabling collaborative work through data sharing and preventing unauthorized access to data in a competitive environment.

NeuroLOG: a community-driven middleware design.

The NeuroLOG project designs an ambitious neurosciences middleware, gaining from many existing components and learning from past project experiences. It is targeting a focused application area and adopting a user-centric perspective to meet the neuroscientists expectations. It aims at fostering the adoption of HealthGrids in a pre-clinical community. This paper details the project's design study and methodology which were proposed to achieve the integration of heterogeneous site data schemas and the definition of a site-centric policy. The NeuroLOG middleware will bridge HealthGrid and local resources to match user desires to control their resources and provide a transitional model towards HealthGrids.