Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map.Electronic supplementary materialThe online version of this article (doi:10.1007/s12035-013-8489-4) contains supplementary material, which is available to authorized users.

COMMEnTOPEn 2 Scientific Data | (2020) 7:136 | https://doi.), an effort to build a comprehensive, standardized knowledge repository of SARS-CoV-2 virus-host interaction mechanisms, guided by input from domain experts and based on published work. This knowledge, available in the vast body of existing literature 1,2 and the fast-growing number of new SARS-CoV-2 publications, needs rigorous and efficient organization in both human and machine-readable formats.This endeavour is an open collaboration between clinical researchers, life scientists, pathway curators, computational biologists and data scientists. Currently, 162 contributors from 25 countries around the world are participating in the project, including partners from Reactome 3 , WikiPathways 4 , IMEx Consortium 5 , Pathway Commons 6 , DisGeNET 7 , ELIXIR 8 , and the Disease Maps Community 9 . With this effort, we aim for long-term community-based development of high-quality models and knowledge bases, linked to data repositories.The COVID-19 Disease Map will be a platform for visual exploration and computational analyses of molecular processes involved in SARS-CoV-2 entry, replication, and host-pathogen interactions, as well as immune response, host cell recovery and repair mechanisms. The map will support the research community and improve our understanding of this disease to facilitate the development of efficient diagnostics and therapies. Figure 1 illustrates the initial scope and layout of the map and its life cycle.At the time this Comment went to press, the COVID-19 Disease Map contains pathways of (i) the virus replication cycle and its transcription mechanisms; (ii) SARS-CoV-2 impact on ACE2-regulated pulmonary blood pressure, apoptosis, Cul2-mediated ubiquitination, heme catabolism, Interferon 2 and PAMP signalling, and endoplasmic reticulum stress; (iii) SARS-CoV-2 proteins Nsp4, Nsp6, Nsp14 and Orf3a. Moreover, the map incorporates the COVID-19 collection of WikiPathway diagrams 10 and a pre-published genome-scale metabolic model of human alveolar macrophages with SARS-CoV-2 11 . All these contributed open-access resources are referenced at https://fairdomhub.org/projects/190#models.By combining diagrammatic representation of COVID-19 mechanisms with underlying models, the map fulfils a dual role. First, it is a graphical, interactive representation of disease-relevant molecular mechanisms linking different knowledge bases. Second, it is a computational resource of reviewed content for graph-based analyses 12 and disease modelling 13 . Thus, it provides a platform for domain experts, such as clinicians, virologists, and immunologists, to collaborate with data scientists and computational biologists for a rigorous model building, accurate data interpretation and drug repositioning. It offers a shared mental map to understand gender, age, and other susceptibility features of the host, disease progression, defence mechanisms, and response to treatment. Finally, it can be used together with the maps of other human diseases to study comorbidities.In...

Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories.

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.