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Relational information between different types of entities is often modelled by a multilayer network (MLN) -- a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied this impact for several commonly occurring tasks related to MLN analysis. Additionally, layer arrangements with a dimensionality beyond 2D, which are common in this scenario, motivate the use of stereoscopic displays. We ran a human subject study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer arrangements. The study employs six analysis tasks that cover the spectrum of an MLN task taxonomy, from path finding and pattern identification to comparisons between and across layers. We found no clear overall winner. However, we explore the task-to-arrangement space and derive empirical-based recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for MLNs., Comment: IEEE Transactions on Visualization and Computer Graphics, In press, To appear. Accepted to IEEE VIS 2023

The coronavirus disease 2019 (COVID-19) pandemic has so far resulted in the infection of more than 42 million people worldwide with more than one million deaths (https://covid19.who.int). Caused by the SARS-CoV-2 virus, COVID-19 is complex and involves multiple biological pathways. To understand how COVID-19 develops, it is crucial to decipher the underlying molecular mechanisms of the SARS-CoV-2 replication cycle since all signalling cascades that culminate in the emergence of clinical symptoms derive essentially from the events encompassing the replication cycle. Aiming to provide a comprehensive resource for assisting the COVID-19 research community in this quest, we built, in collaboration with the Reactome database and University of Edinburgh teams under the COVID-19 Disease Map project, a comprehensive map of the SARS-CoV-2 replication cycle containing all sequential events making up the replication cycle: virus attachment, entry, transcription, replication, assembly and release to extracellular space. Encoded in a CellDesigner’s SBML format, our SARS-CoV-2 replication cycle map currently contains 104 reactions and 105 bioentities manually collected from 18 publications. Additionally, all entities and reactions are annotated according to the standards provided by the Minimal Information Requested In the Annotation of Models (MIRIAM). While virus attachment and entry events are based solely on the biomedical literature concerning SARS-CoV-2, the other events are based mostly on the coronaviruses SARS-CoV and MHV-A49 for which more studies are available. This map shows important features of the SARS-CoV-2 infectivity. First, cell susceptibility to SARS-CoV-2 infection depends on multiple host entry factors, namely, angiotensin-converting enzyme 2 (ACE2), cholesterol, heparan sulfate, the proteases cathepsin B (CTSB), cathepsin L (CTSL) and TMPRSS2, the proprotein convertase furin, and the transmembrane receptor neuropilin 1 (NRP1). Second, SARS-CoV-2 can enter cells either by direct fusion of the virion and cell membranes in the presence of proteases TMPRSS2 and furin or by endocytosis in their absence. Third, SARS-CoV-2 replication and transcription take place within double-membrane vesicles (DMVs) that protect intermediate double-stranded RNAs from the cell innate immunity sensors. We expect that this map, along with the other maps under the COVID-19 Disease Map project, can be both a standardised knowledge repository for the COVID-19 and coronaviruses research communities as well as a hypothesis-generating resource for new discoveries concerning the crosstalk between SARS-CoV-2 replication cycle and host cell signalling pathways.

Summary The analysis of biological networks is increasingly important in the life sciences and in particular in systems biology. Computer-based analysis tools are exploited for the investigation of these networks. However, to find relevant data sources can be a time-consuming task, access to information changes, frequently it is not obvious to a user which tools can be used in combination with specific data sources, and network data is often not available in a format directly usable by analysis tools.To assist in collecting information about biological network data sources, and to help in investigating possible combinations of data sources and analysis software, we created BiNCo-wiki. BiNCo-wiki is a wiki system that stores information related to biological networks and allows all users to add or modify this information in an easy way. The collaborative character of a wiki system supports easy sharing of information and allows the community-based extension of the information already stored in BiNCo-wiki. We describe the general structure and functionality of the system and its initial content. BiNCo-wiki is accessible, free of charge, at http://pgrc.ipk-gatersleben.de/BiNCO-wiki.

The (developmental) neurotoxicity hazard is still unknown for most chemicals. Establishing a test battery covering most of the relevant adverse outcome pathways may close this gap, without requiring a huge animal experimentation program. Ideally, each of the assays would cover multiple mechanisms of toxicity. One candidate test is the human LUHMES cell-based NeuriTox test. To evaluate its readiness for larger-scale testing, a proof of concept library assembled by the U.S. National Toxicology Program (NTP) was screened. Of the 75 unique compounds, seven were defined as specifically neurotoxic after the hit-confirmation phase and additional ten compounds were generally cytotoxic within the concentration range of up to 20 micromolar. As complementary approach, the library was screened in the PeriTox test, which identifies toxicants affecting the human peripheral nervous system. Of the eight PeriTox hits, five were similar to the NeuriTox hits: rotenone, colchicine, diethylstilbestrol, berberine chloride, and valinomycin. The unique NeuriTox hit, methyl-phenylpyridinium (MPP+) is known from in vivo studies to affect only dopaminergic neurons (which LUHMES cells are). Conversely, the known peripheral neurotoxicant acrylamide was picked up in the PeriTox, but not in the NeuriTox assay. All of the five common hits had also been identified in the published neural crest migration (cMINC) assay, while none of them emerged as cardiotoxicant in a previous screen using the same library. These comparative data suggest that complementary in vitro tests can pick up a broad range of toxicants, and that multiple test results might help to predict organ specificity patterns.

Funder: Bundesministerium für Bildung und Forschung (BMBF); Id: http://dx.doi.org/10.13039/501100002347, We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective.

Funder: Bundesministerium f��r Bildung und Forschung, Funder: Bundesministerium f��r Bildung und Forschung (BMBF), We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

International audience; Immersive Analytics is a new research initiative that aims to remove barriers between people, their data and the tools they use for analysis and decision making. Here we clarify the aims of immersive analytics research, its opportunities and historical context, as well as providing a broad research agenda for the field. In addition, we review how the term immersion has been used to refer to both technological and psychological immersion, both of which are central to immersive analytics research.