ICoVeR - an interactive visualization tool for verification and refinement of metagenomic bins.

BACKGROUND: Recent advances in high-throughput sequencing allow for much deeper exploitation of natural and engineered microbial communities, and to unravel so-called "microbial dark matter" (microbes that until now have evaded cultivation). Metagenomic analyses result in a large number of genomic fragments (contigs) that need to be grouped (binned) in order to reconstruct draft microbial genomes. While several contig binning algorithms have been developed in the past 2 years, they often lack consensus. Furthermore, these software tools typically lack a provision for the visualization of data and bin characteristics. RESULTS: We present ICoVeR, the Interactive Contig-bin Verification and Refinement tool, which allows the visualization of genome bins. More specifically, ICoVeR allows curation of bin assignments based on multiple binning algorithms. Its visualization window is composed of two connected and interactive main views, including a parallel coordinates view and a dimensionality reduction plot. To demonstrate ICoVeR's utility, we used it to refine disparate genome bins automatically generated using MetaBAT, CONCOCT and MyCC for an anaerobic digestion metagenomic (AD microbiome) dataset. Out of 31 refined genome bins, 23 were characterized with higher completeness and lower contamination in comparison to their respective, automatically generated, genome bins. Additionally, to benchmark ICoVeR against a previously validated dataset, we used Sharon's dataset representing an infant gut metagenome. CONCLUSIONS: ICoVeR is an open source software package that allows curation of disparate genome bins generated with automatic binning algorithms. It is freely available under the GPLv3 license at https://git.list.lu/eScience/ICoVeR . The data management and analytical functions of ICoVeR are implemented in R, therefore the software can be easily installed on any system for which R is available. Installation and usage guide together with the example files ready to be visualized are also provided via the project wiki. ICoVeR running instance preloaded with AD microbiome and Sharon's datasets can be accessed via the website

Visual Analytics of Multilayer Networks Across Disciplines (Dagstuhl Seminar 19061)

This report documents the program and the outcomes of Dagstuhl Seminar 19061 "Visual Analytics of Multilayer Networks Across Disciplines". Networks, used to understand systems, often contain multiple types of nodes and/or edges. They are often flattened to a single network, even though real-world systems are more accurately modelled as a set of interacting networks, or layers, with different node and edge types. These are so-called multilayer networks. These networks are studied by researchers both in network visualization and in complex systems -- the domain from which the concept of multilayer networks has recently emerged. Moreover, researchers in various application domains study these systems, e.g. biology, digital humanities, sociology and journalism. These research areas have shown parallel individual developments. Therefore, one of the aims of the seminar was to bring together an interdisciplinary community of researchers and practitioners of different disciplines. This interdisciplinary community discussed existing solutions, open challenges and future research directions for visual analytics of multilayer networks across disciplines. The seminar was attended by researchers from information visualization, visual analytics, complex systems and application domains. The application domains covered digital humanities, social sciences, biological sciences, and in public health research (25% of attendees were from these fields). The seminar not only provided multiple application domains for the visualization experts, but also also provided the domains experts with different groups of visualization experts in breakouts sessions, to expose them to multiple approaches to solving their problems. Building on this close working relationship between the visualization and domain experts, working groups were defined to determine which are the important challenges for multilayer network visualization. A number of sub-topics were identified that require further research: A unifying visualization framework, Novel Visual Encodings, Analytic and Attributes, Interaction, Evaluation, Use Cases and Human Factors. The outcomes of the seminar should stimulate collaborative research on these topics between our community, complex networks, and wide range of application domains for the visual analytics of multilayer network

VAST Challenge MC1: An Off the Shelf Approach to Messy Data

<p>An of the shelf approach to analyzing complex data for the 2014 IEEE VAST challgenge (MC1).</p

Towards A Taxonomy of Autonomous Vehicle Handover Situations

International audienc

Machine Learning to Support the Presentation of Complex Pathway Graphs.

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts

Driver De-Skilling and its Effect for Safety in Autonomous Driving

Semi-automated vehicles allow drivers to conduct other activities in the vehicle, such as reading a book. In case"br" of an emergency, the vehicle might induce a handover. This may happen in cases which are not manageable for"br" the automated system. It requires the driver to take over and resolve the situation in sub-optimal, complex, and"br" potentially dangerous situations. As a result of a lack of frequent driving, drivers may no longer possess the"br" skills to do so. This phenomenon is also known as one of the ironies of automation (Bainbridge, 1983). We target"br" the question how de-skilling will affect driving capabilities of drivers and how we can support the skill loss. In"br" an online study, we showed a dominance of initial skilling over de-skilling effects. In interviews with pilots, we"br" identified strategies against de-skilling in aviation for adaptation in the automotive domain. We show that initial"br" driver education, repeated transition training, increased situation awareness, constant mode awareness,"br" calibrating trust, and assigning responsibility are important factors for safe automated driving

Interactive Visual Support for Metagenomic Contig Binning

<p>This poster describes an initial prototype which combines multivariate visualization, clustering and dimensionality reduction to support the task of contig binning in metagenomic studies.</p

A taxonomy of autonomous vehicle handover situations

International audienceThis paper provides a taxonomy of different forms of autonomous vehicle handover situations. It covers scheduled, emergency and non-emergency handovers and it differentiates between system and driver initiated handovers. The purpose is to examine how the system and driver are responsible for different stages in the transition timeline, i.e., first alert, handover phase, and return to automated control (handback). This is examined from the perspective of SAE levels in comparison to aspects drawn from situational awareness. The work is complemented by analysis drawn from current practice within the insurance industry and interviews with insurers. The result is a closer examination of system and driver responsibility which is independent of but includes SAE levels with respect to specific handover situations. It also identifies gaps between the current legal liability for accidents when compared to aspects such as the situational awareness requirements placed on driver under different driving conditions