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Whole genome sequencing is now routinely used to profile mutations in DNA in the soma and in the germline, informing molecular diagnoses of disease and therapeutic decisions. Structural variants (SVs) are the main new type of alterations we see more of, and they are often diagnostic, prognostic, or therapy-informing. However, the size and complexity of SV data, combined with the difficulty of obtaining accurate SV calls, pose challenges in the interpretation of SVs, requiring tedious visual inspection of potentially pathogenic variants with multiple visualization tools. To overcome the problems with the interpretation of SVs, we developed Chromoscope, an open-source web-based application for the interactive visualization of structural variants. Chromoscope has several innovative features which unlock the insights from whole genome sequencing: visualization at multiple scale levels simultaneously, effective navigation across scales; easy setup for loading users' large datasets, and a feature to export, share, and further customize visualizations. We are hosting a freely available public instance of Chromoscope (https://chromoscope.bio) to showcase data from the ‘Pan-cancer Analysis of the Whole Genomes’ consortium, providing easy access to this reference dataset. We additionally facilitate the set up of visualizations for users' data. We anticipate that Chromoscope will accelerate the exploration and interpretation of SVs by a broad range of scientists and clinicians, leading to new insights into genomic biomarkers.

The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling—a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js, its source code, and documentation are available at https://gosling.js.org.

Summary The regulation of genes by cis-regulatory elements (CREs) is complex and differs between cell types. Visual analysis of large collections of chromatin profiles across diverse cell types, integrated with computational methods, can reveal meaningful biological insights. We developed Cistrome Explorer, a web-based interactive visual analytics tool for exploring thousands of chromatin profiles in diverse cell types. Integrated with the Cistrome Data Browser database which contains thousands of ChIP-seq, DNase-seq and ATAC-seq samples, Cistrome Explorer enables the discovery of patterns of CREs across cell types and the identification of transcription factor binding underlying these patterns. Availability and implementation Cistrome Explorer and its source code are available at http://cisvis.gehlenborglab.org/ and released under the MIT License. Documentation can be accessed via http://cisvis.gehlenborglab.org/docs/. Supplementary information Supplementary data are available at Bioinformatics online.

Summary Gos is a declarative Python library designed to create interactive multiscale visualizations of genomics and epigenomics data. It provides a consistent and simple interface to the flexible Gosling visualization grammar. Gos hides technical complexities involved with configuring web-based genome browsers and integrates seamlessly within computational notebooks environments to enable new interactive analysis workflows. Availability and implementation Gos is released under the MIT License and available on the Python Package Index (PyPI). The source code is publicly available on GitHub (https://github.com/gosling-lang/gos), and documentation with examples can be found at https://gosling-lang.github.io/gos.

While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking.A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021.Advanced age (HR 2.77, 95%CI 2.53-3.04, p 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI.COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery.Authors are supported by various funders, with full details stated in the acknowledgement section.

Interpretation of genomics data is critically reliant on the application of a wide range of visualization tools. A large number of visualization techniques for genomics data and different analysis tasks pose a significant challenge for genomics analysts: which visualization technique is most likely to help them generate insights into their data? Since genomics analysts typically have limited training in data visualization, their choices are often based on trial and error or guided by technical details, such as data formats that a specific tool can load. This approach prevents them from making effective visualization choices for the many combinations of data types and analysis questions that they encounter in their work. Visualization recommendation systems assist non-experts in creating data visualization by recommending appropriate visualizations based on the data and task characteristics. However, existing visualization recommendation systems are not designed to handle domain-specific problems. To address these challenges we designed GenoREC, a novel visualization recommendation system for genomics. GenoREC enables genomics analysts to select effective visualizations based on a description of their data and analysis tasks. Here we present the recommendation model that uses a knowledge-based method for choosing appropriate visualizations and a web application that enables analysts to input their requirements, explore recommended visualizations, and export them for their usage. Furthermore, we present the results of two user studies demonstrating that GenoREC recommends visualizations that are both accepted by domain experts and suited to address the given genomics analysis problem. All supplemental material is available on OSF: https://osf.io/y73pt/

A series of recent studies has focused on designing cross-resolution and cross-device visualizations, i.e., responsive visualization, a concept adopted from responsive web design. However, these studies mainly focused on visualizations with a single view to a small number of views, and there are still unresolved questions about how to design responsive multi-view visualizations. In this paper, we present a reusable and generalizable framework for designing responsive multi-view visualizations focused on genomics data. To gain a better understanding of existing design challenges, we review web-based genomics visualization tools in the wild. By characterizing tools based on a taxonomy of responsive designs, we find that responsiveness is rarely supported in existing tools. To distill insights from the survey results in a systematic way, we classify typical view composition patterns, such as "vertically long," "horizontally wide," "circular," and "cross-shaped" compositions. We then identify their usability issues in different resolutions that stem from the composition patterns, as well as discussing approaches to address the issues and to make genomics visualizations responsive. By extending the Gosling visualization grammar to support responsive constructs, we show how these approaches can be supported. A valuable follow-up study would be taking different input modalities into account, such as mouse and touch interactions, which was not considered in our study.

Visualization plays a critical role in XAI due to its efficiency in organizing and communicating information. However, current visualizations are designed mainly for AI researchers and offer little to no support for domain experts such as biologists or clinicians. Failing to consider the perspective of users and the characteristics of the applied research domains can significantly undermine the utility and usability of visualizations and explanations. We discuss the challenges and opportunities about the utility and usability of visualization in AI-driven scientific discovery.

UNSTRUCTURED Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study In “International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study” (J Med Internet Res 2021 Oct 11;23(10):e31400. doi: 10.2196/31400), two errors were noted. Due to a system error, the equal contribution of the last three authors was not noted. To correct this under the JMIR parameters allowing only one equal contribution footnote, we are implementing the following changes. In the originally published paper, equal contribution was noted as: “Griffin M Weber MD, PhD1*, Harrison G Zhang1*, Sehi L'Yi PhD1*,… *These authors contributed equally” This has been corrected to: “Griffin M Weber MD, PhD1*, Harrison G Zhang1*, Sehi L'Yi PhD1*,… Tianxi Cai ScD1*‡, Andrew M South MD, MS36*, Gabriel A Brat MD, MPH1*… *These authors contributed equally” Additionally, the Authors’ Contribution section has been updated to include: “These authors contributed equally: Griffin M Weber MD, PhD, Harrison G Zhang, Sehi L’Yi PhD. These authors jointly supervised the work: Tianxi Cai ScD, Andrew M South MD, MS, Gabriel A Brat MD.” The correction will appear in the online version of the paper on the JMIR Publications website on November 2, 2021, together with the publication of this correction notice.

International audience; ObjectiveFor multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information.Materials and Methods For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or can be a single center, corresponding to transfer learning.Results Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations.Conclusions The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Analysis and interpretation of genomics data are the backbones of breakthroughs and discoveries in biomedical research. Visualization tools and techniques play a significant role in the workflow of genomics researchers, and they are regularly employed in the interpretation of genomics data. However, the vast majority of genomics researchers have little or no formal training in data visualization design. Therefore, they require guidance on designing effective visualizations for a given set of data and analysis tasks. In this poster, we present the methodology behind a recommender system for genomics data and our preliminary design of a knowledge-based recommendation system. The system allows genomics researchers to navigate through a selection of visualization options and identify the techniques that meet their preferences and requirements.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across 5 countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on comorbidities and temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

We present a systematic review on three comparative layouts (i.e., juxtaposition, superposition, and explicit-encoding) which are information visualization (InfoVis) layouts designed to support comparison tasks. For the last decade, these layouts have served as fundamental idioms in designing many visualization systems. However, we found that the layouts have been used with inconsistent terms and confusion, and the lessons from previous studies are fragmented. The goal of our research is to distill the results from previous studies into a consistent and reusable framework. We review 127 research papers, including 15 papers with quantitative user studies, which employed comparative layouts. We first alleviate the ambiguous boundaries in the design space of comparative layouts by suggesting lucid terminology (e.g., chart-wise and item-wise juxtaposition). We then identify the diverse aspects of comparative layouts, such as the advantages and concerns of using each layout in the real-world scenarios and researchers' approaches to overcome the concerns. Building our knowledge on top of the initial insights gained from the Gleicher et al.'s survey, we elaborate on relevant empirical evidence that we distilled from our survey (e.g., the actual effectiveness of the layouts in different study settings) and identify novel facets that the original work did not cover (e.g., the familiarity of the layouts to people). Finally, we show the consistent and contradictory results on the performance of comparative layouts and offer practical implications for using the layouts by suggesting trade-offs and seven actionable guidelines., Comment: IEEE VIS InfoVis 2020, ACM 2012 CCS - Human-centered computing, Visualization, Visualization theory, concepts, and paradigms

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

There are many types of clustering algorithms such as centroid, hierarchical, or density-based methods. Each algorithm has unique data grouping principles, which creates different varieties of clusters. Ordering Points To Identify the Clustering Structure (OPTICS) is a well-known density-based algorithm to analyze arbitrary shaped and varying density clusters, but the obtained clusters only correlate loosely. Hierarchical agglomerative clustering (HAC) reveals a hierarchical structure of clusters, but is unable to clearly find non-convex shaped clusters. In this paper, we provide a novel hierarchy generation framework and application which can aid users by combining the advantages of the two clustering methods.

In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs. However, there still is a need for improving the performance for miRNA prediction algorithms and more importantly for interactive visualization tools for an integrative analysis of multiple prediction results. miRTarVis+ has an intuitive interface to support the analysis pipeline of load, filter, predict, and visualize. It can predict targets of miRNA by adopting Bayesian inference and maximal information-based nonparametric exploration (MINE) analyses as well as conventional correlation and mutual information analyses. miRTarVis+ supports an integrative analysis of multiple prediction results by providing an overview of multiple prediction results and then allowing users to examine a selected miRNA-mRNA network in an interactive treemap and node-link diagram. To evaluate the effectiveness of miRTarVis+, we conducted two case studies using miRNA-mRNA expression profile data of asthma and breast cancer patients and demonstrated that miRTarVis+ helps users more comprehensively analyze targets of miRNA from miRNA-mRNA expression profile data. miRTarVis+ is available at http://hcil.snu.ac.kr/research/mirtarvisplus.

We present CloakingNote, a novel desktop interface for subtle writing. The main idea of CloakingNote is to misdirect observers' attention away from a real text by using a prominent decoy text. To assess the subtlety of CloakingNote, we conducted a subtlety test while varying the contrast ratio between the real text and its background. Our results demonstrated that the real text as well as the interface itself were subtle even when participants were aware that a writer might be engaged in suspicious activities. We also evaluated the feasibility of CloakingNote through a performance test and categorized the users' layout strategies.

Background Though cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious. Results In this paper, we present XCluSim, a visual analytics tool that enables users to interactively compare multiple clustering results based on the Visual Information Seeking Mantra. We build a taxonomy for categorizing existing techniques of clustering results visualization in terms of the Gestalt principles of grouping. Using the taxonomy, we choose the most appropriate interactive visualizations for presenting individual clustering results from different types of clustering algorithms. The efficacy of XCluSim is shown through case studies with a bioinformatician. Conclusions Compared to other relevant tools, XCluSim enables users to compare multiple clustering results in a more scalable manner. Moreover, XCluSim supports diverse clustering algorithms and dedicated visualizations and interactions for different types of clustering results, allowing more effective exploration of details on demand. Through case studies with a bioinformatics researcher, we received positive feedback on the functionalities of XCluSim, including its ability to help identify stably clustered items across multiple clustering results.

Heart diseases associated with living conditions, such as coronary heart disease and hypertension are becoming more and more complex. If heart condition didn't get long-term and real-time monitoring and diagnosis, it may induce sudden heart disease. As the heart auscultation is one of the most fundamental ways to evaluate the heart functions, a stethoscope can be used to auscultate respiratory sounds and heart sounds, and diagnose most of the cardiopulmonary disorders and other diseases. Therefore, we tried to develop a smartphone-based stethoscope system that can record heart sound and calculate heart rate from the sound. The proposed system is designed to acquire the heart sounds using the iPhone or the iPad applications, record the acquired data on a real-time basis and display the heart sounds in a graph, and allow listening to the acquired sounds. Also, the peak of the heart sounds is identified on a real-time basis to determine the heart rate, thereby quantifying the information related to the cardiovascular system. The system can be utilized under various environments depending on the sensor type such as built-in microphones within the smartphones, external microphones, and electronic stethoscopes. In subsequent studies, we plan to utilize the detected heart sound peaks to display the magnitude of the heart sounds, and apply various analysis methods to quantify the information related to cardiac activities.

The pupillary light reflex (PLR) test is the method of examining the immediate reaction of the pupil by light stimuli, and dysfunctions in autonomic nervous system can be evaluated quickly and easily through this method. Existing pupillary light reflex examinations utilize medical penlights to provide light stimuli at the pupil of the examined person, directly observe and assess the changes in the pupil based on the stimuli, and record them. Therefore, they have weaknesses in that the recorded results can vary depending on the examiner, and as the changes of the pupil are not measured quantitatively, such methods do not allow objective assessments. The proposed system in this study utilizes the flash LED of the smartphone to provide light stimuli to the patient's pupil, and the resulting reactions are recorded as images and videos to be analyzed. In case of video recording, the user can turn the flash on or off at the desired moment and data acquisition time was set to be 6 seconds. For the image mode, the system was designed to acquire one image prior to light stimulation and 4 images after light simulation, so that temporal changes in pupil sizes with passing of time can be compared. The system developed in this study utilizes smartphones so that anyone can easily utilize the tool conveniently without restrictions of time and location. Further, other strengths include the fact that light stimulation can be provided using the flash function built within the smartphones, as well as the fact that patient data could be saved and managed easily, and shared using the email transmission function.