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        <title>Frontiers in Bioinformatics | Data Visualization section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/bioinformatics/sections/data-visualization</link>
        <description>RSS Feed for Data Visualization section in the Frontiers in Bioinformatics journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-04-06T23:39:18.760+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756459</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756459</link>
        <title><![CDATA[molIEreVIS: exploring and interpreting the evidence behind drug repurposing predictions]]></title>
        <pubdate>2026-03-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amal Alnouri</author><author>Andreas Hinterreiter</author><author>Christian Steinparz</author><author>Labinot Bajraktari</author><author>Sebastian Burgstaller-Muehlbacher</author><author>Markus Bauer</author><author>Gregorio Alanis-Lobato</author><author>Marc Streit</author>
        <description><![CDATA[IntroductionFinding new uses for existing drugs, known as drug repurposing, is a widely adopted drug development strategy in the pharmaceutical industry. Computational drug repurposing leverages vast biomedical data to prioritize repurposing candidates. Once these candidates are prioritized, domain experts face the burden of evaluating their true potential.MethodsIn this work, we propose a visualization-based approach to address this challenge for a multimodal class of computational drug repurposing, where heterogeneous evidence modalities are integrated. We conducted a design study in close collaboration with domain experts, from which we derived a domain abstraction of the expert assessment process. Grounded in this abstraction, we developed an interactive visualization approach that explicitly models the expert reasoning process. We applied the proposed approach to create a prototype implementation, molIEreVIS, in the context of an operational drug repurposing pipeline. We used this prototype to collect qualitative feedback from domain experts actively engaged in assessing computational drug repurposing candidates.ResultsThe results demonstrate the potential of our approach to support insights and reasoning in this process and reveal directions for enhancements and future work.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756507</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756507</link>
        <title><![CDATA[SHACLens: a visualization workflow for SHACL violation exploration in knowledge graphs]]></title>
        <pubdate>2026-03-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Christian A. Steinparz</author><author>Andreas Hinterreiter</author><author>Labinot Bajraktari</author><author>Vitaly Sedlyarov</author><author>Markus J. Bauer</author><author>Thomas Zichner</author><author>Marc Streit</author>
        <description><![CDATA[IntroductionValidating large knowledge graphs with the Shapes Constraint Language (SHACL) often yields violation reports too large to interpret and trace to root causes, especially in industry-scale datasets such as pharmaceutical omics pipelines.MethodsWe present SHACLens, an interactive visualization workflow—developed with a major pharmaceutical partner—that links ontology, instance data, and violation reports across multiple coordinated views. We contribute a practitioner-informed workflow co-designed with pharmaceutical data-analysis experts. A Node-Link View combines ontology and groups of equivalent violations, a projection view reveals clusters of nodes with similar errors, a LineUp table combines instance data with violation information, a Class Tree offers a class-hierarchy overview, and an integrated LLM assistant provides contextual explanations and can operate the system via natural-language commands.ResultsWithin this workflow, selections and filters propagate across views, exposing co-occurring errors and their likely upstream causes. Analysts iteratively identify violation clusters, inspect correlations, and trace the detailed cause of errors.Evaluation and implicationsWe evaluated SHACLens through an iterative expert-in-the-loop design process with the partner team and a qualitative study on a transcriptomics dataset containing 5,203 violating nodes with the same experts. In this study, SHACLens efficiently surfaced repeated sets of errors due to missing objects and schema inconsistencies, supporting goal-oriented analysis and serendipitous findings.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1694775</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1694775</link>
        <title><![CDATA[Machine learning for N-dimensional spatial reasoning tasks on the web]]></title>
        <pubdate>2026-03-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Blake Moody</author><author>JieHyun Kim</author><author>Sanghyuk Kim</author><author>Daniel Haehn</author>
        <description><![CDATA[Spatial reasoning is essential for solving complex tasks in dynamic and high-dimensional environments. However, current training models for spatial tasks are computationally demanding and heavily reliant on human input. To address this gap, we present Snake-ML, a web-based simulation tool and proof-of-concept framework designed to demonstrate client-side training of spatial reasoning tasks. Snake-ML serves as an efficient and intuitive test bed for developing spatial navigation strategies in browser-based environments. We chose the snake game as our test bed because it is well suited for demonstrating spatial reasoning in low-dimensional visual spaces while remaining relevant to higher-dimensional tasks, compared to alternative methods. Through quantitative analysis, on the edge alone, Snake-ML achieves a 4.58× speedup in model inference. Additionally, we developed a direct TensorFlow.js GPU pipeline that achieves up to a 32× speedup in training time without any CPU/GPU synchronization. This pipeline has the potential to improve many edge-based AI visualization projects. Snake-ML shows potential for adaptability to complex spatial tasks, such as autonomous systems, robotics, and AI-driven environments. Our code and web-based simulation tool are publicly available.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1757489</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1757489</link>
        <title><![CDATA[Visualizing the multidimensional landscape of biological variation in modern microscopy]]></title>
        <pubdate>2026-03-10T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Gesine F. Müller</author><author>Torben Göpel</author><author>Nico Scherf</author><author>Jan Huisken</author>
        <description><![CDATA[Variation is a foundational biological principle that has historically been marginalized—both due to limited experimental accessibility and because of idealized, stereotypic blueprints rooted in essentialist thinking. With the advent of genetics and quantitative biology investigating environmental influences on the phenotype, variation was redefined from mere noise to a fundamental property. Modern light sheet microscopy now enables high-resolution, long-term imaging of dynamic processes across large populations, making it possible to systematically study phenotypic variation in vivo. Yet, the resulting high-dimensional datasets overwhelm traditional modes of analysis and visualization, risking the loss of biological insight. The transition from qualitative representation to quantitative measurement demands new epistemic practices—shifting from selective human interpretation to computational abstraction. Instead of relying on either very limited sampling or exhaustive scanning, we advocate for representative sampling of phenotypic variation: adaptive, model-guided systems that dynamically sample biological variation using real-time feedback, directing attention towards biologically relevant events and rare or extreme phenotypes. The underlying models act as the interface to human insight, constructing navigable, queryable representations of variation as a multidimensional manifold shaped by genetics, environment, and stochasticity. Crucially, adaptive systems call for new methods of visualizations—interfaces that encode uncertainty, consensus, and distributional structure. Such visualizations should preserve the interpretability of historical illustrations while fully embracing biological variation. The future of biology lies not in acquiring more data, but in developing smarter ways to sample, represent, and understand it.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1763403</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1763403</link>
        <title><![CDATA[Chart builder: an interactive tool for user driven data visualization in the electron microscopy data bank]]></title>
        <pubdate>2026-03-06T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Neli Fonseca</author><author>Amudha Kumari Duraisamy</author><author>Zhe Wang</author><author>Sriram Somasundharam</author><author>Minoosadat Tayebinia</author><author>Lucas C. de Oliveira</author><author>Miao Ma</author><author>Jack Turner</author><author>Ardan Patwardhan</author><author>Gerard J. Kleywegt</author><author>Matthew Hartley</author><author>Kyle L. Morris</author>
        <description><![CDATA[The cryogenic sample-electron microscopy (cryoEM) field has generated significant amounts of 3D Electron Microscopy (3DEM) volumetric data and associated metadata, now comprehensively archived in the Electron Microscopy Data Bank (EMDB - www.emdatabank.org) and the Electron Microscopy Public Image Archive (EMPIAR - www.empiar.org). Harnessing the full potential of these resources requires robust, flexible, and publicly accessible tools for data exploration, analysis and retrieval. Here, we present Chart Builder, an interactive web-based platform that enables researchers to create customizable, publication-quality visualizations directly from archival metadata, validation assessments, and cross-reference annotations. Chart Builder integrates the same query-driven and flexible Solr search system as EMDB search, into a user interface with tools to assist users to filter, group, and compare data without programming expertise. It supports multiple chart types (including line, bar, area, scatter (2D and 3D), histogram, bubble, pie, geographic and Venn diagrams) with customizable axes, data series, and statistical operators. Users can apply global filters, define temporal, categorical, or custom query-based axes, and explore multi-dimensional relationships interactively. Chart data-points are linked to their underlying datasets, such that visualisation interaction opens entry-level or archive-level search results for inspection and datasets from charts may be exported in several ways. Findability, accessibility, interoperability and reusability of data are facilitated by these direct access and export mechanisms, including HTML embedding, persistent URL sharing and chart/data download options. By combining interactivity and ease of use with up-to-date access to the EMDB and EMPIAR archive metadata, both computational and experimental communities may explore and visualize current metadata and export to formats for further analysis or as publication-ready figures. Chart Builder promotes community-driven data analysis and empowers users to evaluate trends in the biological 3DEM field. Chart Builder is freely accessible and fully integrated into the EMDB website at https://www.ebi.ac.uk/emdb/statistics/builder/.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1762759</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1762759</link>
        <title><![CDATA[Movement beyond data: epistemic and pictorial challenges in understanding moving life]]></title>
        <pubdate>2026-02-19T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Janina Wellmann</author>
        <description><![CDATA[Nothing inside organisms is at rest; everything moves. Cells are transported through the vascular system, proteins move cargo away or toward the cell nucleus, and enzymes repair DNA, which is constantly modified by metabolic cell processes or external influences affecting the organism. Various processes occur simultaneously in the crowded spaces of cells and across tissues, carefully coordinated and orchestrated. In contemporary science, movement lies at the root of studying cellular and molecular processes—in short, of all the activities that occur within the organism. This article provides a historical perspective and methodological reflection on the study of cellular and subcellular motion in current biotechnology. It shows that, far from being evident, movement is not simply observed but actively made. I argue, first, that the conceptualization of motion in current biotechnology occurs through image-making and is thus shaped by a long pictorial history and the struggle to depict movement. Second, to make the invisible move under the conditions of visibility, metaphors and imaginaries drawn from our everyday experience of animal motion are transposed into the nanoscopic sphere, thereby setting the framework and limits of understanding motion at the molecular level.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1752027</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1752027</link>
        <title><![CDATA[From signal overload to shared insight: creating and structuring scientific visuals for comprehension and dialogue]]></title>
        <pubdate>2026-02-13T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Joost M. Bakker</author><author>Dirma Janse</author><author>Martijn van Overbruggen</author><author>Maarten C. A. van der Sanden</author>
        <description><![CDATA[Effective communication of scientific content can be challenging due to cognitive overload. This is experienced especially during conferences and poster presentations, where the presence of competing stimuli limits message retention. Scientific visuals offer a means to overcome this limitation by emphasizing the essential components of a narrative in a form that is rapidly and intuitively processed. Rather than serving primarily as demonstrations of complexity or markers of personal accomplishment, scientific visuals should function as tools for idea exchange, enabling broader comprehension and facilitating dialogue. The Gestalt principles are an important guide for the visual creation process. These perceptual principles exploit pre-attentive processing mechanisms that allow viewers to extract essential structure and meaning immediately with minimal conscious effort. Effective development of scientific visuals can be approached in three stages: an initial sketch phase, focused on defining the core content and refining the central message, followed by a design phase and refinement phase, in which form, layout and color are used according to perceptual principles. This structured process ensures that complex narratives can be communicated with clarity and precision. By prioritizing cognitive accessibility over ornamental design, visuals become a central and intrinsic component of scientific discourse, supporting insight generation, fostering dialogue, and contributing to collaborative learning and consecutive knowledge building.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1719516</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1719516</link>
        <title><![CDATA[Visualizing stability: a sensitivity analysis framework for t-SNE embeddings]]></title>
        <pubdate>2026-01-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Susanne Zabel</author><author>Philipp Hennig</author><author>Kay Nieselt</author>
        <description><![CDATA[t-distributed Stochastic Neighbour Embedding (t-SNE) is a cornerstone for visualizing high-dimensional biological data, where each high-dimensional data point is represented as a point in a two-dimensional map. However, this static map provides no information about the stability of the visual layout, the features that influence it, or the impact of uncertainty in the input data. This work introduces a computational framework that allows one to extend the standard t-SNE plot by visual clues about the stability of the t-SNE embedding. First, we perform a sensitivity analysis to determine feature influence: by combining the Implicit Function Theorem with automatic differentiation, our method computes the sensitivity of the embedding w.r.t. the input data, provided in a Jacobian of first-order derivatives. Heatmap-visualizations of this Jacobian or summarizations thereof reveal which input features are most influential in shaping the embedding and identifying regions of structural instability. Second, when input data uncertainty is available, our framework uses this Jacobian to propagate error, probabilistically quantifying the positional uncertainty of each embedded point. This uncertainty is visualized by augmenting the plot with hypothetical outcomes, which display the positional confidence of each point. We apply our framework to three diverse biological datasets (bulk RNA-seq, proteomics, and single-cell transcriptomics), demonstrating its ability to directly link visual patterns to their underlying biological drivers and reveal ambiguities invisible in a standard plot. By providing this principled means to assess the robustness and interpretability of t-SNE visualizations, our work enables more rigorous and informed scientific conclusions in bioinformatics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1708311</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1708311</link>
        <title><![CDATA[Why science needs art]]></title>
        <pubdate>2025-10-24T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Giulia Ghisleni</author><author>Christian Stolte</author><author>Megan Gozzard</author><author>Lea Von Soosten</author><author>Antonia Bruno</author>
        <description><![CDATA[This perspective paper examines the profound cognitive and methodological parallels between scientific and artistic research, challenging the traditional distinction between the two domains. While science and art use different languages, both emerge from the human drive for creativity and understanding. We argue that scientific inquiry, often presented as strictly objective and methodical, inherently shares with art the need for imagination, flexibility, and interpretative thinking. Drawing on neuroscience, education, design theory, and the visual arts, we highlight how artistic practices, particularly in the visual arts, can enhance scientific learning, innovation, and public engagement. We advocate integrating art into scientific training and research to foster a more creative and inclusive epistemology. Through examples in microbiology, education, and data visualization, we show how the arts can support deeper understanding, cross-disciplinary collaboration, and more effective science communication. Ultimately, we call for a shift toward a more integrated approach that embraces the complementary strengths of both art and science in advancing knowledge and societal impact.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1528515</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1528515</link>
        <title><![CDATA[Adaptive sampling methods facilitate the determination of reliable dataset sizes for evidence-based modeling]]></title>
        <pubdate>2025-09-04T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Tim Breitenbach</author><author>Thomas Dandekar</author>
        <description><![CDATA[How can we be sure that there is sufficient data for our model, such that the predictions remain reliable on unseen data and the conclusions drawn from the fitted model would not vary significantly when using a different sample of the same size? We answer these and related questions through a systematic approach that examines the data size and the corresponding gains in accuracy. Assuming the sample data are drawn from a data pool with no data drift, the law of large numbers ensures that a model converges to its ground truth accuracy. Our approach provides a heuristic method for investigating the speed of convergence with respect to the size of the data sample. This relationship is estimated using sampling methods, which introduces a variation in the convergence speed results across different runs. To stabilize results—so that conclusions do not depend on the run—and extract the most reliable information encoded in the available data regarding convergence speed, the presented method automatically determines a sufficient number of repetitions to reduce sampling deviations below a predefined threshold, thereby ensuring the reliability of conclusions about the required amount of data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1586744</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1586744</link>
        <title><![CDATA[ngx-mol-viewers: Angular components for interactive molecular visualization in bioinformatics]]></title>
        <pubdate>2025-06-26T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Damiano Clementel</author><author>Alessio Del Conte</author><author>Alexander Miguel Monzon</author><author>Silvio C. E. Tosatto</author>
        <description><![CDATA[Advancements in bioinformatics have been propelled by technologies like machine learning and have resulted in substantial increases in data generated from both empirical observations and computational models. Hence, well-known biological databases are growing in size and centrality by integrating data from different sources. While the primary goal of these databases is to collect and distribute data through application programming interfaces (APIs), providing visualization and analysis tools directly on the browser interface is crucial for users to understand the data, which increases the usefulness and overall impact of the databases. Currently, some front-end frameworks are available for the sustained development of the user interface (UI) and user experience (UX) of these resources. Angular is one of the most popular frameworks to be broadly adopted within the BioCompUP laboratory. This work describes a library of reusable and customizable components that can be easily integrated into the Angular framework to provide visualizations of various aspects of protein molecules, such as their sequences, structures, and annotations. Currently, the library includes three main independent components. The first is the ngx-structure-viewer, which allows visualization of molecules through the MolStar three-dimensional viewer. The second is the ngx-sequence-viewer, which provides visualization and annotation capabilities for a single sequence or multiple sequence alignments. The third the ngx-features-viewer, enables the mapping and visualization of various biological annotations onto the same molecule. All these tools are available for download through the Node Package Manager (NPM), and more information is available at https://biocomputingup.github.io/ngx-mol-viewers/ (under development).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1588661</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1588661</link>
        <title><![CDATA[Interactive visualization of large molecular systems with VTX: example with a minimal whole-cell model]]></title>
        <pubdate>2025-06-06T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Maxime Maria</author><author>Valentin Guillaume</author><author>Simon Guionnière</author><author>Nicolas Dacquay</author><author>Cyprien Plateau–Holleville</author><author>Vincent Larroque</author><author>Jean Lardé</author><author>Yassine Naimi</author><author>Jean-Philip Piquemal</author><author>Guillaume Levieux</author><author>Nathalie Lagarde</author><author>Stéphane Mérillou</author><author>Matthieu Montes</author>
        <description><![CDATA[VTX is an open-source molecular visualization software designed to overcome the scaling limitations of existing real-time molecular visualization software when handling massive molecular datasets. VTX employs a meshless molecular graphics engine utilizing impostor-based techniques and adaptive level-of-detail (LOD) rendering. This approach significantly reduces memory usage and enables real-time visualization and manipulation of large molecular systems. Performance benchmarks against VMD, PyMOL, and ChimeraX using a 114-million-bead Martini minimal whole-cell model demonstrate VTX’s efficiency, maintaining consistent frame rates even under interactive manipulation on standard computer hardware. VTX incorporates features such as Screen-Space Ambient Occlusion (SSAO) for enhanced depth perception and free-fly navigation for intuitive exploration of large molecular systems. VTX is open-source and free for non commercial use. Binaries for Windows and Ubuntu Linux are available at http://vtx.drugdesign.fr. VTX source code is available at https://github.com/VTX-Molecular-Visualization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1589122</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1589122</link>
        <title><![CDATA[Structural biology meets typography: using protein structures to inspire creative expression and connect diverse audiences]]></title>
        <pubdate>2025-05-08T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Leonora Martínez-Núñez</author>
        <description><![CDATA[Proteins are complex molecular machines with specific structures that determine their function. Advances in structural bioinformatics and visualization have expanded access to molecular data, most notably through the Protein Data Bank (PDB). This perspective explores the intersection between structural biology and typography, integrating a protein alphabet with the 36 Days of Type design project. Using ChimeraX, Blender and Molecular Nodes, 3D molecular models were processed, stylized, and shared on social media under the #36daysoftype hashtag, which led to engagement across a diverse audience. This work was also presented at VIZBI 2024 conference and influenced the VIZBI 2025 conference logo design. This project frames the role of scientific illustration and visual arts in connecting disciplines, boosting public engagement, and encouraging interdisciplinary collaboration, while also inspiring future applications like biology-inspired typography to enhance scientific literacy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1523184</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1523184</link>
        <title><![CDATA[pubCounteR: an R package for interrogating published literature for experimentally-derived gene lists within a user-defined biological context]]></title>
        <pubdate>2025-05-06T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Marina Leer</author><author>George A. Soultoukis</author><author>Markus Jähnert</author><author>Masoome Oveisi</author><author>Dirk Walther</author><author>Tim J. Schulz</author>
        <description><![CDATA[Basic and clinical biomedical research relies heavily on modern large-scale datasets that include genomics, transcriptomics, epigenomics, metabolomics, and proteomics, among other “Omics”. These research tools very often generate lists of candidate genes that are hypothesized or shown to be responsible for the biological effect in question. To aid the biological interpretation of experimentally-obtained gene lists, we developed pubCounteR, an R-package and web-based interface that screens publications by a user-defined set of keywords representing a specific biological context for experimentally-derived gene lists.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1395981</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1395981</link>
        <title><![CDATA[Visual analysis of multi-omics data]]></title>
        <pubdate>2024-09-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Austin Swart</author><author>Ron Caspi</author><author>Suzanne Paley</author><author>Peter D. Karp</author>
        <description><![CDATA[We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool’s interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different “visual channel” of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1349205</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1349205</link>
        <title><![CDATA[Rvisdiff: An R package for interactive visualization of differential expression]]></title>
        <pubdate>2024-09-02T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>David Barrios</author><author>Carlos Prieto</author>
        <description><![CDATA[Rvisdiff is an R/Bioconductor package that generates an interactive interface for the interpretation of differential expression results. It creates a local web page that enables the exploration of statistical analysis results through the generation of auto-analytical visualizations. Users can explore the differential expression results and the source expression data interactively in the same view. As input, the package supports the results of popular differential expression packages such as DESeq2, edgeR, and limma. As output, the package generates a local HTML page that can be easily viewed in a web browser. Rvisdiff is freely available at https://bioconductor.org/packages/Rvisdiff/.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1353807</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1353807</link>
        <title><![CDATA[Design principles for molecular animation]]></title>
        <pubdate>2024-08-21T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Stuart G. Jantzen</author><author>Gaël McGill</author><author>Jodie Jenkinson</author>
        <description><![CDATA[Molecular visualization is a powerful way to represent the complex structure of molecules and their higher order assemblies, as well as the dynamics of their interactions. Although conventions for depicting static molecular structures and complexes are now well established and guide the viewer’s attention to specific aspects of structure and function, little attention and design classification has been devoted to how molecular motion is depicted. As we continue to probe and discover how molecules move - including their internal flexibility, conformational changes and dynamic associations with binding partners and environments - we are faced with difficult design challenges that are relevant to molecular visualizations both for the scientific community and students of cell and molecular biology. To facilitate these design decisions, we have identified twelve molecular animation design principles that are important to consider when creating molecular animations. Many of these principles pertain to misconceptions that students have primarily regarding the agency of molecules, while others are derived from visual treatments frequently observed in molecular animations that may promote misconceptions. For each principle, we have created a pair of molecular animations that exemplify the principle by depicting the same content in the presence and absence of that design approach. Although not intended to be prescriptive, we hope this set of design principles can be used by the scientific, education, and scientific visualization communities to facilitate and improve the pedagogical effectiveness of molecular animation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1415078</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1415078</link>
        <title><![CDATA[RIPS (rapid intuitive pathogen surveillance): a tool for surveillance of genome sequence data from foodborne bacterial pathogens]]></title>
        <pubdate>2024-08-09T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Tim Muruvanda</author><author>Hugh Rand</author><author>James Pettengill</author><author>Arthur Pightling</author>
        <description><![CDATA[Monitoring data submitted to the National Center for Biotechnology Information’s Pathogen Detection whole-genome sequence database, which includes the foodborne bacterial pathogens Listeria monocytogenes, Salmonella enterica, and Escherichia coli, has proven effective for detecting emerging outbreaks. As part of the submission process, new sequence data are typed using a whole-genome multi-locus sequence typing scheme and clustered with sequences already in the database. Publicly available text files contain the results of these analyses. However, contextualizing and interpreting this information is complex. We present the Rapid Intuitive Pathogen Surveillance (RIPS) tool, which shows the results of the NCBI Rapid Reports, along with appropriate metadata, in a graphical, interactive dashboard. RIPS makes the information in the Rapid Reports useful for real-time surveillance of genome sequence databases.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1356659</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1356659</link>
        <title><![CDATA[From complex data to clear insights: visualizing molecular dynamics trajectories]]></title>
        <pubdate>2024-04-11T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Hayet Belghit</author><author>Mariano Spivak</author><author>Manuel Dauchez</author><author>Marc Baaden</author><author>Jessica Jonquet-Prevoteau</author>
        <description><![CDATA[Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1331043</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1331043</link>
        <title><![CDATA[Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs]]></title>
        <pubdate>2024-02-05T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Jannes Peeters</author><author>Daniël M. Bot</author><author>Gustavo Rovelo Ruiz</author><author>Jan Aerts</author>
        <description><![CDATA[Current visualizations in microbiome research rely on aggregations in taxonomic classifications or do not show less abundant taxa. We introduce Snowflake: a new visualization method that creates a clear overview of the microbiome composition in collected samples without losing any information due to classification or neglecting less abundant reads. Snowflake displays every observed OTU/ASV in the microbiome abundance table and provides a solution to include the data’s hierarchical structure and additional information obtained from downstream analysis (e.g., alpha- and beta-diversity) and metadata. Based on the value-driven ICE-T evaluation methodology, Snowflake was positively received. Experts in microbiome research found the visualizations to be user-friendly and detailed and liked the possibility of including and relating additional information to the microbiome’s composition. Exploring the topological structure of the microbiome abundance table allows them to quickly identify which taxa are unique to specific samples and which are shared among multiple samples (i.e., separating sample-specific taxa from the core microbiome), and see the compositional differences between samples. An R package for constructing and visualizing Snowflake microbiome composition graphs is available at https://gitlab.com/vda-lab/snowflake.]]></description>
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