Lightning Talks

<p>We wanted to showcase a lot of different contributions and the beautiful heterogeneity of bioinformatics ending with a lighting talk session! Here's the list of the 3' presentations:</p> <ul> <li>Guixifying workflow management system: past, present, maybe future? by Simon Tournier </li> <li>VTX, High Performance Visualization of Molecular Structure and Trajectories by valentin</li> <li>Multimodal Tumor Evolution Analysis: Interactive 4D CT and Time-Aligned Clinical Data in a Hospital Web Platform by Fabian Fulga</li> <li>DNA storage and open-source projects by Babar Khan</li> <li>From Binary to Granular: Automating Multi-Threshold Survival Analysis with OptSurvCutR by Payton Yau</li> </ul> <hr /> <p><strong>Guixifying workflow management system: past, present, maybe future?</strong> Bioinformatics and Computational Biology face a twofold challenge. On one hand, the number of steps required to process the amounts of data is becoming larger and larger. And each step implies software involving more and more dependencies. On the other hand, Reproducible Research requires the ability to deeply verify and scrutinize all the processes. And Open Science asks about the ability to reuse, modify or extend.</p> <p>Workflow might be transparent and reproducible if and only if it’s built on the top of package managers that allow, with the passing of time, to finely control both the set of dependencies and the ability to scrutinize or adapt.</p> <p>The first story is <a href="https://hpc.guix.info/">Guix</a> Workflow Language (<a href="https://guixwl.org/">GWL</a>): a promise that has not reached its potential. The second story is Concise Common Workflow Language (<a href="https://ccwl.systemreboot.net/">CCWL</a>): compiling Guile/Scheme workflow descriptions to CWL inputs. The third story is <a href="https://forge.systemreboot.net/ravanan/">Ravanan</a>: a CWL implementation powered by <a href="https://hpc.guix.info/">Guix</a> – a transparent and reproducible package manager.</p> <p>This talk is a threefold short story that makes one: long-term, transparent and reproducible workflow needs first package managers.</p> <hr /> <p><strong>VTX, High Performance Visualization of Molecular Structure and Trajectories</strong> VTX is a molecular visualization software capable to handle most molecular structures and dynamics trajectories file formats. It features a real-time high-performance molecular graphics engine, based on modern OpenGL, optimized for the visualization of massive molecular systems and molecular dynamics trajectories. VTX includes multiple interactive camera and user interaction features, notably free-fly navigation and a fully modular graphical user interface designed for increased usability. It allows the production of high-resolution images for presentations and posters with custom background. VTX design is focused on performance and usability for research, teaching, and educative purposes. Please visit our website at https://vtx.drugdesign.fr/ and/or our github at https://github.com/VTX-Molecular-Visualization for more.</p> <hr /> <p><strong>Multimodal Tumor Evolution Analysis: Interactive 4D CT and Time-Aligned Clinical Data in a Hospital Web Platform</strong> Modern oncology practice relies on understanding how tumors evolve across multiple imaging studies and how these changes correlate with clinical events. This talk presents a hospital-oriented web platform for multimodal tumor evolution analysis, integrating interactive 4D CT visualization with time-aligned clinical data, including PDF clinical documents, lab results and treatment milestones.</p> <p>The system combines a Node.js front end with a Flask-based visualization backend that handles CT preprocessing, metadata extraction, and generation of time-synchronized 4D volumes. Clinicians can navigate volumetric CT scans across multiple time points, compare tumor morphology longitudinally, and immediately access the corresponding clinical context within the same interface. The platform displays radiology reports, pathology documents, and other PDF-based data side-by-side with imaging, creating a unified temporal view of patient evolution.</p> <p>We describe the architecture, including the ingestion pipeline for DICOM and document data, the design of the multimodal synchronization layer, rendering strategies for large 4D CT volumes, and the integration of document viewers and time-series dashboards.</p> <p>Web platform: https://github.com/owtlaw6/Licenta Flask App (CT Scan related scripts): https://github.com/fabi200123/4D_CT_Scan</p> <hr /> <p><strong>DNA storage and open-source projects</strong> The magnetic recording field goes back to the pioneering work of Oberlin Smith, who conceptualized a magnetic recording apparatus in 1878. Fast forward, in 1947, engineers invented the first high-speed, cathode ray tube based fully electronic memory. In 1950, engineers developed magnetic drum memory. In 1951, the first tape storage device was invented. By 1953, engineers had developed magnetic core memory. The first hard disk drive RAMAC was developed in 1957. Since then, HDDs have dominated the storage for several decades and continue to do so because of its low cost-per-gigabyte and low bit-error-rate. Based on some estimates, in 2023, approximately 330 million terabytes of data were created each day. By 2024, HDDs dominated over half of the world’s data storage. As of 2025, approximately 0.4 zettabytes of new data are being generated each day, which equals about 402.74 million terabytes. What does it indicate? Data is growing and there is a need of solutions in term of longevity, low power consumption, and high capacity. Deoxyribonucleic acid (DNA) based storage is being considered as one of the solutions. This talk is about current status of DNA storage and open-source projects that exist in this domain so far.</p> <hr /> <p><strong>From Binary to Granular: Automating Multi-Threshold Survival Analysis with OptSurvCutR</strong> In risk modelling, categorising continuous variables—such as biomarker levels or credit scores—is essential for creating distinct risk groups. While existing tools can optimize a single threshold (creating "High" vs "Low" groups), they lack a systematic framework for identifying multiple cut-points. This limitation forces analysts to rely on simple binary splits, which often mask the actual shape of the data. This approach fails to detect complex biological realities, such as U-shaped risk profiles or multi-step risk stratification involving 3, 4, or even 5+ distinct groups.</p> <p>In this lightning talk, I will introduce OptSurvCutR, an R package designed to bridge this gap using a reproducible workflow. Currently under peer review at rOpenSci, the package automates the search for optimal thresholds in time-to-event data.</p> <p>I will demonstrate how the package: - <strong>Goes Beyond Binary Splits</strong>: Unlike standard tools restricted to a single cut-off, <em>OptSurvCutR</em> uses systematic searches to identify multiple thresholds, automatically defining granular risk strata (e.g., Low, Moderate, High, Severe).</p> <ul> <li> <p><strong>Prevents False Positives</strong>: It integrates statistical corrections (MSRS) to ensure that the differences between these multiple curves are real, not just random chance.</p> </li> <li> <p><strong>Quantifies Uncertainty</strong>: It uses bootstrap validation to measure the stability of the thresholds, ensuring that your multi-level risk model is robust.</p> </li> </ul> <p>Project Links: - <strong>Source Code (GitHub)</strong>: https://github.com/paytonyau/OptSurvCutR - <strong>rOpenSci Review Process</strong>: https://github.com/ropensci/software-review/issues/731 - <strong>Preprint</strong>: https://doi.org/10.1101/2025.10.08.681246</p>