Scalable and Interpretable Visual Analytics for Big Data

The field of bioinformatics is witnessing a dramatic surge in data volumes due to the advent of advanced high-throughput technologies in areas such as genomics, proteomics, metabolomics, and biomedical imaging. As researchers collect increasingly complex and heterogeneous datasets, often encompassing terabytes of high-dimensional and temporally resolved information, the challenge of extracting meaningful insights has intensified. Traditional methods of data visualization, which are effective for smaller or simpler datasets, frequently fail to capture the intricate structure, uncertainty, and dynamic processes embedded within modern biological data. Furthermore, the integration of multi-omics, spatially mapped omics, cryo-electron microscopy, and digital pathology data greatly amplifies the need for visualization methods that are not only scalable but also interpretable by diverse users.Despite notable advances in graphics processing, distributed computing, and interactive web-based platforms, the ability to visually explore and analyze extremely large and multidimensional bioinformatics datasets remains a significant bottleneck. Current tools often struggle to balance computational efficiency, scalability, and user accessibility, and many solutions remain fragmented or tailored to narrow applications. There is a growing call for unified approaches that leverage algorithmic innovation, robust visual design, and user-centered development to ensure that visualizations provide actionable, reproducible insights and are feasible in real-world analytical workflows.This Research Topic seeks to showcase the latest developments in techniques and tools that empower researchers to visualize extremely large bioinformatics datasets with clarity and efficiency. By highlighting innovative, scalable, and interoperable solutions, this collection aims to bridge the gap between raw data volume and scientific understanding, supporting all facets of biological data exploration, hypothesis generation, and communication.To gather further insights in the scalable visualization of extremely large bioinformatics data, we welcome articles focusing on innovative methods, robust design strategies, and practical software tools. Submissions can address, but are not limited to, the following topics:- Scalable visual analytics frameworks for omics, single-cell, and population genomics, including streaming and incremental visualization- High-performance rendering and level-of-detail techniques, such as aggregation, tiling, sampling, and multiscale views for billion-element datasets- Visual integration of multi-omics, multimodal, spatial, and temporal bioinformatics data, with emphasis on interpretability- Web-based, cloud-native, and in-database visualization platforms that enable collaborative, reproducible analysis at scale- Human-centered and perception-aware approaches, including interaction methods, usability studies, and tools for non-expert users- Evaluation, benchmarking, and standards for scalable visualization, including comparative analyses, open datasets, and reproducibility metrics- Machine learning-driven visual analytics, such as embeddings, feature selection, and anomaly detection tightly coupled with visualization at scaleWe encourage contributions that feature open-source tools, FAIR-compliant resources, and solutions designed for broad applicability across the bioinformatics visualization community. Topic editors: Xin Lai, Carson Leung Source: https://www.frontiersin.org/research-topics/80281/scalable-and-interpretable-visual-analytics-for-big-data