The rapid expansion of high-throughput technologies in genomics, transcriptomics, proteomics, metabolomics, and systems biology has transformed modern biological research into a data-intensive discipline. These technologies generate large, high-dimensional, heterogeneous, and often noisy datasets. Data visualization plays a central role in making such data interpretable by revealing patterns, trends, relationships, and anomalies that are difficult to detect through numerical summaries alone. In bioinformatics, visualization is not only a means of communication but also a key component of exploration, quality assessment, and hypothesis generation.This Research Topic aims to showcase and critically evaluate methods, tools, and algorithms for data visualization in bioinformatics. It seeks to highlight best practices, expose limitations, and identify emerging opportunities for visual analytics that support reliable, transparent, and interpretable analysis of complex biological data.We invite contributions that address data visualization in bioinformatics from methodological, algorithmic, and practical perspectives. Subthemes of particular interest include, but are not limited to:- Visualization of high-dimensional omics data, including single cell, time series, and spatially resolved datasets.- Algorithmic foundations of visualization, such as clustering, dimensionality reduction, graph layout, multi omics integration, and uncertainty representation.- Comparative evaluations and benchmarks of visualization methods and tools used in genomics, transcriptomics, proteomics, metabolomics, and network biology.- Design and assessment of interactive visualization systems, dashboards, and visual analytics workflows for bioinformatics.- Reproducible and scalable visualization practices, including integration with analysis pipelines in R, Python, and other platforms.We welcome three main types of manuscripts: mini reviews, systematic reviews, and full reviews. Mini reviews should provide concise, focused overviews on a specific tool, algorithmic family, or application domain in visualization bioinformatics. Systematic reviews should follow a transparent, predefined methodology to comprehensively assess the literature on a well-defined visualization topic, including explicit inclusion criteria and, where appropriate, quantitative or qualitative synthesis. Full reviews should offer broad, in depth coverage of major themes in data visualization bioinformatics, integrating methodological, technical, and application-oriented perspectives, and outlining conceptual frameworks or future research agendas. Together, these article types will provide both focused insights and broad syntheses to guide researchers, tool developers, and practitioners working at the intersection of data visualization and bioinformatics.
The rapid expansion of high-throughput technologies in genomics, transcriptomics, proteomics, metabolomics, and systems biology has transformed modern biological research into a data-intensive discipline. These technologies generate large, high-dimensional, heterogeneous, and often noisy datasets. Data visualization plays a central role in making such data interpretable by revealing patterns, trends, relationships, and anomalies that are difficult to detect through numerical summaries alone. In bioinformatics, visualization is not only a means of communication but also a key component of exploration, quality assessment, and hypothesis generation.This Research Topic aims to showcase and critically evaluate methods, tools, and algorithms for data visualization in bioinformatics. It seeks to highlight best practices, expose limitations, and identify emerging opportunities for visual analytics that support reliable, transparent, and interpretable analysis of complex biological data.We invite contributions that address data visualization in bioinformatics from methodological, algorithmic, and practical perspectives. Subthemes of particular interest include, but are not limited to:- Visualization of high-dimensional omics data, including single cell, time series, and spatially resolved datasets.- Algorithmic foundations of visualization, such as clustering, dimensionality reduction, graph layout, multi omics integration, and uncertainty representation.- Comparative evaluations and benchmarks of visualization methods and tools used in genomics, transcriptomics, proteomics, metabolomics, and network biology.- Design and assessment of interactive visualization systems, dashboards, and visual analytics workflows for bioinformatics.- Reproducible and scalable visualization practices, including integration with analysis pipelines in R, Python, and other platforms.We welcome three main types of manuscripts: mini reviews, systematic reviews, and full reviews. Mini reviews should provide concise, focused overviews on a specific tool, algorithmic family, or application domain in visualization bioinformatics. Systematic reviews should follow a transparent, predefined methodology to comprehensively assess the literature on a well-defined visualization topic, including explicit inclusion criteria and, where appropriate, quantitative or qualitative synthesis. Full reviews should offer broad, in depth coverage of major themes in data visualization bioinformatics, integrating methodological, technical, and application-oriented perspectives, and outlining conceptual frameworks or future research agendas. Together, these article types will provide both focused insights and broad syntheses to guide researchers, tool developers, and practitioners working at the intersection of data visualization and bioinformatics.