Volume II: Methods, Tools and Algorithms in Data Visualization

High-throughput technologies in genomics, transcriptomics, proteomics, metabolomics, and systems biology have turned modern biology into a data-intensive discipline. These platforms produce large, high-dimensional, heterogeneous, and often noisy datasets whose scale outpaces our ability to interpret them. Data visualization addresses this gap: it reveals patterns, trends, relationships, and anomalies that numerical summaries alone cannot surface.In bioinformatics, visualization is more than a communication tool. It is central to data exploration, quality assessment, and hypothesis generation, and it shapes how researchers reason about complex biological systems.This Research Topic aims to showcase and critically evaluate the methods, tools, and algorithms that drive data visualization in bioinformatics. We seek contributions that highlight best practices, expose current limitations, and identify emerging opportunities for visual analytics supporting reliable, transparent, and interpretable analysis of complex biological data.We welcome methodological, algorithmic, and practical perspectives. Subthemes of particular interest include, but are not limited to:o Visualization of high-dimensional omics data, including single-cell, time-series, and spatially resolved datasets.o Algorithmic foundations of visualization, such as clustering, dimensionality reduction, graph layout, multi-omics integration, and uncertainty representation.o Comparative evaluations and benchmarks of visualization methods and tools across genomics, transcriptomics, proteomics, metabolomics, and network biology.o Design and assessment of interactive visualization systems, dashboards, and visual analytics workflows for bioinformatics.o Reproducible and scalable visualization practices, including integration with analysis pipelines in R, Python, and other platforms.Together, these contributions will offer both focused insights and broad syntheses to guide researchers, tool developers, and practitioners working at the intersection of data visualization and bioinformatics. Topic editors: Periyasamy Vijayalakshmi, Barbora Kozlikova Source: https://www.frontiersin.org/research-topics/86152/volume-ii-methods-tools-and-algorithms-in-data-visualization