EasyOmics: Low- and No-Code Visualization and Analytics Methods in Metabolomics and Multi-omics

Computational biology and systems bioinformatics are entering a transformative phase where the integration of multi-layered biological data has become indispensable. Traditional single-omics analyses, focused on isolated molecular entities such as genes, proteins, or metabolites, are giving way to holistic approaches that unite genomics, transcriptomics, proteomics, and metabolomics into comprehensive multi-omics frameworks. These integrative strategies reveal complex biological interactions and enable a deeper understanding of cellular systems and their environmental contexts. However, the analytical demands of multi-omics data exceed the technical capacity of many researchers, as complex coding and advanced computational skills are often required. As a result, accessibility and reproducibility remain formidable challenges in the field. Recent advances in artificial intelligence, automated coding, and user-oriented bioinformatics platforms are beginning to bridge this gap, fostering new opportunities for democratized, transparent, and reproducible analyses.This Research Topic aims to consolidate and present validated strategies, frameworks, and workflows that facilitate multi-omics analyses through low- and no-code solutions. It seeks contributions that examine how user-friendly interfaces, automation, and visual analytics can empower non-specialized users to perform complex analyses with minimal programming expertise. The goal is to accelerate discovery while maintaining scientific rigor and transparency. Key questions include how such tools can ensure reproducibility, interoperability, and standardization across different layers of omics data, and how they might enable broader participation in computational biosciences through design innovation, education, and best practices.To gather further insights into the intersection of accessibility, innovation, and rigor in multi-omics data analysis, we welcome articles addressing, but not limited to, the following themes:• Development, benchmarking, and validation of low- or no-code omics analysis platforms• Case studies demonstrating successful implementation in metabolomics and multi-omics research• Interoperability, reproducibility, and data standardization in visual analytics workflows• Automated generation of standardized plots, visual reports, and dashboards• Design and assessment of user interfaces and experiences enhancing accessibility• Comparative studies of low-/no-code versus traditional code-based analytical workflows• Educational and training frameworks for empowering researchers in code-free omics analysis Topic editors: William C Ray, Joshua Blakeslee, Mattheos Koffas Source: https://www.frontiersin.org/research-topics/82388/easyomics-low--and-no-code-visualization-and-analytics-methods-in-metabolomics-and-multi-omics