Computational Multi-Omics Analysis of Abiotic Stress Tolerance in Crops

Abiotic stresses, including drought, heat, salinity, cold, and heavy metal toxicity, are among the most damaging constraints on global agricultural productivity, collectively responsible for more than 50% of crop yield losses worldwide. As climate change intensifies the frequency and severity of these stressors, understanding the molecular and genetic mechanisms that underpin stress tolerance has become a central priority in plant science. The rapid expansion of high-throughput omics technologies has made it possible to simultaneously interrogate the genome, transcriptome, proteome, metabolome, and epigenome of crop plants under stress conditions, generating datasets of extraordinary depth and complexity. However, translating these multi-layered molecular profiles into actionable biological knowledge requires sophisticated computational strategies capable of integrating heterogeneous data types, resolving regulatory hierarchies, and identifying the key molecular nodes that govern adaptive responses. Despite substantial progress, the field still lacks standardized multi-omics integration pipelines, and the mechanistic interpretation of cross-layer interactions under field-relevant stress conditions remains incomplete.This Research Topic aims to present and evaluate cutting-edge analytical strategies for decoding abiotic stress tolerance in crop species. The objective is to advance our understanding of how crops perceive, signal, and adapt to environmental stressors at the molecular level by leveraging integrative multi-omics methodologies. Authors are encouraged to present novel computational frameworks, benchmark existing tools, and report case studies that connect multi-omics findings to phenotypic outcomes and breeding-relevant targets. Special attention is given to studies that bridge the gap between computational discovery and applied crop improvement, including the identification of stress-responsive regulatory networks, biomarkers of tolerance, and candidate genes or pathways suitable for functional validation or genomic-assisted breeding. By compiling these contributions, this Research Topic seeks to define best practices and catalyse progress in stress biology research across economically and nutritionally important crops.We welcome articles addressing, but not limited to, the following themes:o Transcriptome-wide analysis of stress-responsive gene expression networkso Integration of genomics, transcriptomics, and metabolomics under abiotic stress conditionso Epigenomic regulation of stress-responsive gene expression, including DNA methylation and histone modification profilingo Proteomics and phosphoproteomics for characterising stress signalling cascadeso Machine learning and AI approaches for multi-omics data integration and stress phenotype predictiono Network biology and systems-level modelling of abiotic stress tolerance mechanismso Comparative multi-omics analysis across stress-tolerant and stress-sensitive crop genotypeso Single-cell and spatial omics approaches applied to stress response in plant tissueso Bioinformatics tool development and benchmarking for multi-omics integration in crop specieso Translational applications linking computational findings to genomic-assisted breeding and variety development Topic editors: Avneesh Kumar, UPASNA SRIVASTAVA, Ömer Faruk Coşkun Source: https://www.frontiersin.org/research-topics/84968/computational-multi-omics-analysis-of-abiotic-stress-tolerance-in-crops