AI-Assisted Comparative and Functional Genomics for Crop Stress Resilience

Crop production is increasingly challenged by heat, drought, emerging pathogens, and other environmental stresses, including their combined effect. Advances in genome sequencing and functional genomics are providing new opportunities to understand the genetic basis of stress adaptation and to use this information for crop improvement. However, translating the rapidly growing volume of genomic data into genes, regulatory mechanisms, and useful breeding targets remains a major challenge. Rice and Brassica provide particularly useful systems for investigating these questions. Although they are evolutionarily distant, both are major crop species for which extensive genomic resources, high-quality reference genomes, pangenomes, transcriptomic datasets, and genetic mapping populations are increasingly available. Studies in these crops have identified many genes and regulatory mechanisms associated with heat and drought tolerance, disease resistance, and other agronomically important traits. Comparative analysis across such diverse crop lineages can help determine which stress-response mechanisms are conserved and which have evolved in a lineage- or species-specific manner. Recent developments in long-read sequencing, chromosome-scale genome assembly, pangenomics, transcriptomics, and other multi-omics approaches have further expanded the possibilities for comparative and functional studies. These technologies allow structural variation, gene-family evolution, regulatory variation, and genotype-to-phenotype relationships to be investigated at a resolution that was difficult to achieve previously. Nevertheless, integrating these different layers of information and identifying biologically meaningful candidates from large genomic datasets remain important computational challenges. Artificial intelligence (AI) and machine learning can complement established genomic approaches in addressing some of these challenges. Applications include assessing and improving genome assemblies, prioritizing candidate genes within GWAS and QTL intervals, identifying patterns in large transcriptomic and multi-omics datasets, predicting gene function and regulatory elements, and integrating heterogeneous genomic information. Of particular interest are approaches that are biologically interpretable and can be evaluated across different genotypes, species, or crop systems rather than being restricted to a single dataset. This research topic will focus on the use of comparative and functional genomics, advanced sequencing technologies, and computational approaches to understand the genetic basis of biotic and abiotic stress resilience in crops. Rice and Brassica will serve as important anchor systems for the collection, but contributions involving other crop species and their wild relatives are also encouraged where they provide comparative insights, reveal conserved or lineage-specific stress-response mechanisms, or introduce approaches that can be transferred across crop systems. We particularly welcome studies addressing:• Comparative analysis of genes, gene families, regulatory elements, and pathways involved in heat, drought, disease resistance, and other stress responses.• Chromosome-scale genome assemblies, pangenomes, and structural variation associated with crop adaptation and stress resilience. • Functional genomics studies using transcriptomics, epigenomics, proteomics, metabolomics, or integrated multi-omics approaches. • GWAS and QTL studies aimed at identifying genomic regions and candidate genes associated with biotic and abiotic stress tolerance. • AI and machine-learning approaches for candidate-gene prioritization, genome annotation, assembly assessment, variant interpretation, or genomic prediction. • Host–pathogen genomics integrating pathogen variation or effector biology with host genomic and functional responses. • Gene-editing and transgenic studies investigating genes or regulatory elements involved in crop stress resilience. • Comparative studies examining conserved and lineage-specific mechanisms among rice, Brassica, other crop species, and their wild relatives. • Translational genomics connecting genomic discoveries with marker development, haplotype analysis, genomic selection, precision breeding, and crop-improvement programs. We welcome Original Research, Reviews, Methods, and other relevant article types that address these areas. Through this collection, we aim to bring together researchers working in crop genetics, comparative and functional genomics, bioinformatics, AI and machine learning, sequencing technologies, and crop breeding. The broader goal is to understand how genomic information can be translated more effectively into biological knowledge and ultimately into strategies for developing crops with improved resilience to changing environmental and disease pressures. Topic editors: Kumar Paritosh, Gunvant B Patil, Sivasubramanian Rajarammohan, Nitin Uttam Kamble Source: https://www.frontiersin.org/research-topics/85630/ai-assisted-comparative-and-functional-genomics-for-crop-stress-resilience