Triticeae Genomics and Genetics, 2024, Vol.15, No.2, 66-76 http://cropscipublisher.com/index.php/tgg 68 3 Genomic Resources inTriticeae 3.1 Reference genomes and genome assemblies The development of high-quality reference genomes and genome assemblies has been pivotal in advancing Triticeae research. The TRITEX workflow, an open-source computational tool, has significantly improved the assembly of large-genome Triticeae crops such as wheat and barley. This tool integrates various sequencing data types to construct sequence scaffolds with high contiguity, which are then ordered into chromosomal pseudomolecules. TRITEX has been successfully applied to tetraploid wild emmer, hexaploid bread wheat, and the barley cultivar Morex, providing valuable resources for the research community (Monat et al., 2019). Additionally, the generation of multiple wheat genome assemblies has revealed extensive structural variations and gene content differences among wheat lines, which are crucial for understanding the genetic basis of traits and for breeding purposes (Walkowiak et al., 2020). The release of a gold-standard, fully annotated reference wheat-genome assembly in 2018 marked a significant milestone, enabling more efficient exploitation of genomic resources in wheat breeding (Hussain et al., 2022). 3.2 Genomic databases and bioinformatics tools The establishment of genomic databases and bioinformatics tools has facilitated the analysis and interpretation of complex Triticeae genomes. GeneTribe, a novel homology inference method, incorporates gene collinearity to connect emerging genome assemblies within the Triticeae tribe. This tool has been integrated into the Triticeae-GeneTribe database, which includes 12 Triticeae genomes and 3 outgroup model genomes, providing versatile analysis and visualization functions (Chen et al., 2022) (Figure 1). Furthermore, the Zymoseptoria tritici ORFeome library, which includes over 3000 quality-controlled plasmids, represents a powerful resource for functional genomics studies, offering opportunities to understand the biology of this wheat pathogen (Chaudhari et al., 2019). Chen et al. (2020) addresses the challenges of homology inference in the Triticeae tribe, shaped by frequent polyploidization and reticulate evolution. It introduces a multi-level framework to organize assemblies and annotations, enabling efficient gene-gene mapping and hierarchical database construction. The framework classifies pairwise relationships into six levels, applying different strategies for each to enhance accuracy. The study highlights the limitations of sequence similarity-based methods, particularly for genetically similar assemblies, and incorporates additional information such as collinearity, annotation quality, and chromosome groups to improve inference accuracy. By decomposing polyploid genomes into diploid subgenomes and considering gene loss and duplication events, the proposed method aims to provide a more reliable inference of homology relationships. This approach enhances our understanding of Triticeae genomics and supports the development of more accurate genomic tools for crop improvement. 3.3 Genetic diversity and germplasm collections Efficient utilization of genetic diversity in germplasm collections is essential for crop improvement. Bioinformatic approaches have been developed to extract functional genetic diversity from heterogeneous germplasm collections. For instance, genome-wide SNP data can capture significantly more haplotypic diversity compared to traditional methods based on geographic or environmental data. This approach has been demonstrated in crops like sorghum, where subsets of germplasm collections were assembled to maximize diversity at functional loci relevant to breeding programs (Reeves et al., 2020). Additionally, a haplotype-led approach has been developed to increase the precision of wheat breeding by identifying genetic diversity using genome assemblies from multiple wheat cultivars. This method allows for the focused discovery of novel haplotypes, enhancing the efficiency and precision of breeding efforts (Brinton et al., 2020). 3.4 Functional genomics resources Functional genomics resources are crucial for understanding gene function and improving crop traits. The high-resolution genome-wide association study (GWAS) in wheat has identified numerous quantitative trait loci (QTL) and candidate genes associated with important agronomic traits. This study utilized a large panel of wheat cultivars and high-density SNP markers, providing a solid foundation for QTL fine mapping and candidate
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