Genetic Diversity and Population Structure in Türkiye Bread Wheat Genotypes Revealed by Simple Sequence Repeats (SSR) Markers

Mfind: a tool for DNA barcode analysis in angiosperms and its relationship with microsatellites using a sliding window algorithm

MAIN CONCLUSION

Mfind is a tool to analyze the impact of microsatellite presence on DNA barcode specificity. We found a significant correlation between barcode entropy and microsatellite count in angiosperm. Genetic barcodes and microsatellites are some of the identification methods in taxonomy and biodiversity research. It is important to establish a relationship between microsatellite quantification and genetic information in barcodes. In order to clarify the association between the genetic information in barcodes (expressed as Shannon's Measure of Information, SMI) and microsatellites count, a total of 330,809 DNA barcodes from the BOLD database (Barcode of Life Data System) were analyzed. A parallel sliding-window algorithm was developed to compute the Shannon entropy of the barcodes, and this was compared with the quantification of microsatellites like (AT)n, (AC)n, and (AG)n. The microsatellite search method utilized an algorithm developed in the Java programming language, which systematically examined the genetic barcodes from an angiosperm database. For this purpose, a computational tool named Mfind was developed, and its search methodology is detailed. This comprehensive study revealed a broad overview of microsatellites within barcodes, unveiling an inverse correlation between the sumz of microsatellites count and barcodes information. The utilization of the Mfind tool demonstrated that the presence of microsatellites impacts the barcode information when considering entropy as a metric. This effect might be attributed to the concise length of DNA barcodes and the repetitive nature of microsatellites, resulting in a direct influence on the entropy of the barcodes.

Identification and Analysis of Candidate Genes Associated with Yield Structure Traits and Maize Yield Using Next-Generation Sequencing Technology

The main challenge of agriculture in the 21st century is the continuous increase in food production. In addition to ensuring food security, the goal of modern agriculture is the continued development and production of plant-derived biomaterials. Conventional plant breeding methods do not allow breeders to achieve satisfactory results in obtaining new varieties in a short time. Currently, advanced molecular biology tools play a significant role worldwide, markedly contributing to biological progress. The aim of this study was to identify new markers linked to candidate genes determining grain yield. Next-generation sequencing, gene association, and physical mapping were used to identify markers. An additional goal was to also optimize diagnostic procedures to identify molecular markers on reference materials. As a result of the conducted research, 19 SNP markers significantly associated with yield structure traits in maize were identified. Five of these markers (28629, 28625, 28640, 28649, and 29294) are located within genes that can be considered candidate genes associated with yield traits. For two markers (28639 and 29294), different amplification products were obtained on the electrophorograms. For marker 28629, a specific product of 189 bp was observed for genotypes 1, 4, and 10. For marker 29294, a specific product of 189 bp was observed for genotypes 1 and 10. Both markers can be used for the preliminary selection of well-yielding genotypes.