Signal-Processing tools for core-collection selection from genetic-resource collection
Selecting a representative core collection (CC) is a proven and effective strategy for overcoming the expenses and difficulties of managing genetic resources in gene banks around the globe. Because of the diverse applications available for these sub-collections, several algorithms have been successfully implemented to construct them based on genotypic, phenotypic, passport or geographic data (either by individual datasets or by consensus). However, to the best of our knowledge, no single comprehensive datasets has been properly explored to date. Thus, researchers evaluate multiple datasets in order to construct representative CCs; this can be quite difficult, but one feasible solution for such an evaluation is to manage all available data as one discrete signal, which allows signal processing tools (SPTs) to be implemented during data analysis. In this research, we present a proof-of-concept study that shows the possibility of mapping to a discrete signal any type of data available from genetic resource collections in order to take advantage of SPTs for the construction of CCs that adequately represent the diversity of two crops. This method is referred to as ‘SPT selection.’ All available information for each element of the tested collections was analysed under this perspective and compared when possible, with one of the most used algorithms for CC selection. Genotype-only SPT selection did not prove as effective as standard CC selection did not prove as effective as standard CC selection algorithms; however, the SPT approach can consider genotype alongside other types of information, which results in well-represented Ccs that consider both the genotype and agromorphological diversities present in original collections. Furthermore, SPT-based analysis can evaluate all available data both in a comprehensive manner and under different perspective, and despite its limitations, the analysis renders satisfactory results. Thus, SPT-based algorithms for CC selection can be valuable in the field of genetic resources research, management and exploitation.
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