Allows users to interactively explore the consequences of sampling procedure for different sets of traits. MSTRAT defines a robust minimum size for the core collection based on actual patterns of redundancy in the collection. It is based on the M strategy which examines all possible core collections and singles out those that maximize the number of observed alleles at the marker loci.
Permits the development of core sets using the advanced M strategy. PowerCore indicates a high coincidence rate value for the core sets. It takes into account all classes, without omission of any of its variables. This tool covers all distribution ranges of each class. It implements a heuristic algorithm that is able to calculate the costs to reach the goal to select candidate entries.
Permits the selection of core collections. Core Hunter treats core selection as a formal optimization problem by directly sampling from the entire collection, optimizing a given diversity measure. It prevents the search stalling in local optima by using Replica Exchange Monte Carlo (REMC) search. This tool is able to build cores based on genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many provided evaluation measures depending on the precise purpose of the core.
Selects population subsets. STPGA is based on a modified genetic algorithm (GA) with tabu search and look ahead property. It assesses and chooses the elite individuals and subsequently the next population is formed from the elites by genetically motivated operations such as crossover and mutation. This tool can be run in parallel to solve the subset selection problem for any given objective function.
Offers a method to select a core collection using modified statistical measures related to genetic allele coverage and diversity. GenoCore is an R package developed to select a core collection from large, diverse genetic datasets. It can be used for large datasets such as high-density single nucleotide polymorphism (SNP) arrays and next-generation sequencing (NGS). Compared with similar methods, GenoCore has a biggest minMR, so the minimum genetic distance between samples in the core collection is largest.