Core collection is important method for enhancing the effectiveness of cost and shortening the time for analysis of genetic diversity, population structure, genome-wide association study and breeding, etc. Recently, a large number of genetic markers (> 100,000 SNPs) are included in high-density SNP arrays and next generation sequencing data. However, there exists no software for picking out the efficient and consistent core subset from a huge dataset such as high-density SNP-chip and nextgeneration sequencing technology. It is necessary to develop software which can take genetically important samples in population and have coherence and high coverage.
We provide a new program, GenoCore, which can quickly and efficiently find the core subset covering genetic diversity for the entire population. We introduce simple measure, coverage and diversity score which reflect genotype errors and genetic variations, and help to select a sample rapidly and accurately. Comparing our method to other core collection software using example datasets is meaningful in order to validate its performance using genetic distance, diversity, coverage, required system resource and the number of selected samples,. GenoCore selects the smallest, most consistent, and representative core collection from all samples, using less memory and efficient scores and has more genetic coverage than does the other software.
GenoCore was written in R language, and can be accessed online with example dataset and test results, https://github.com/lovemun/Genocore.
We provide a new program, GenoCore, which can quickly and efficiently find the core subset covering genetic diversity for the entire population. We introduce simple measure, coverage and diversity score which reflect genotype errors and genetic variations, and help to select a sample rapidly and accurately. Comparing our method to other core collection software using example datasets is meaningful in order to validate its performance using genetic distance, diversity, coverage, required system resource and the number of selected samples,. GenoCore selects the smallest, most consistent, and representative core collection from all samples, using less memory and efficient scores and has more genetic coverage than does the other software.
GenoCore was written in R language, and can be accessed online with example dataset and test results, https://github.com/lovemun/Genocore.