Dataset Information

Soybean-VCF2Genomes: a database to identify the closest accession in soybean germplasm collection.

ABSTRACT: BACKGROUND:The development of next generation sequencer (NGS) and the analytical methods allowed the researchers to profile their samples more precisely and easier than before. Especially for agriculture, the certification of the genomic background of their plant materials would be important for the reliability of seed market and stable yield as well as for quarantine procedure. However, the analysis of NGS data is still difficult for non-computational researchers or breeders to verify their samples because majority of current softwares for NGS analysis require users to access unfamiliar Linux environment. MAIN BODY:Here, we developed a web-application, "Soybean-VCF2Genomes", http://pgl.gnu.ac.kr/soy_vcf2genome/ to map single sample variant call format (VCF) file against known soybean germplasm collection for identification of the closest soybean accession. Based on principal component analysis (PCA), we simplified genotype matrix for lowering computational burden while maintaining accurate clustering. With our web-application, users can simply upload single sample VCF file created by more than 10x resequencing strategy to find the closest samples along with linkage dendrogram of the reference genotype matrix. CONCLUSION:The information of the closest soybean cultivar will allow breeders to estimate relative germplasmic position of their query sample to determine soybean breeding strategies. Moreover, our VCF2Genomes scheme can be extended to other plant species where the whole genome sequences of core collection are publicly available.

SUBMITTER: Ha J

PROVIDER: S-EPMC6652137 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Soybean-VCF2Genomes: a database to identify the closest accession in soybean germplasm collection.

Ha Jungmin J Jeon Ho Hwi HH Woo Dong U DU Lee Yejin Y Park Halim H Lee Joohyeong J Kang Yang Jae YJ

BMC bioinformatics 20190724 Suppl 13

<h4>Background</h4>The development of next generation sequencer (NGS) and the analytical methods allowed the researchers to profile their samples more precisely and easier than before. Especially for agriculture, the certification of the genomic background of their plant materials would be important for the reliability of seed market and stable yield as well as for quarantine procedure. However, the analysis of NGS data is still difficult for non-computational researchers or breeders to verify t ...[more]

PMID: 31337332

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Project description:Germplasm banks are growing in their importance, number of accessions and amount of characterization data, with a large emphasis on molecular genetic markers. In this work, we offer an integrated view of accessions and marker data in an information theory framework. The basis of this development is the mutual information between accessions and allele frequencies for molecular marker loci, which can be decomposed in allele specificities, as well as in rarity and divergence of accessions. In this way, formulas are provided to calculate the specificity of the different marker alleles with reference to their distribution across accessions, accession rarity, defined as the weighted average of the specificity of its alleles, and divergence, defined by the Kullback-Leibler formula. Albeit being different measures, it is demonstrated that average rarity and divergence are equal for any collection. These parameters can contribute to the knowledge of the structure of a germplasm collection and to make decisions about the preservation of rare variants. The concepts herein developed served as the basis for a strategy for core subset selection called HCore, implemented in a publicly available R script. As a proof of concept, the mathematical view and tools developed in this research were applied to a large collection of Mexican wheat accessions, widely characterized by SNP markers. The most specific alleles were found to be private of a single accession, and the distribution of this parameter had its highest frequencies at low levels of specificity. Accession rarity and divergence had largely symmetrical distributions, and had a positive, albeit non-strictly linear relationship. Comparison of the HCore approach for core subset selection, with three state-of-the-art methods, showed it to be superior for average divergence and rarity, mean genetic distance and diversity. The proposed approach can be used for knowledge extraction and decision making in germplasm collections of diploid, inbred or outbred species.

Dataset Information

Soybean-VCF2Genomes: a database to identify the closest accession in soybean germplasm collection.

Publications

Soybean-VCF2Genomes: a database to identify the closest accession in soybean germplasm collection.

Similar Datasets

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