Analysis of Genomic Variability within Inbred Populations with NGSEP

The development and availability of high throughput sequencing (HTS) technologies and different bioinformatic tools revolutionized the research on genomics allowing to obtain genome-wide data on entire populations of nearly every form of life. Research communities of inbred species, such as rice or beans, are developing large datasets of variability from Whole Genome Sequencing (WGS) data, and also performing Genotype by Sequencing (GBS) experiments on structured mapping and breeding populations. Unlike outbred species, haplotypes in these populations can be obtained and analyzed directly from genotype calls. However, most bioinformatics tools can not take advantage of this feature because they were mostly developed for human genetics. Our bioinformatics group recently developed the Next Generation Sequencing Experience Platform (NGSEP) for accurate, efficient, and user-friendly analysis of HTS data. We showed using both WGS and GBS data that, for inbred species, NGSEP provides better accuracy compared to other commonly used software for variants detection. Here we present the latest functionalities implemented in NGSEP for analysis of genomic variability datasets in VCF format from populations of inbred samples: 1) A Hidden Markov Model (HMM) for genotype imputation, from either known or unknown parental haplotypes, 2) Assessment of diversity across the genome through allele sharing statistics, and 3) Analysis of introgression of foreign haplotypes within subpopulations. These analysis options have been useful to analyze large genomic datasets such as that provided by the 3000 rice genomes project and we expect they will be useful for further sequencing efforts in different inbred crops.