DNAPOD: Implementation of Virtual Machine for DNAPOD Workflow and Genotype Imputation

With advances in next-generation sequencing, datasets for DNA polymorphism among various species and strains have been produced, stored and distributed from the websites of individual genotyping projects. However, reliability among genotype datasets varies because the experimental and analytical conditions are different among assays. It is desirable to integrate DNA polymorphism data into a database with uniform quality control distributed from a single platform in a single dataset. The DNA polymorphism annotation database (DNApod) (http://tga.nig.ac.jp/dnapod/) is an integrated database that stores genome-wide DNA polymorphism datasets acquired under uniform analytical conditions, including the quality of the raw data, reference genome version and evaluation algorithms. DNApod genotypic data is re-analyzed whole-genome shotgun datasets extracted from the Sequence Read Archive and distributes genome-wide DNA polymorphism datasets and known-gene annotations for each DNA polymorphism. The purpose of the database is to store genome-wide DNA polymorphism datasets of plants, with crops as a first priority. The analytical methods are available as a DNApod workflow in the DDBJ Read Annotation Pipeline (DDBJ pipeline) (https://p.ddbj.nig.ac.jp/) and a virtual machine image configured in the Galaxy platform by Pitagora-Galaxy (http://www.pitagora-galaxy.org/). The DNApod genotypic database currently contains 679 rice lines, 404 maize lines, and 66 sorghum lines. Eighty-seven percent of DNApod genotypic data have a coverage depth <5 on a reference genome. Shallow-coverage data tend to generate false-negative results. For these data, we are constructing a workflow of genotype imputation.