Arumilli Meharji, Layer Ryan M, Hytönen Marjo K, Lohi Hannes
Department of Veterinary Biosciences, Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.
Genetics Research Program, The Folkhälsan Research Center, Helsinki, Finland.
Front Genet. 2020 Mar 3;11:152. doi: 10.3389/fgene.2020.00152. eCollection 2020.
Genotype Query Tools (GQT) were developed to discover disease-causing variations from billions of genotypes and millions of genomes, processes data at substantially higher speed over other existing methods. While GQT has been available to a wide audience as command-line software, the difficulty of constructing queries among non-IT or non-bioinformatics researchers has limited its applicability. To overcome this limitation, we developed webGQT, an easy-to-use tool with a graphical user interface. With pre-built queries across three modules, webGQT allows for pedigree analysis, case-control studies, and population frequency studies. As a package, webGQT allows researchers with less or no applied bioinformatics/IT experience to mine potential disease-causing variants from billions.
webGQT offers a flexible and easy-to-use interface for model-based candidate variant filtering for Mendelian diseases from thousands to millions of genomes at a reduced computation time. Additionally, webGQT provides adjustable parameters to reduce false positives and rescue missing genotypes across all modules. Using a case study, we demonstrate the applicability of webGQT to query non-human genomes. In addition, we demonstrate the scalability of webGQT on large data sets by implementing complex population-specific queries on the 1000 Genomes Project Phase 3 data set, which includes 8.4 billion variants from 2504 individuals across 26 different populations. Furthermore, webGQT supports filtering single-nucleotide variants, short insertions/deletions, copy number or any other variant genotypes supported by the VCF specification. Our results show that webGQT can be used as an online web service, or deployed on personal computers or local servers within research groups.
webGQT is made available to the users in three forms: 1) as a webserver available at https://vm1138.kaj.pouta.csc.fi/webgqt/, 2) as an R package to install on personal computers, and 3) as part of the same R package to configure on the user's own servers. The application is available for installation at https://github.com/arumds/webgqt.
基因型查询工具（GQT）旨在从数十亿个基因型和数百万个基因组中发现致病变异，其处理数据的速度比其他现有方法快得多。虽然GQT作为命令行软件已被广泛使用，但非信息技术或非生物信息学研究人员构建查询的难度限制了其适用性。为克服这一限制，我们开发了webGQT，这是一个带有图形用户界面的易用工具。通过三个模块中的预建查询，webGQT可进行系谱分析、病例对照研究和群体频率研究。作为一个软件包，webGQT使几乎没有或没有应用生物信息学/信息技术经验的研究人员能够从数十亿个数据中挖掘潜在的致病变异。
webGQT为基于模型的候选变异筛选提供了一个灵活且易用的界面，用于从数千到数百万个基因组中筛选孟德尔疾病的变异，同时减少计算时间。此外，webGQT提供了可调整的参数，以减少所有模块中的假阳性并挽救缺失的基因型。通过一个案例研究，我们展示了webGQT查询非人类基因组的适用性。此外，我们通过对1000基因组计划第三阶段数据集（其中包括来自26个不同群体的2504个个体的84亿个变异）实施复杂的群体特异性查询，展示了webGQT在大数据集上的可扩展性。此外，webGQT支持筛选单核苷酸变异、短插入/缺失、拷贝数或VCF规范支持的任何其他变异基因型。我们的结果表明，webGQT既可以用作在线网络服务，也可以部署在研究小组内的个人计算机或本地服务器上。
webGQT以三种形式提供给用户：1）作为一个网络服务器，可在https://vm1138.kaj.pouta.csc.fi/webgqt/上使用；2）作为一个R包，可安装在个人计算机上；3）作为同一个R包的一部分，可在用户自己的服务器上进行配置。该应用程序可在https://github.com/arumds/webgqt上进行安装。
