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GEnomes Management Application (GEM.app): A new software tool for large-scale collaborative genome analysis

机译:GEnomes管理应用程序(GEM.app):用于大规模协作基因组分析的新软件工具

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摘要

Novel genes are now identified at a rapid pace for many Mendelian disorders, and increasingly, for genetically complex phenotypes. However, new challenges have also become evident: (1) effectively managing larger exome and/or genome datasets, especially for smaller labs; (2) direct hands-on analysis and contextual interpretation of variant data in large genomic datasets; and (3) many small and medium-sized clinical and research-based investigative teams around the world are generating data that, if combined and shared, will significantly increase the opportunities for the entire community to identify new genes. To address these challenges we have developed GEnomes Management Application (GEM.app), a software tool to annotate, manage, visualize, and analyze large genomic datasets (). GEM.app currently contains ~1,600 whole exomes from 50 different phenotypes studied by 40 principal investigators from 15 different countries. The focus of GEM.app is on user-friendly analysis for non-bioinformaticians to make NGS data directly accessible. Yet, GEM.app provides powerful and flexible filter options, including single family filtering, across family/phenotype queries, nested filtering, and evaluation of segregation in families. In addition, the system is fast, obtaining results within 4 seconds across ~1,200 exomes. We believe that this system will further enhance identification of genetic causes of human disease.
机译:现在,对于许多孟德尔疾病,以及越来越多的遗传上复杂的表型,新基因的识别速度很快。然而,新的挑战也变得显而易见:(1)有效地管理较大的外显子组和/或基因组数据集,特别是对于较小的实验室; (2)对大型基因组数据集中的变异数据进行直接动手分析和上下文解释; (3)世界各地的许多中小型临床和研究型研究小组正在生成数据,这些数据如果组合和共享,将大大增加整个社区识别新基因的机会。为了应对这些挑战,我们开发了GEnomes管理应用程序(GEM.app),这是一种用于注释,管理,可视化和分析大型基因组数据集的软件工具。 GEM.app当前包含来自50个不同表型的1600个完整外显子组,来自15个不同国家的40位主要研究者对此进行了研究。 GEM.app的重点是非生物信息学家的用户友好分析,以使NGS数据可直接访问。但是,GEM.app提供了强大而灵活的过滤器选项,包括跨族/表型查询的单族过滤,嵌套过滤以及对族隔离的评估。此外,该系统速度很快,可在约1200个外显子组内在4秒钟内获得结果。我们相信,该系统将进一步加强对人类疾病遗传原因的识别。

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