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

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

机构信息

Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA.

出版信息

Hum Mutat. 2013 Jun;34(6):842-6. doi: 10.1002/humu.22305. Epub 2013 Apr 3.

Abstract

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 (https://genomics.med.miami.edu/). 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 nonbioinformaticians to make next-generation sequencing 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 sec across ∼1,200 exomes. We believe that this system will further enhance identification of genetic causes of human disease.

摘要

现在,许多孟德尔疾病和遗传复杂表型的新基因都在被快速识别。然而,新的挑战也变得明显:(1)有效地管理更大的外显子组和/或基因组数据集,特别是对于较小的实验室;(2)直接对大型基因组数据集中的变体数据进行分析和上下文解释;(3)世界各地许多小型和中型临床和基于研究的调查团队正在生成数据,如果将这些数据进行组合和共享,将极大地增加整个社区识别新基因的机会。为了解决这些挑战,我们开发了 GEnomes Management Application(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(https://genomics.med.miami.edu/)。GEM.app 目前包含来自 15 个不同国家的 40 位主要研究人员研究的 50 种不同表型的约 1600 个全外显子组。GEM.app 的重点是为非生物信息学家提供用户友好的分析,使下一代测序数据能够直接访问。然而,GEM.app 提供了强大而灵活的筛选选项,包括单一家系筛选、跨家系/表型查询、嵌套筛选以及在家系中评估分离。此外,该系统速度很快,在大约 1200 个外显子组中可以在 4 秒内获得结果。我们相信,这个系统将进一步增强对人类疾病遗传原因的识别。

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