University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183 Rouen cedex, France.
BMC Bioinformatics. 2012;13 Suppl 14(Suppl 14):S9. doi: 10.1186/1471-2105-13-S14-S9. Epub 2012 Sep 7.
Whole exome sequencing (WES) has become the strategy of choice to identify a coding allelic variant for a rare human monogenic disorder. This approach is a revolution in medical genetics history, impacting both fundamental research, and diagnostic methods leading to personalized medicine. A plethora of efficient algorithms has been developed to ensure the variant discovery. They generally lead to ~20,000 variations that have to be narrow down to find the potential pathogenic allelic variant(s) and the affected gene(s). For this purpose, commonly adopted procedures which implicate various filtering strategies have emerged: exclusion of common variations, type of the allelics variants, pathogenicity effect prediction, modes of inheritance and multiple individuals for exome comparison. To deal with the expansion of WES in medical genomics individual laboratories, new convivial and versatile software tools have to implement these filtering steps. Non-programmer biologists have to be autonomous combining themselves different filtering criteria and conduct a personal strategy depending on their assumptions and study design.
We describe EVA (Exome Variation Analyzer), a user-friendly web-interfaced software dedicated to the filtering strategies for medical WES. Thanks to different modules, EVA (i) integrates and stores annotated exome variation data as strictly confidential to the project owner, (ii) allows to combine the main filters dealing with common variations, molecular types, inheritance mode and multiple samples, (iii) offers the browsing of annotated data and filtered results in various interactive tables, graphical visualizations and statistical charts, (iv) and finally offers export files and cross-links to external useful databases and softwares for further prioritization of the small subset of sorted candidate variations and genes. We report a demonstrative case study that allowed to identify a new candidate gene related to a rare form of Alzheimer disease.
EVA is developed to be a user-friendly, versatile, and efficient-filtering assisting software for WES. It constitutes a platform for data storage and for drastic screening of clinical relevant genetics variations by non-programmer geneticists. Thereby, it provides a response to new needs at the expanding era of medical genomics investigated by WES for both fundamental research and clinical diagnostics.
全外显子测序（WES）已成为鉴定罕见人类单基因疾病编码等位变异的首选策略。这种方法是医学遗传学史上的一次革命，影响了基础研究和导致个性化医疗的诊断方法。已经开发出大量有效的算法来确保变异的发现。它们通常会产生约 20,000 种变异，需要进行筛选，以找到潜在的致病性等位变异和受影响的基因。为此，通常采用涉及各种过滤策略的程序：排除常见变异、等位变异类型、致病性效应预测、遗传模式和多个个体进行外显子比较。为了应对医学基因组学中 WES 的扩展，个体实验室必须实施新的方便且通用的软件工具来执行这些过滤步骤。非程序员生物学家必须能够自主结合不同的过滤标准，并根据他们的假设和研究设计制定个人策略。
我们描述了 EVA（外显子变异分析器），这是一种用户友好的网络界面软件，专门用于医疗 WES 的过滤策略。借助不同的模块，EVA（i）集成并存储注释的外显子变异数据，严格保密给项目所有者，（ii）允许结合处理常见变异、分子类型、遗传模式和多个样本的主要过滤器，（iii）提供注释数据和过滤结果的浏览，以及各种交互式表格、图形可视化和统计图表，（iv）最后提供导出文件和交叉链接到外部有用的数据库和软件，以进一步对排序候选变异和基因的小子集进行优先级排序。我们报告了一个示范案例研究，该研究确定了与罕见阿尔茨海默病形式相关的新候选基因。
EVA 是为 WES 开发的一种用户友好、通用且高效的过滤辅助软件。它是一个数据存储平台，也是非程序员遗传学家进行临床相关遗传变异大规模筛选的平台。因此，它为 WES 调查的医学基因组学新时代的新需求提供了响应，无论是基础研究还是临床诊断。
