Li Zhongshan, Liu Zhenwei, Jiang Yi, Chen Denghui, Ran Xia, Sun Zhong Sheng, Wu Jinyu
Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.
Beijing Institute of Life Science, Chinese Academy of Sciences, Beijing, China.
Hum Mutat. 2017 Jan;38(1):25-33. doi: 10.1002/humu.23125. Epub 2016 Oct 13.
Exome sequencing has been widely used to identify the genetic variants underlying human genetic disorders for clinical diagnoses, but the identification of pathogenic sequence variants among the huge amounts of benign ones is complicated and challenging. Here, we describe a new Web server named mirVAFC for pathogenic sequence variants prioritizations from clinical exome sequencing (CES) variant data of single individual or family. The mirVAFC is able to comprehensively annotate sequence variants, filter out most irrelevant variants using custom criteria, classify variants into different categories as for estimated pathogenicity, and lastly provide pathogenic variants prioritizations based on classifications and mutation effects. Case studies using different types of datasets for different diseases from publication and our in-house data have revealed that mirVAFC can efficiently identify the right pathogenic candidates as in original work in each case. Overall, the Web server mirVAFC is specifically developed for pathogenic sequence variant identifications from family-based CES variants using classification-based prioritizations. The mirVAFC Web server is freely accessible at https://www.wzgenomics.cn/mirVAFC/.
外显子组测序已被广泛用于识别导致人类遗传疾病的基因变异以用于临床诊断,但在大量良性变异中识别致病序列变异既复杂又具有挑战性。在此,我们描述了一种名为mirVAFC的新型网络服务器,用于从单个个体或家族的临床外显子组测序(CES)变异数据中对致病序列变异进行优先级排序。mirVAFC能够全面注释序列变异,使用自定义标准过滤掉大多数不相关的变异,根据估计的致病性将变异分类到不同类别,最后根据分类和突变效应提供致病变异的优先级排序。使用来自已发表文献和我们内部数据的针对不同疾病的不同类型数据集进行的案例研究表明,mirVAFC在每种情况下都能像原始研究一样有效地识别出正确的致病候选基因。总体而言,网络服务器mirVAFC是专门为使用基于分类的优先级排序从基于家族的CES变异中识别致病序列变异而开发的。可通过https://www.wzgenomics.cn/mirVAFC/免费访问mirVAFC网络服务器。