Next-generation sequencing (NGS) technologies generate thousands to millions of genetic variants per sample. Identification of potential disease-causal variants is labor intensive as it relies on filtering using various annotation metrics and consideration of multiple pathogenicity prediction scores. We have developed VPOT (variant prioritization ordering tool), a python-based command line tool that allows researchers to create a single fully customizable pathogenicity ranking score from any number of annotation values, each with a user-defined weighting. The use of VPOT can be informative when analyzing entire cohorts, as variants in a cohort can be prioritized. VPOT also provides additional functions to allow variant filtering based on a candidate gene list or by affected status in a family pedigree. VPOT outperforms similar tools in terms of efficacy, flexibility, scalability, and computational performance. VPOT is freely available for public use at GitHub (https://github.com/VCCRI/VPOT/). Documentation for installation along with a user tutorial, a default parameter file, and test data are provided.
随着第二代测序（NGS）技术的日益广泛应用，研究人员现在面临着从几十万到数百万的遗传变异数据。目前已有多种致病性预测算法，如CADD、PolyPhen-2、SIFT和MutationTaster2等，但还没有一种被普遍认为是最好的单一算法。因此，研究者通常采取使用多种方法进行预测，并认为由这些软件共同预测出的变异更具有致病性研究价值。这使得识别潜在疾病变异位点的工作量非常巨大。基于此，我们开发了VPOT（变异优先级排序工具），这是一个基于python语言的工具，它允许研究者从任意数量的注释值中自主创建一个致病性排名分数，其中每个值都具有用户所定义的权重。当分析多个队列时，使用VPOT可以提供更为有效的信息，因为用户可以选择任意一个队列中的变异被优先考虑。此外，VPOT还提供一个额外功能，即允许基于候选基因列表或受影响的家族谱系位点对变异进行筛选。相比于其他类似的工具，VPOT在效率、灵活性、可扩展性和计算性能方面均具有优势。用户可从GitHub (https://github.com/VCCRI/VPOT/) 中免费获得VPOT。我们同时附上安装说明、使用教程、默认参数文件以及测试数据。





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