Abstract
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.
Highlights
Peer review under responsibility of Beijing Institute of Genomics, Chinese Academy of Sciences and Genetics Society of China.With the increasing use of next-generation sequencing (NGS) methods, researchers are faced with many genetic variants, from hundreds of thousands to millions, to evaluate
Based on the family pedigree which shows that the parents were consanguineous, we used variant prioritization ordering tool (VPOT)’s inheritance model filtering to refine the number of candidate variants based on an autosomal recessive inheritance model (AR) (Figure 2)
After application of inheritance model filtering, 14 variants remained with a HAAO homozygous variant ranked first, consistent with the reported genetic cause in this family (Table 1 and Table S1) [17]
Summary
With the increasing use of next-generation sequencing (NGS) methods, researchers are faced with many genetic variants, from hundreds of thousands to millions, to evaluate. Software such as ANNOVAR and VEP [1,2] use databases that provide functional consequences, pathogenicity predictions, and population frequencies to annotate genetic variants.
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