VPOT:A Customizable Variant Prioritization Ordering Tool for Annotated Variants

被引:0
作者
Eddie Ip [1 ,2 ]
Gavin Chapman [1 ,2 ]
David Winlaw [3 ,4 ]
Sally LDunwoodie [1 ,2 ,5 ]
Eleni Giannoulatou [1 ,2 ]
机构
[1] Victor Chang Cardiac Research Institute
[2] St Vincent's Clinical School,University of New South Wales
[3] Heart Centre for Children,The Children's Hospital at Westmead
[4] Sydney Medical School,University of Sydney
[5] School of Biotechnology and Biomolecular Sciences,University of New South Wales
关键词
Next-generation sequencing; Pathogenicity predictions; Variant prioritization; Customizable ranking; Genomic annotation;
D O I
暂无
中图分类号
Q811.4 [生物信息论];
学科分类号
0711 ; 0831 ;
摘要
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 Git Hub(https://github.com/VCCRI/VPOT/).Documentation for installation along with a user tutorial,a default parameter file,and test data are provided.
引用
收藏
页码:540 / 545
页数:6
相关论文
共 3 条
[1]  
Variant Ranker: a web-tool to rank genomic data according to functional significance[J] . John Alexander,Dimitris Mantzaris,Marianthi Georgitsi,Petros Drineas,Peristera Paschou.BMC Bioinformatics . 2017 (1)
[2]  
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models[J] . Hashem A. Shihab,Julian Gough,David N. Cooper,Peter D. Stenson,Gary L. A. Barker,Keith J. Edwards,Ian N. M. Day,Tom R. Gaunt.Human Mutation . 2012 (1)
[3]  
Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++[J] . Eugene V. Davydov,David L. Goode,Marina Sirota,Gregory M. Cooper,Arend Sidow,Serafim Batzoglou.PLOS Computational Biology . 2010 (12)