VCF/Plotein: visualization and prioritization of genomic variants from human exome sequencing projects

Abstract Motivation Identifying disease-causing variants from exome sequencing projects remains a challenging task that often requires bioinformatics expertise. Here we describe a user-friendly graphical application that allows medical professionals and bench biologists to prioritize and visualize genetic variants from human exome sequencing data. Results We have implemented VCF/Plotein, a graphical, fully interactive web application able to display exome sequencing data in VCF format. Gene and variant information is extracted from Ensembl. Cross-referencing with external databases and application-based gene and variant filtering have also been implemented. All data processing is done locally by the user’s CPU to ensure the security of patient data. Availability and implementation Freely available on the web at https://vcfplotein.liigh.unam.mx. Website implemented in JavaScript using the Vue.js framework, with all major browsers supported. Source code freely available for download at https://github.com/raulossio/VCF-plotein. Supplementary information Supplementary data are available at Bioinformatics online.

[1]  Tudor Groza,et al.  Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources , 2018, Nucleic Acids Res..

[2]  Deanna M. Church,et al.  ClinVar: public archive of relationships among sequence variation and human phenotype , 2013, Nucleic Acids Res..

[3]  D. Bishop,et al.  A population-based analysis of germline BAP1 mutations in melanoma , 2017, Human molecular genetics.

[4]  Gonçalo R. Abecasis,et al.  The variant call format and VCFtools , 2011, Bioinform..

[5]  Varun Ramraj,et al.  BrowseVCF: a web-based application and workflow to quickly prioritize disease-causative variants in VCF files , 2016, bioRxiv.

[6]  P. Bork,et al.  A method and server for predicting damaging missense mutations , 2010, Nature Methods.

[7]  Andrew J. Hill,et al.  Analysis of protein-coding genetic variation in 60,706 humans , 2015, bioRxiv.

[8]  G. Abecasis,et al.  Exome sequencing and complex disease: practical aspects of rare variant association studies , 2012, Human molecular genetics.

[9]  F. Cunningham,et al.  The Ensembl Variant Effect Predictor , 2016, Genome Biology.

[10]  Steven N. Hart,et al.  VCF-Miner: GUI-based application for mining variants and annotations stored in VCF files , 2015, Briefings Bioinform..

[11]  Mingming Jia,et al.  COSMIC: somatic cancer genetics at high-resolution , 2016, Nucleic Acids Res..

[12]  Steven Henikoff,et al.  SIFT: predicting amino acid changes that affect protein function , 2003, Nucleic Acids Res..

[13]  I. Adzhubei,et al.  Predicting Functional Effect of Human Missense Mutations Using PolyPhen‐2 , 2013, Current protocols in human genetics.

[14]  Elizabeth M. Smigielski,et al.  dbSNP: the NCBI database of genetic variation , 2001, Nucleic Acids Res..

[15]  Christian Gilissen,et al.  Unlocking Mendelian disease using exome sequencing , 2011, Genome Biology.

[16]  Aaron R. Quinlan,et al.  GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations , 2013, PLoS Comput. Biol..

[17]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[18]  James Y. Zou Analysis of protein-coding genetic variation in 60,706 humans , 2015, Nature.

[19]  Francisco Salavert,et al.  A web-based interactive framework to assist in the prioritization of disease candidate genes in whole-exome sequencing studies , 2014, Nucleic Acids Res..