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Bioinformatics. 2018 Dec 15;34(24):4307-4309. doi: 10.1093/bioinformatics/bty536.

PopViz: a webserver for visualizing minor allele frequencies and damage prediction scores of human genetic variations.

Author information

1
St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA.
2
Laboratory of Human Genetics of Infectious Diseases, INSERM UMR1163, Paris, France.
3
Imagine Institute, Paris Descartes University, Paris, France.
4
Howard Hughes Medical Institute, New York, NY, USA.
5
Pediatric Immunology-Hematology Unit, Necker Hospital for Sick Children, Paris, France.
6
The Charles Bronfman Institute for Personalized Medicine.
7
Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Abstract

Summary:

Next-generation sequencing (NGS) generates large amounts of genomic data and reveals about 20 000 genetic coding variants per individual studied. Several mutation damage prediction scores are available to prioritize variants, but there is currently no application to help investigators to determine the relevance of the candidate genes and variants quickly and visually from population genetics data and deleteriousness scores. Here, we present PopViz, a user-friendly, rapid, interactive, mobile-compatible webserver providing a gene-centric visualization of the variants of any human gene, with (i) population-specific minor allele frequencies from the gnomAD population genetic database; (ii) mutation damage prediction scores from CADD, EIGEN and LINSIGHT and (iii) amino-acid positions and protein domains. This application will be particularly useful in investigations of NGS data for new disease-causing genes and variants, by reinforcing or rejecting the plausibility of the candidate genes, and by selecting and prioritizing, the candidate variants for experimental testing.

Availability and implementation:

PopViz webserver is freely accessible from http://shiva.rockefeller.edu/PopViz/.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
30535305
PMCID:
PMC6289133
[Available on 2019-12-15]
DOI:
10.1093/bioinformatics/bty536

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