VIVID: A Web Application for Variant Interpretation and Visualization in Multi-dimensional Analyses

Abstract

Large-scale comparative genomics- and population genetic studies generate enormous amounts of polymorphism data in the form of DNA variants. Ultimately, the goal of many of these studies is to associate genetic variants to phenotypes or fitness. We introduce VIVID, an interactive, user-friendly web application that integrates a wide range of approaches for encoding genotypic to phenotypic information in any organism or disease, from an individual or population, in three-dimensional (3D) space. It allows mutation mapping and annotation, calculation of interactions and conservation scores, prediction of harmful effects, analysis of diversity and selection, and 3D visualization of genotypic information encoded in Variant Call Format on AlphaFold2 protein models. VIVID enables the rapid assessment of genes of interest in the study of adaptive evolution and the genetic load, and it helps prioritizing targets for experimental validation. We demonstrate the utility of VIVID by exploring the evolutionary genetics of the parasitic protist Plasmodium falciparum, revealing geographic variation in the signature of balancing selection in potential targets of functional antibodies.

Keywords: data visualization; evolution; multi-dimensional analysis; population genetics; protein structure; variant interpretation.

Fig. 1.

Schematic workflow of VIVID. The back-end of the VIVID webserver consists of a series of tools for population genetics and mutational analysis. The information of allele frequency in the population and consensus sequence of each sample in the population are extracted from user provided VCF file by using Bcftools v1.8 and are further used by BioStructmap v.0.4.1 to map population genetic indices onto the 3D protein structure. The 3D structure of nonsynonymous mutations is modeled by MODELLER 10.1 and their effects on protein stability and interatomic interactions are evaluated by Dynamut2 and Arpeggio, respectively. All analysis outcomes (variant frequency, stabilizing/destabilizing effects, interatomic interactions, conservation score, diversity, selection, mutations, etc.) are visualized on 3D protein structure using NGLviewer. The front-end of the VIVID webserver was designed with Materialize v1.0.0 and the back-end was based on Python 2.7 via Flask Framework version v1.0.2 on a Linux server running Apache.

Fig. 2.

Seven panels showing the VIVID output on the results page. These panels show the results of integrative analyses of SNPs (data derived from the MalariaGen Pf3k version5 dataset) on the EBA175 RII protein.

Fig. 2.

Seven panels showing the VIVID output on the results page. These panels show the results of integrative analyses of SNPs (data derived from the MalariaGen Pf3k version5 dataset) on the EBA175 RII protein.

Fig. 3.

Analyses of EBA175 RII polymorphisms from two different populations—Thailand (Asia) and Guinea (Africa) using VIVID. A part of the F2 domain (circle) illustrates distinct balancing selection between Thailand and Guinea populations. Unless otherwise stated, only the results from the Thailand population are shown. (A) Schematic diagram of the dimerized EBA175 RII ligand binding to glycophorin A human receptor. (B) The highlighted residues (circle) indicated the target of known inhibitory antibodies from previous studies (Ambroggio et al. 2013; Chen et al. 2013). (C) Map of nonsynonymous and synonymous mutations on EBA175 RII. (D) Map of ΔΔG values on EBA175 RII. (E) Map of spatial Tajima’s D on EBA175 RII. (F) Map of PSSM scores on EBA175 RII. (G) Map of nucleotide diversity on EBA175 RII.