# vvv2_align_SE, vvv2_align_PE/vvv2_display: Galaxy-Based Workflows and Tool Designed to Perform, Summarize and Visualize Variant Calling and Annotation in Viral Genome Assemblies

## TL;DR

This paper introduces vvv2_display, a tool that simplifies the analysis of viral genome data by visually summarizing and linking key variant information.

## Contribution

The novel contribution is vvv2_display, a Galaxy-based tool that integrates variant visualization and annotation for viral genomes.

## Key findings

- vvv2_display generates a PNG image showing alignment coverage and significant variants along the genome.
- The tool produces a TSV file listing high-confidence variants with detailed annotations and identifiers.

## Abstract

Background: Next-generation sequencing (NGS) analysis of viral samples generates results dispersed across multiple files—genome assembly, variant calling, and functional annotations—making integrated interpretation challenging. Variants often yield numerous low-frequency or non-significant variants, yet only a small fraction are biologically relevant. Virologists must manually sift through extensive data to identify meaningful mutations, a time-consuming and error-prone process. To address these practical challenges, we developed vvv2_display, a dedicated summarization and visualization tool, integrated within comprehensive Galaxy workflows. Results: vvv2_display streamlines variant interpretation by consolidating key results into two concise and interoperable outputs. The first output is a PNG image showing alignment coverage depth and genomic annotations, with significant variants displayed along the genome as symbols whose height reflects frequency and shape indicates the affected protein. At a glance, this enables virologists to identify all deviations from a reference viral genome. Each significant variant is assigned a unique identifier that directly links to the second output: a tab-separated (TSV) text file listing only high-confidence variants, with frequencies, flanking nucleotides, and impacted genes and proteins. This cross-referenced design supports rapid, accurate, and intuitive data exploration. Availability: vvv2_display is open source, available on Github and installable via Mamba.