IMPACT-VIS

Integrated Mapping of Phenotype-Associated Candidate Targets (IMPACT) For Interactive Interpretation and Prioritization of Genomic Variants

Authors
Affiliation

Nicholas Boehler

University of Toronto Mississauga

Hai-Ying Mary Cheng

University of Toronto Mississauga

Published

December 18, 2025

1 Welcome

IMPACT-VIS is an R Shiny application for interactive visualization and annotation of genomic variants produced by the IMPACT preprocessing workflows. Built on the modern Rhino framework, IMPACT-VIS provides researchers with an integrated platform for exploring structural variants (SVs), copy number variants (CNVs), and small nucleotide variants (SNVs) across multiple samples.

Note

This documentation is designed for genomics researchers and computational biologists seeking to analyze IMPACT workflow outputs. For developers, see the Architecture & Design and API Reference sections.

2 Key Features

  • Integrated Multi-Variant Analysis: Simultaneous exploration of SNVs/Indels, SVs, and CNVs
  • Interactive Visualizations: Publication-ready plots with Plotly interactivity
  • On-Disk Processing: Memory-efficient filtering of large datasets via SeqArray
  • Persistent Annotations: Per-sample annotation states saved across sessions
  • Reproducible Workflows: Full dependency versioning via renv, Docker reproducibility

3 Quick Navigation

3.1 For First-Time Users

Start with the Quick Start Guide to install IMPACT-VIS and run your first analysis.

3.2 For Data Preparation

Review Data Preparation to learn how to format and validate your IMPACT workflow outputs.

3.3 For Interface Guidance

Explore the Interface Overview for a comprehensive tour of the user interface and controls.

4 Background

IMPACT (Integrated Mapping of Phenotype-Associated Candidate Targets) is an open-source, modular framework for phenotype-configurable interpretation of variant data derived from whole-genome sequencing (WGS) experiments across all major variant classes. IMPACT-VIS was developed as an interactive visualization module to enable intuitive exploration of IMPACT-preprocessed variants. Key motivations include:

  1. Unified Interface: Single application for exploring SNVs/Indels, SVs, and CNVs alongside phenotype-relevant prioritization
  2. Reproducibility: Versioned dependencies and containerization support for reproducible analyses
  3. High-Quality Dynamic Plotting: Publication-ready interactive visualizations for exploratory genomic analysis
  4. Phenotype-Aware Prioritization: Variant filtering and ranking reflect disease relevance via Open Targets gene associations

5 About This Documentation

This site serves as the primary reference for IMPACT-VIS users and developers. It is structured in four main sections:

5.1 📖 User Guide

Practical tutorials and walkthroughs for researchers using IMPACT-VIS.

5.2 🔬 Methods & Architecture

Technical documentation of algorithms, system design, performance characteristics, and reproducibility protocols.

5.3 🔧 API Reference

Auto-generated documentation extracted from roxygen2 comments within the IMPACT-VIS Box modules. This reference covers all public functions and internal structures within the codebase.

5.4 📋 Input Specifications

Detailed schemas and validation rules for input file formats (FAVOR-annotated GDS, AnnotSV-annotated TSV, and SCIP-annotated TXT).

6 Getting Started: Three Paths

6.2 Path 2: Prepare Your Own Data

  1. Use the upstream IMPACT preprocessing modules (IMPACT-SNV, IMPACT-SV, IMPACT-CNV) to generate formatted variant files
  2. Read Data Preparation to understand file requirements and validation procedures
  3. Launch IMPACT-VIS and load your samples
  4. Follow the Interface Overview to explore results

6.3 Path 3: Deep Dive into Methods

Jump directly to the Methods section to understand the computational approaches, algorithms, and validation protocols.

7 Citation

If you use IMPACT-VIS in your research, please cite:

@article{impact-TBA,
  title={Integrated Mapping of Phenotype-Associated Candidate Targets for interpretation and prioritization of genomic variants},
  authors={Boehler, N. and Cheng, H. Y. M.},
  journal={TBA},
  year={TBA},
  volume={XX},
  pages={XX}
}

8 System Requirements

  • R ≥ 4.0
  • Operating System: Linux, macOS, or Windows (with WSL)
  • Memory: 4 GB minimum (8+ GB recommended for large datasets)
  • Browser: Modern browser with JavaScript enabled (Chrome, Firefox, Safari, Edge)

9 License

IMPACT-VIS is released under the MIT License. See the LICENSE file for details.

10 Contributing

We welcome contributions, bug reports, and feature requests. Please see CONTRIBUTING.md for guidelines on:

  • Setting up a development environment
  • Code style and conventions
  • Pull request process
  • Running tests

11 Support & Issues

  • GitHub Issues: Report bugs or request features on the issue tracker
  • Documentation Issues: Found a typo or unclear explanation? Open an issue

12 Acknowledgments

We gratefully acknowledge the developers and communities who made IMPACT-VIS possible:

Framework & Architecture:

  • Rhino and Rhinoverse (Appsilon) for providing an opinionated, professional framework for building production-grade Shiny applications
  • box (Klipstein & team) for enabling modular R package management

Data Processing & Annotation:

  • FAVOR/FAVORannotator team for functional genome annotation infrastructure
  • AnnotSV developers for structural variant annotation tools
  • SCIP developers for copy number variant analysis

Visualization & UI:

  • shiny, ggplot2, plotly, and SeqArray maintainers for core visualization and data access
  • shiny.fluent team (Microsoft/Appsilon) for Fluent UI components in R

Last Updated: 2025-12-10
Version: 1.0.0
Repository: github.com/boehlernick/IMPACT-VIS