GENOVIS: a Python package for the visualization of population genetic analyses

Background: Despite its importance, generating clear and customisable figures remains challenging for researchers without a bioinformatic background. This is due to the fact that most population genetics tools target specific analyses and rely on language-specific scripting (e.g., R or Python), producing large amounts of non-interactive outputs. Moreover, many visualization tools are challenging to integrate into existing analytical pipelines or cross-platform environments, adding time-consuming and technically demanding steps. Therefore, there is a growing demand for powerful, user-friendly, and flexible visualization tools that enable researchers with varying levels of bioinformatic expertise to investigate and communicate a wide range of population genetic questions, using both simulated and empirical datasets. Results: To address this need, we developed GENOVIS, a Python package available both as a command-line and graphical interfaces (CLI and GUI) that streamlines and simplifies the generation of six key population genomics visualizations: single-nucleotide polymorphism (SNP) density heatmaps (mapden), runs of homozygosity (ROH) plots (rohpainter), genomic relationship matrix heatmaps (relmap), 3D principal component analysis (PCA) (pca3d), admixture barplots (admix), and Manhattan plots (manplot). As such, GENOVIS provides a unified and user-friendly interface to produce publication-ready figures with minimal coding and high flexibility. Conclusion: With the development of the visualization software GENOVIS, we provide a streamlined solution for generating high-quality, publication-ready graphics with customizable features through both CLI and GUI interfaces. Built on a flexible Python framework, GENOVIS enables efficient generation of runs of homozygosity plots, 3D PCA, admixture barplots, SNP density heatmaps, Manhattan plots, and genomic relationship heatmaps, making advanced population genomics analyses more accessible and reproducible.

Universität Wien