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. 2019 Sep 2;9:12648. doi: 10.1038/s41598-019-49114-z

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Workflow of VIVA. INPUT: VCF file is a required file. Users can use one or any combination of variant filters, sample selection, and grouping options. DATA PROCESSING: Data processing requires the Julia programing language and depends on several well-maintained Julia packages. Plotting uses the PlotlyJS.jl wrapper for Plotly. VIVA has two interface choices. Users may use the program through a Jupyter Notebook or from the command line. OUTPUT: VIVA’s four visualization options include heatmaps of genotype and read depth data as well as scatter plots of average sample read depth and average variant read depth data. These visualizations can be saved in HTML, PDF, SVG, or EPS formats. HTML format enables users to share and analyze the data interactively between research groups which supports collaborative work environments.