From d5ae2de15dce08defd89ed998c8c77a4a7c76db9 Mon Sep 17 00:00:00 2001 From: George Tollefson Date: Thu, 28 Mar 2019 15:49:17 -0400 Subject: [PATCH] docs(readme): removed typo line --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 4a35492..77aa993 100644 --- a/README.md +++ b/README.md @@ -13,8 +13,6 @@ VariantVisualization.jl is a package we built specifically to power the genetics *VIVA* is a user-friendly command line tool for creating publication quality graphics from Variant Call Format (VCF) files and has been designed for clinicians and bioinformaticians to explore their VCF files visually. Users can quickly extract genotype or read depth information and plot trends in interactive categorical heatmaps and scatter plots of average read depth values. VIVA offers a robust set of filters to select variants and samples of interest for analysis. VIVA is especially useful in early data exploration for identifying batch effect and sources of poor read depth, as well as identifying distribution of disease causing variants in a set of clinical samples. -To contribute to *VIVA*, developers may use the functions contained - ## Getting Started: *Installation* @@ -29,7 +27,7 @@ Windows 10, Windows 7 ### Command Line Tool 1. Add VariantVisualization.jl in the Julia Pkg prompt. -2. Download the [VIVA](https://github.com/compbiocore/VariantVisualization.jl/tree/master/VIVA) tool script and save it to a working directory for your analysis. +2. Download the [VIVA](https://github.com/compbiocore/VariantVisualization.jl/blob/master/viva) tool script and save it to a working directory for your analysis. 3. Navigate to your working directory and follow the [VIVA manual](https://compbiocore.github.io/VariantVisualization.jl/latest/) to generate your plots. ### Jupyter Notebook