From 4b3a959b4ca2f03b0d85161f1ba90c20391db4b7 Mon Sep 17 00:00:00 2001 From: Arnold Kuzniar Date: Tue, 13 Feb 2024 11:16:37 +0100 Subject: [PATCH] Update codacy badges --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 86e625359..b910ff1e1 100644 --- a/README.md +++ b/README.md @@ -3,8 +3,8 @@ [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1217111.svg)](https://doi.org/10.5281/zenodo.1217111) [![Published in PeerJ](https://img.shields.io/badge/published%20in-PeerJ-blue.svg)](https://doi.org/10.7717/peerj.8214) [![CI](https://github.com/GooglingTheCancerGenome/sv-callers/actions/workflows/ci.yaml/badge.svg?branch=master)](https://github.com/GooglingTheCancerGenome/sv-callers/actions/workflows/ci.yaml) -[![Codacy Badge](https://app.codacy.com/project/badge/Grade/eaa33d7d090048898c112a4a87815479)](https://www.codacy.com/gh/GooglingTheCancerGenome/sv-callers/dashboard?utm_source=github.com&utm_medium=referral&utm_content=GooglingTheCancerGenome/sv-callers&utm_campaign=Badge_Grade) -[![Codacy Badge](https://app.codacy.com/project/badge/Coverage/eaa33d7d090048898c112a4a87815479)](https://www.codacy.com/gh/GooglingTheCancerGenome/sv-callers/dashboard?utm_source=github.com&utm_medium=referral&utm_content=GooglingTheCancerGenome/sv-callers&utm_campaign=Badge_Coverage) +[![Codacy Badge](https://app.codacy.com/project/badge/Grade/eaa33d7d090048898c112a4a87815479)](https://app.codacy.com/gh/GooglingTheCancerGenome/sv-callers/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) +[![Codacy Badge](https://app.codacy.com/project/badge/Coverage/eaa33d7d090048898c112a4a87815479)](https://app.codacy.com/gh/GooglingTheCancerGenome/sv-callers/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_coverage) Structural variants (SVs) are an important class of genetic variation implicated in a wide array of genetic diseases. _sv-callers_ is a _Snakemake_-based workflow that combines several state-of-the-art tools for detecting SVs in whole genome sequencing (WGS) data. The workflow is easy to use and deploy on any Linux-based machine. In particular, the workflow supports automated software deployment, easy configuration and addition of new analysis tools as well as enables to scale from a single computer to different HPC clusters with minimal effort.