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GENOVA

GENome Organisation Visual Analytics

Install / Use

/learn @robinweide/GENOVA
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

<!-- README.md is generated from README.Rmd. Please edit that file -->

GENOVA <img src="vignettes/logo_GENOVA.png" align="right" alt="" width="200" />

GitHub Build
Status Project Status: Active – The project has reached a stable, usable
state and is being actively
developed. minimal R
version GitHub tag (latest by
date)

Explore the Hi-Cs!

The increase in interest for Hi-C methods in the chromatin community has led to a need for more user-friendly and powerful analysis methods. The few currently available software packages for Hi-C do not allow a researcher to quickly summarize and visualize their data. An easy to use software package, which can generate a comprehensive set of publication-quality plots, would allow researchers to swiftly go from raw Hi-C data to interpretable results.

Here, we present GENome Organisation Visual Analytics (GENOVA): a software suite to perform in-depth analyses on various levels of genome organisation, using Hi-C data. GENOVA facilitates the comparison between multiple datasets and supports the majority of mapping-pipelines.

GENOVA directly reads data from:

  • HiC-pro
  • cooler
  • juicer

Installation

You can install GENOVA from GitHub with:

# install.packages("remotes")
remotes::install_github("robinweide/GENOVA")

Note to long-time users

Version 1.0 will contain a massive overhaul, which will result in breaking nearly every analysis. To provide legacy support, we made the ye olde lighthouse release. This can be installed with devtools::install_github("robinweide/GENOVA@v0.95"). Furthermore, if you have custom scripts based on the output of construct.experiment(), you can use v1 and set legacy=TRUE in loadContacts() to get a similar output. This, of course, also allows you to load .cooler and .hic files in pre-v1 versions :+1:.

Support

We have provided a quite lengthy vignette, so please read that first. If there are still unanswered questions, please use the issue-tracker.

Publication

Please see our preprint on bioRxiv: Hi-C Analysis with GENOVA: a case study with cohesin variants.

Code of conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.


View on GitHub
GitHub Stars87
CategoryData
Updated2d ago
Forks16

Languages

R

Security Score

100/100

Audited on Apr 4, 2026

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