- recipe r-xgr
The central goal of XGR by Fang et al. (2016) <doi:10.1186/s13073-016-0384-y> is to provide a data interpretation system necessary to do "big data" science. It is designed to make a user-defined gene or SNP list (or genomic regions) more interpretable by comprehensively utilising ontology annotations and interaction networks to reveal relationships and enhance opportunities for biological discovery. XGR is unique in supporting a broad range of ontologies (including knowledge of biological and molecular functions, pathways, diseases and phenotypes - in both human and mouse) and different types of networks (including functional, physical and pathway interactions). There are two core functionalities of XGR. The first is to provide basic infrastructures for easy access to built-in ontologies and networks. The second is to support data interpretations via 1) enrichment analysis using either built-in or custom ontologies, 2) similarity analysis for calculating semantic similarity between genes (or SNPs) based on their ontology annotation profiles, 3) network analysis for identification of gene networks given a query list of (significant) genes, SNPs or genomic regions, and 4) annotation analysis for interpreting genomic regions using co-localised functional genomic annotations (such as open chromatin, epigenetic marks, TF binding sites and genomic segments) and using nearby gene annotations (by ontologies). Together with its web app, XGR aims to provide a user-friendly tool for exploring genomic relations at the gene, SNP and genomic region level.
- Homepage:
http://XGR.r-forge.r-project.org, http://galahad.well.ox.ac.uk/XGR
- License:
GPL2 / GPL-2
- Recipe:
- package r-xgr¶
- Versions:
1.1.7-5,1.1.7-4,1.1.7-3,1.1.7-2,1.1.7-1,1.1.7-0,1.1.6-1,1.1.6-0,1.1.5-0,1.1.7-5,1.1.7-4,1.1.7-3,1.1.7-2,1.1.7-1,1.1.7-0,1.1.6-1,1.1.6-0,1.1.5-0,1.1.4-0- Depends:
on bioconductor-biocgenerics
on bioconductor-genomicranges
on bioconductor-iranges
on bioconductor-s4vectors
on bioconductor-suprahex
on r-base
>=4.3,<4.4.0a0on r-dnet
on r-dplyr
on r-ggnetwork
on r-ggplot2
on r-ggrepel
on r-igraph
on r-matrix
on r-rcircos
on r-tidyr
- Additional platforms:
Installation¶
You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).
Pixi¶
With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:
pixi global install r-xgr
to add into an existing workspace instead, run:
pixi add r-xgr
In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:
pixi workspace channel add conda-forge
pixi workspace channel add bioconda
Conda¶
With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:
conda install r-xgr
Alternatively, to install into a new environment, run:
conda create -n envname r-xgr
with envname being the name of the desired environment.
Container¶
Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:
docker pull quay.io/biocontainers/r-xgr:<tag>
(see r-xgr/tags for valid values for <tag>).
Integrated deployment¶
Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/r-xgr/README.html)
