- recipe bioconductor-precisetadhub
Pre-trained random forest models obtained using preciseTAD
- Homepage:
https://bioconductor.org/packages/3.20/data/experiment/html/preciseTADhub.html
- License:
MIT + file LICENSE
- Recipe:
An experimentdata package to supplement the preciseTAD package containing pre-trained models and the variable importances of each genomic annotation used to build the model parsed into list objects and available in ExperimentHub. In total, preciseTADhub provides access to n=84 random forest classification models optimized to predict TAD/chromatin loop boundary regions and stored as .RDS files. The value, n, comes from the fact that we considered l=2 cell lines {GM12878, K562}, g=2 ground truth boundaries {Arrowhead, Peakachu}, and c=21 autosomal chromosomes {CHR1, CHR2, …, CHR22} (omitting CHR9). Furthermore, each object is itself a two-item list containing: (1) the model object, and (2) the variable importances for CTCF, RAD21, SMC3, and ZNF143 used to predict boundary regions. Each model is trained via a "holdout" strategy, in which data from chromosomes {CHR1, CHR2, …, CHRi-1, CHRi+1, …, CHR22} were used to build the model and the ith chromosome was reserved for testing. See https://doi.org/10.1101/2020.09.03.282186 for more detail on the model building strategy.
- package bioconductor-precisetadhub¶
- Versions:
1.18.0-0,1.14.0-0,1.10.0-0,1.8.0-0,1.6.0-0,1.2.0-1,1.2.0-0,1.0.0-0- Depends:
on bioconductor-data-packages
>=20260207on bioconductor-experimenthub
>=3.0.0,<3.1.0on curl
on r-base
>=4.5,<4.6.0a0
- 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 bioconductor-precisetadhub
to add into an existing workspace instead, run:
pixi add bioconductor-precisetadhub
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 bioconductor-precisetadhub
Alternatively, to install into a new environment, run:
conda create -n envname bioconductor-precisetadhub
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/bioconductor-precisetadhub:<tag>
(see bioconductor-precisetadhub/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/bioconductor-precisetadhub/README.html)
