recipe bioconductor-precisetadhub

Pre-trained random forest models obtained using preciseTAD

Homepage:

https://bioconductor.org/packages/3.18/data/experiment/html/preciseTADhub.html

License:

MIT + file LICENSE

Recipe:

/bioconductor-precisetadhub/meta.yaml

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:

1.10.0-0,  1.8.0-0,  1.6.0-0,  1.2.0-1,  1.2.0-0,  1.0.0-0

depends bioconductor-data-packages:

>=20231203

depends bioconductor-experimenthub:

>=2.10.0,<2.11.0

depends curl:

depends r-base:

>=4.3,<4.4.0a0

requirements:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-precisetadhub

and update with::

   mamba update bioconductor-precisetadhub

To create a new environment, run:

mamba create --name myenvname bioconductor-precisetadhub

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/bioconductor-precisetadhub:<tag>

(see `bioconductor-precisetadhub/tags`_ for valid values for ``<tag>``)

Download stats

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-precisetadhub/README.html)