recipe bioconductor-precisetad

preciseTAD: A machine learning framework for precise TAD boundary prediction

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/preciseTAD.html

License:

MIT + file LICENSE

Recipe:

/bioconductor-precisetad/meta.yaml

preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution. As an input, it takes BED-formatted genomic coordinates of domain boundaries detected from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations from ENCODE or other consortia. preciseTAD employs several feature engineering strategies and resampling techniques to address class imbalance, and trains an optimized random forest model for predicting low-resolution domain boundaries. Translated on a base-level, preciseTAD predicts the probability for each base to be a boundary. Density-based clustering and scalable partitioning techniques are used to detect precise boundary regions and summit points. Compared with low-resolution boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines. The pre-trained model can accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and ZNF143 annotation data for this cell line.

package bioconductor-precisetad

versions:

1.12.0-0,  1.10.0-0,  1.8.0-0,  1.4.0-0,  1.2.0-0,  1.0.0-2,  1.0.0-1

depends bioconductor-genomicranges:

>=1.54.0,<1.55.0

depends bioconductor-iranges:

>=2.36.0,<2.37.0

depends bioconductor-rcgh:

>=1.32.0,<1.33.0

depends bioconductor-s4vectors:

>=0.40.0,<0.41.0

depends r-base:

>=4.3,<4.4.0a0

depends r-caret:

depends r-cluster:

depends r-dbscan:

depends r-dosnow:

depends r-e1071:

depends r-foreach:

depends r-gtools:

depends r-modelmetrics:

depends r-pbapply:

depends r-proc:

depends r-prroc:

depends r-randomforest:

requirements:

additional platforms:

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-precisetad

and update with::

   mamba update bioconductor-precisetad

To create a new environment, run:

mamba create --name myenvname bioconductor-precisetad

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-precisetad:<tag>

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

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