recipe bioconductor-ledpred

Learning from DNA to Predict Enhancers






This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences.

package bioconductor-ledpred

(downloads) docker_bioconductor-ledpred



depends r-akima:

depends r-base:


depends r-e1071:


depends r-ggplot2:

depends r-irr:

depends r-jsonlite:

depends r-plot3d:

depends r-plyr:

depends r-rcurl:

depends r-rocr:

depends r-testthat:



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-ledpred

and update with::

   mamba update bioconductor-ledpred

To create a new environment, run:

mamba create --name myenvname bioconductor-ledpred

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<tag>

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

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