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 details

AuthorElodie Darbo, Denis Seyres, Aitor Gonzalez
Bioconductor views ChIPSeq Classification MotifAnnotation Sequencing Software SupportVectorMachine
MaintainerAitor Gonzalez <aitor.gonzalez@univ-amu.fr>
LicenseMIT | file LICENSE
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))


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LedPred documentation built on Nov. 8, 2020, 8 p.m.