DESCRIPTION

Package: LedPred
Title: Learning from DNA to Predict Enhancers
Description: 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.
Version: 1.7.7
Date: 2016-09-24
Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez
Maintainer: Aitor Gonzalez <aitor.gonzalez@univ-amu.fr>
Depends:
    R (>= 3.2.0),
    e1071 (>= 1.6)
Imports:
    akima,
    ggplot2,
    irr,
    jsonlite,
    parallel,
    plot3D,
    plyr,
    RCurl,
    ROCR,
    testthat
License: MIT | file LICENSE
LazyData: true
Packaged: 2016-08-09 13:56:55 UTC; aitor
biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq,
    Sequencing, Classification
NeedsCompilation: no
BugReports: https://github.com/aitgon/LedPred/issues
RoxygenNote: 6.1.0