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.

Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("LedPred")
AuthorElodie Darbo, Denis Seyres, Aitor Gonzalez
Bioconductor views ChIPSeq Classification MotifAnnotation Sequencing Software SupportVectorMachine
Date of publicationNone
MaintainerAitor Gonzalez <aitor.gonzalez@univ-amu.fr>
LicenseMIT | file LICENSE
Version1.8.0

View on Bioconductor

Files

DESCRIPTION
LICENSE
NAMESPACE
NEWS
R
R/Data.R R/FeatureNbTuner.R R/FeatureRanking.R R/LedPredClass.R R/Model.R R/ModelPerformance.R R/ParameterTuner.R R/createModel.R R/evaluateModelPerformance.R R/ledpred.R R/mapFeaturesToCRMs.R R/mcTune.R R/rankFeatures.R R/scoreData.R R/tuneFeatureNb.R
build
build/vignette.rds
data
data/crm.features.rda
data/feature.ranking.rda
inst
inst/CITATION
inst/doc
inst/doc/LedPred.R
inst/doc/LedPred.Rnw
inst/doc/LedPred.pdf
inst/extdata
inst/extdata/259_matrices_lightNames.tf
inst/extdata/2nt_intergenic_droso.freq
inst/extdata/negative_50CADenhancers.bed
inst/extdata/ngs
inst/extdata/ngs/K4me1_E_F_6_8h_H3_subtracted_reduced.wig
inst/extdata/ngs/docBoundCRM.bed
inst/extdata/ngs/dtcfBoundCRM.bed
inst/extdata/ngs/pmadBoundCRM.bed
inst/extdata/ngs/pnrBoundCRM.bed
inst/extdata/ngs/tinBoundCRM.bed
inst/extdata/positive_50CADenhancers.bed
inst/extdata/prediction_small.bed
inst/extdata/testdata
inst/extdata/testdata/230NonActivesEnhancers4_8h_2seqs.bed
inst/extdata/testdata/23From66ActiveEnhancersInMesoAt68h_2seqs.bed
inst/extdata/testdata/feature_nb.txt
inst/extdata/testdata/feature_rank.txt
inst/extdata/testdata/test_ngs_bed_small
inst/extdata/testdata/test_ngs_bed_small/mfile2.tf
inst/extdata/testdata/test_ngs_bed_small/negative_5seqs.bed
inst/extdata/testdata/test_ngs_bed_small/ngs_beds
inst/extdata/testdata/test_ngs_bed_small/ngs_beds/ngs.bed
inst/extdata/testdata/test_ngs_bed_small/ngs_beds/ngs2.bed
inst/extdata/testdata/test_ngs_bed_small/outdir_bak
inst/extdata/testdata/test_ngs_bed_small/outdir_bak/feature_matrix.tab
inst/extdata/testdata/test_ngs_bed_small/positive_2seqs.bed
man
man/LedPred.Rd man/createModel.Rd man/crm.features.Rd man/evaluateModelPerformance.Rd man/feature.ranking.Rd man/mapFeaturesToCRMs.Rd man/mcTune.Rd man/rankFeatures.Rd man/scoreData.Rd man/tuneFeatureNb.Rd
tests
tests/testthat
tests/testthat.R
tests/testthat/_ROC_perf.png
tests/testthat/_kappa_measures.png
tests/testthat/data_iris2
tests/testthat/data_iris2/feature.ranking.rda
tests/testthat/data_iris2/iris2.rda
tests/testthat/data_iris2/x.rda
tests/testthat/data_iris2/y.rda
tests/testthat/test-iris2_Data.R tests/testthat/test-iris2_FeatureNbTuner.R tests/testthat/test-iris2_FeatureRanking.R tests/testthat/test-iris2_LedPred.R tests/testthat/test-iris2_ModelCreate.R tests/testthat/test-iris2_ModelPerformance.R tests/testthat/test-iris2_ParameterTuner.R
vignettes
vignettes/LedPred.Rnw
vignettes/_ROCR_perf.png
vignettes/_c_g_eval.png
vignettes/_kappa_measures.png
vignettes/figs
vignettes/figs/vignette_ROC_perf.png
vignettes/figs/vignette_c_g_eval.png
vignettes/figs/vignette_kappa_measures.png

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.