LedPred
Learning from DNA to Predict Enhancers
Bioconductor version: 3.23 · Package version: 1.46.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("LedPred")Details
| Maintainer | Aitor Gonzalez <aitor.gonzalez@univ-amu.fr> |
| Author | Elodie Darbo, Denis Seyres, Aitor Gonzalez |
| License | MIT | file LICENSE |
| Bug Reports | https://github.com/aitgon/LedPred/issues |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | ChIPSeq, Classification, MotifAnnotation, Sequencing, Software, SupportVectorMachine |
| Package Short Url | https://bioconductor.org/packages/LedPred/ |
Citation
From within R, enter citation("LedPred"):
Elodie Darbo, Denis Seyres, Aitor Gonzalez. LedPred: Learning from DNA to Predict Enhancers. doi:10.18129/B9.bioc.LedPred, R package version 1.46.0, https://bioconductor.org/packages/LedPred.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | LedPred_1.46.0.tar.gz |
| Windows binary (x86_64) | LedPred_1.46.0.zip |
| macOS binary (arm64) | LedPred_1.46.0.tgz |
| macOS binary (x86_64) | LedPred_1.46.0.tgz |