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 |
|
---|---|
Author | Elodie Darbo, Denis Seyres, Aitor Gonzalez |
Bioconductor views | ChIPSeq Classification MotifAnnotation Sequencing Software SupportVectorMachine |
Maintainer | Aitor Gonzalez <aitor.gonzalez@univ-amu.fr> |
License | MIT | file LICENSE |
Version | 1.24.0 |
Package repository | View on Bioconductor |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.