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.