Supervised classification based on support vector machines (SVMs) has 
successfully been used for the prediction of cis-regulatory modules (CRMs). 
However, no integrated tool using such heterogeneous data as position-specific 
scoring matrices, ChIP-seq data or conservation scores is currently available. 
Here, we present LedPred, a flexible SVM workflow that predicts new regulatory 
sequences based on the annotation of known CRMs, which are associated to a large 
variety of feature types. LedPred is provided as an R/Bioconductor package 
connected to an online server to avoid installation of non-R software. Due to 
the heterogeneous CRM feature integration, LedPred excels at the prediction of 
regulatory sequences in Drosophila and mouse datasets compared with similar 
SVM-based software.
AVAILABILITY AND IMPLEMENTATION: LedPred is available on GitHub: 
https://github.com/aitgon/LedPred and Bioconductor: 
http://bioconductor.org/packages/release/bioc/html/LedPred.html under the MIT 
license.
CONTACT: aitor.gonzalez@univ-amu.fr
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.