Enhancing motif finding models using multiple sources of genome-wide data

The SUCcESS package implements the CTCM model (particularly logistic regression model) proposed by Shim and Keleş (2008) for integrating quantitative information into motif finding as well as its extension to use multiple data sources at a time (e.g., ChIP-seq, nucleosome occupancy, or conservation score). We implemented them as an extended module for cosmo, developed by Bembom et al. (2007), implementing an algorithm which supervises detection of motifs using a set of constraints for a position weight matrix. Note that although this package provides all functions implemented in cosmo, we ask that you instead use the latest version of cosmo for the supervised motif detection algorithm. This package is for running SUCcESS option only. This vignette just provides the instructions for how to run SUCcESS: you will need to consult Shim and Keleş (2008) for methodological details and the cosmo vignette (you can find it in the document directory of this package) for detailed options.