Enhancers are stretches of DNA (100-1000 bp) that play a major role in development gene expression, evolution and disease. It has been recently shown that in high-level eukaryotes enhancers rarely work alone, instead they collaborate by forming clusters of cis-regulatory modules (CRMs). Even if the binding of transcription factors is sequence-specific, the identification of functionally similar enhancers is very difficult and it cannot be carried out with traditional alignment-based techniques. In this paper we study the use of alignment-free measures for the classification of CRMs. However alignment-free measures are generally tied to a fixed resolution k. Here we propose an alignment-free statistic that is based on multiple resolution patterns derived from Entropic Profiles. Entropic Profile is a function of the genomic location that captures the importance of that region with respect to the whole genome. We evaluate several alignment-free statistics on simulated data and real mouse ChIP-seq sequences. The new statistic is highly successful in discriminating functionally related enhancers and, in almost all experiments, it outperforms fixed-resolution methods.
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