Computational methods attempting to identify instances of cis-regulatory modules 
(CRMs) in the genome face a challenging problem of searching for potentially 
interacting transcription factor binding sites while knowledge of the specific 
interactions involved remains limited. Without a comprehensive comparison of 
their performance, the reliability and accuracy of these tools remains unclear. 
Faced with a large number of different tools that address this problem, we 
summarized and categorized them based on search strategy and input data 
requirements. Twelve representative methods were chosen and applied to predict 
CRMs from the Drosophila CRM database REDfly, and across the human ENCODE 
regions. Our results show that the optimal choice of method varies depending on 
species and composition of the sequences in question. When discriminating CRMs 
from non-coding regions, those methods considering evolutionary conservation 
have a stronger predictive power than methods designed to be run on a single 
genome. Different CRM representations and search strategies rely on different 
CRM properties, and different methods can complement one another. For example, 
some favour homotypical clusters of binding sites, while others perform best on 
short CRMs. Furthermore, most methods appear to be sensitive to the composition 
and structure of the genome to which they are applied. We analyze the principal 
features that distinguish the methods that performed well, identify weaknesses 
leading to poor performance, and provide a guide for users. We also propose key 
considerations for the development and evaluation of future CRM-prediction 
methods.