  We have compared a recently developed module-based algorithm LeMoNe for
reverse-engineering transcriptional regulatory networks to a mutual information
based direct algorithm CLR, using benchmark expression data and databases of
known transcriptional regulatory interactions for Escherichia coli and
Saccharomyces cerevisiae. A global comparison using recall versus precision
curves hides the topologically distinct nature of the inferred networks and is
not informative about the specific subtasks for which each method is most
suited. Analysis of the degree distributions and a regulator specific
comparison show that CLR is 'regulator-centric', making true predictions for a
higher number of regulators, while LeMoNe is 'target-centric', recovering a
higher number of known targets for fewer regulators, with limited overlap in
the predicted interactions between both methods. Detailed biological examples
in E. coli and S. cerevisiae are used to illustrate these differences and to
prove that each method is able to infer parts of the network where the other
fails. Biological validation of the inferred networks cautions against
over-interpreting recall and precision values computed using incomplete
reference networks.
