  The topological analysis of biological networks has been a prolific topic in
network science during the last decade. A persistent problem with this approach
is the inherent uncertainty and noisy nature of the data. One of the cases in
which this situation is more marked is that of transcriptional regulatory
networks (TRNs) in bacteria. The datasets are incomplete because regulatory
pathways associated to a relevant fraction of bacterial genes remain unknown.
Furthermore, direction, strengths and signs of the links are sometimes unknown
or simply overlooked. Finally, the experimental approaches to infer the
regulations are highly heterogeneous, in a way that induces the appearance of
systematic experimental-topological correlations. And yet, the quality of the
available data increases constantly. In this work we capitalize on these
advances to point out the influence of data (in)completeness and quality on
some classical results on topological analysis of TRNs, specially regarding
modularity at different levels. In doing so, we identify the most relevant
factors affecting the validity of previous findings, highlighting important
caveats to future prokaryotic TRNs topological analysis.
