  Modern genomics experiments measure functional behaviors for many thousands
of DNA sequences. We suggest that, especially when these sequences are chosen
at random, it is natural to compute correlation functions between sequences and
measured behaviors. In simple models for the dependence of DNA flexibility on
sequence, for example, correlation functions can be interpreted directly as
interaction parameters. Analysis of recent experiments shows that this is
surprisingly effective, leading directly to extraction of distinct features for
DNA flexibility and predictions that are as accurate as more complex models.
This approach follows the conventional use of correlation functions in
statistical physics and connects the search for relevant DNA sequence features
to the search for relevant stimulus features in the analysis of sensory
neurons.
