  Background: In addition to known protein-coding genes, large amount of
apparently non-coding sequence are conserved between the human and mouse
genomes. It seems reasonable to assume that these conserved regions are more
likely to contain functional elements than less-conserved portions of the
genome. Here we used a motif-oriented machine learning method to extract the
strongest signal from a set of non-coding conserved sequences.
  Results: We successfully fitted models to reflect the non-coding sequences,
and showed that the results were quite consistent for repeated training runs.
Using the learned model to scan genomic sequence, we found that it often made
predictions close to the start of annotated genes. We compared this method with
other published promoter-prediction systems, and show that the set of promoters
which are detected by this method seems to be substantially similar to that
detected by existing methods.
  Conclusions: The results presented here indicate that the promoter signal is
the strongest single motif-based signal in the non-coding functional fraction
of the genome. They also lend support to the belief that there exists a
substantial subset of promoter regions which share common features and are
detectable by a variety of computational methods.
