  Computational approaches to transcription factor binding site identification
have been actively researched for the past decade.
  Negative examples have long been utilized in de novo motif discovery and have
been shown useful in transcription factor binding site search as well.
  However, understanding of the roles of negative examples in binding site
search is still very limited.
  We propose the 2-centroid and optimal discriminating vector methods, taking
into account negative examples. Cross-validation results on E. coli
transcription factors show that the proposed methods benefit from negative
examples, outperforming the centroid and position-specific scoring matrix
methods. We further show that our proposed methods perform better than a
state-of-the-art method. We characterize the proposed methods in the context of
the other compared methods and show that, coupled with motif subtype
identification, the proposed methods can be effectively applied to a wide range
of transcription factors. Finally, we argue that the proposed methods are
well-suited for eukaryotic transcription factors as well.
  Software tools are available at: http://biogrid.engr.uconn.edu/tfbs_search/.
