title: Apples to apples: improving the performance of motif finders and their significance analysis in the Twilight Zone.
abstract: BACKGROUND
  Effective algorithms for finding relatively weak motifs are an important practical necessity while scanning long DNA sequences for regulatory elements. The success of such an algorithm hinges on the ability of its scoring function combined with a significance analysis test to discern real motifs from random noise.
  
  
  RESULTS
  In the first half of the paper we show that the paradigm of relying on entropy scores and their E-values can lead to undesirable results when searching for weak motifs and we offer alternate approaches to analyzing the significance of motifs. In the second half of the paper we reintroduce a scoring function and present a motif-finder that optimizes it that are more effective in finding relatively weak motifs than other tools.
  
  
  BACKGROUND
  The GibbsILR motif finder is available at http://www.cs.cornell.edu/~keich.
authors.affiliation: Department of Computer Science, Cornell University, Ithaca, NY, USA.
authors.forename: Patrick
authors.initials: P
authors.lastname: Ng
authors.forename: Niranjan
authors.initials: N
authors.lastname: Nagarajan
authors.forename: Neil
authors.initials: N
authors.lastname: Jones
authors.forename: Uri
authors.initials: U
authors.lastname: Keich
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D004247:DNA / Q000235:genetics*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D050436:Regulatory Elements, Transcriptional / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
chemical_list: D004247:DNA