title: MTAP: the motif tool assessment platform.
abstract: BACKGROUND
  In recent years, substantial effort has been applied to de novo regulatory motif discovery. At this time, more than 150 software tools exist to detect regulatory binding sites given a set of genomic sequences. As the number of software packages increases, it becomes more important to identify the tools with the best performance characteristics for specific problem domains. Identifying the correct tool is difficult because of the great variability in motif detection software. Consequently, many labs spend considerable effort testing methods to find one that works well in their problem of interest.
  
  
  RESULTS
  In this work, we propose a method (MTAP) that substantially reduces the effort required to assess de novo regulatory motif discovery software. MTAP differs from previous attempts at regulatory motif assessment in that it automates motif discovery tool pipelines (something that traditionally required many manual steps), automatically constructs orthologous upstream sequences, and provides automated benchmarks for many popular tools. As a proof of concept, we have run benchmarks over human, mouse, fly, yeast, E. coli and B. subtilis.
  
  
  CONCLUSIONS
  MTAP presents a new approach to the challenging problem of assessing regulatory motif discovery methods. The most current version of MTAP can be downloaded from http://biobase.ist.unomaha.edu/
authors.affiliation: College of Information Science & Technology, University of Nebraska at Omaha, Omaha NE, USA. djquest@unmc.edu
authors.forename: Daniel
authors.initials: D
authors.lastname: Quest
authors.forename: Kathryn
authors.initials: K
authors.lastname: Dempsey
authors.forename: Mohammad
authors.initials: M
authors.lastname: Shafiullah
authors.forename: Dhundy
authors.initials: D
authors.lastname: Bastola
authors.forename: Hesham
authors.initials: H
authors.lastname: Ali
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D008969:Molecular Sequence Data; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
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