title: Seeder: discriminative seeding DNA motif discovery.
abstract: BACKGROUND
  The computational identification of transcription factor binding sites is a major challenge in bioinformatics and an important complement to experimental approaches.
  
  
  RESULTS
  We describe a novel, exact discriminative seeding DNA motif discovery algorithm designed for fast and reliable prediction of cis-regulatory elements in eukaryotic promoters. The algorithm is tested on biological benchmark data and shown to perform equally or better than other motif discovery tools. The algorithm is applied to the analysis of plant tissue-specific promoter sequences and successfully identifies key regulatory elements.
authors: Department of Plant Science, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada.
authors: François
authors: F
authors: Fauteux
authors: Mathieu
authors: M
authors: Blanchette
authors: Martina V
authors: MV
authors: Strömvik
mesh_terms: D000465:Algorithms; D000818:Animals; D017360:Arabidopsis / Q000235:genetics; D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology; D006801:Humans; D008969:Molecular Sequence Data; D011401:Promoter Regions, Genetic; D050436:Regulatory Elements, Transcriptional; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000737:chemistry / Q000378:metabolism*
chemical_list: D014157:Transcription Factors