title: Discovering multiple realistic TFBS motifs based on a generalized model.
abstract: BACKGROUND
  Identification of transcription factor binding sites (TFBSs) is a central problem in Bioinformatics on gene regulation. de novo motif discovery serves as a promising way to predict and better understand TFBSs for biological verifications. Real TFBSs of a motif may vary in their widths and their conservation degrees within a certain range. Deciding a single motif width by existing models may be biased and misleading. Additionally, multiple, possibly overlapping, candidate motifs are desired and necessary for biological verification in practice. However, current techniques either prohibit overlapping TFBSs or lack explicit control of different motifs.
  
  
  RESULTS
  We propose a new generalized model to tackle the motif widths by considering and evaluating a width range of interest simultaneously, which should better address the width uncertainty. Moreover, a meta-convergence framework for genetic algorithms (GAs), is proposed to provide multiple overlapping optimal motifs simultaneously in an effective and flexible way. Users can easily specify the difference amongst expected motif kinds via similarity test. Incorporating Genetic Algorithm with Local Filtering (GALF) for searching, the new GALF-G (G for generalized) algorithm is proposed based on the generalized model and meta-convergence framework.
  
  
  CONCLUSIONS
  GALF-G was tested extensively on over 970 synthetic, real and benchmark datasets, and is usually better than the state-of-the-art methods. The range model shows an increase in sensitivity compared with the single-width ones, while providing competitive precisions on the E. coli benchmark. Effectiveness can be maintained even using a very small population, exhibiting very competitive efficiency. In discovering multiple overlapping motifs in a real liver-specific dataset, GALF-G outperforms MEME by up to 73% in overall F-scores. GALF-G also helps to discover an additional motif which has probably not been annotated in the dataset. http://www.cse.cuhk.edu.hk/%7Etmchan/GALFG/
authors.affiliation: Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, N, T,, Hong Kong. tmchan@cse.cuhk.edu.hk
authors.forename: Tak-Ming
authors.initials: TM
authors.lastname: Chan
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.forename: Kwong-Sak
authors.initials: KS
authors.lastname: Leung
authors.forename: Kin-Hong
authors.initials: KH
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D014157:Transcription Factors