title: Optimized mixed Markov models for motif identification.
abstract: BACKGROUND
  Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples.
  
  
  RESULTS
  We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at 1.
  
  
  CONCLUSIONS
  Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods.
authors.affiliation: Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA. huang6@niehs.nih.gov
authors.forename: Weichun
authors.initials: W
authors.lastname: Huang
authors.forename: David M
authors.initials: DM
authors.lastname: Umbach
authors.forename: Uwe
authors.initials: U
authors.lastname: Ohler
authors.forename: Leping
authors.initials: L
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D011381:Programming Languages; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins