title: BEESEM: estimation of binding energy models using HT-SELEX data.
abstract: BACKGROUND
  Characterizing the binding specificities of transcription factors (TFs) is crucial to the study of gene expression regulation. Recently developed high-throughput experimental methods, including protein binding microarrays (PBM) and high-throughput SELEX (HT-SELEX), have enabled rapid measurements of the specificities for hundreds of TFs. However, few studies have developed efficient algorithms for estimating binding motifs based on HT-SELEX data. Also the simple method of constructing a position weight matrix (PWM) by comparing the frequency of the preferred sequence with single-nucleotide variants has the risk of generating motifs with higher information content than the true binding specificity.
  
  
  RESULTS
  We developed an algorithm called BEESEM that builds on a comprehensive biophysical model of protein-DNA interactions, which is trained using the expectation maximization method. BEESEM is capable of selecting the optimal motif length and calculating the confidence intervals of estimated parameters. By comparing BEESEM with the published motifs estimated using the same HT-SELEX data, we demonstrate that BEESEM provides significant improvements. We also evaluate several motif discovery algorithms on independent PBM and ChIP-seq data. BEESEM provides significantly better fits to in vitro data, but its performance is similar to some other methods on in vivo data under the criterion of the area under the receiver operating characteristic curve (AUROC). This highlights the limitations of the purely rank-based AUROC criterion. Using quantitative binding data to assess models, however, demonstrates that BEESEM improves on prior models.
  
  
  METHODS
  Freely available on the web at http://stormo.wustl.edu/resources.html .
  
  
  BACKGROUND
  stormo@wustl.edu.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Genetics.
authors.forename: Shuxiang
authors.initials: S
authors.lastname: Ruan
authors.affiliation: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis 63110, USA.
authors.forename: S Joshua
authors.initials: SJ
authors.lastname: Swamidass
authors.affiliation: Department of Genetics.
authors.forename: Gary D
authors.initials: GD
authors.lastname: Stormo
mesh_terms: D000465:Algorithms; D000818:Animals; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D004247:DNA / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D051379:Mice; D056510:Position-Specific Scoring Matrices; D040081:Protein Array Analysis / Q000379:methods*; D011485:Protein Binding; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software; D013816:Thermodynamics; D014157:Transcription Factors / Q000737:chemistry / Q000378:metabolism*
chemical_list: D014157:Transcription Factors; D004247:DNA