title: Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence.
abstract: BACKGROUND
  Computational modeling transcription factor (TF) sequence specificity is an important research topic in regulatory genomics. A systematic comparison of 26 algorithms to learn TF-DNA binding specificity in in vitro protein-binding microarray (PBM) data was published recently, but the quality of those examined PBMs was not evaluated completely.
  
  
  RESULTS
  Here, new quality-control parameters such as principal component analysis (PCA) ellipse is proposed to assess the data quality for either single or paired PBMs. Additionally, a biophysical model of TF-DNA interactions including adjacent dinucleotide interdependence was implemented in a new program - BayesPI2, where sparse Bayesian learning and relevance vector machine are used to predict unknown model parameters. Then, 66 mouse TFs from the DREAM5 challenge were classified into two groups (i.e. good vs. bad) based on the paired PBM quality-control parameters. Subsequently, computational methods to model TF sequence specificity were evaluated between the two groups.
  
  
  CONCLUSIONS
  Results indicate that both the algorithm performance and the predicted TF-binding energy-level of a motif are significantly influenced by PBM data quality, where poor PBM data quality is linked to specific protein domains (e.g. C2H2 DNA-binding domain). Especially, the new dinucleotide energy-dependent model (BayesPI2) offers great improvement in testing prediction accuracy over the simple energy-independent model, for at least 21% of analyzed the TFs.
authors.affiliation: Pathology Department, Oslo University Hospital - Norwegian Radium Hospital, Montebello, Oslo, 0310, Norway. junbai.wang@rr-research.no.
authors.forename: Junbai
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D051379:Mice; D059372:Nucleotide Motifs; D009841:Oligonucleotides / Q000378:metabolism*; D025341:Principal Component Analysis; D040081:Protein Array Analysis / Q000379:methods*; D011485:Protein Binding; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D009841:Oligonucleotides; D014157:Transcription Factors; D004247:DNA