title: Learning protein-DNA interaction landscapes by integrating experimental data through computational models.
abstract: BACKGROUND
  Transcriptional regulation is directly enacted by the interactions between DNA and many proteins, including transcription factors (TFs), nucleosomes and polymerases. A critical step in deciphering transcriptional regulation is to infer, and eventually predict, the precise locations of these interactions, along with their strength and frequency. While recent datasets yield great insight into these interactions, individual data sources often provide only partial information regarding one aspect of the complete interaction landscape. For example, chromatin immunoprecipitation (ChIP) reveals the binding positions of a protein, but only for one protein at a time. In contrast, nucleases like MNase and DNase can be used to reveal binding positions for many different proteins at once, but cannot easily determine the identities of those proteins. Currently, few statistical frameworks jointly model these different data sources to reveal an accurate, holistic view of the in vivo protein-DNA interaction landscape.
  
  
  RESULTS
  Here, we develop a novel statistical framework that integrates different sources of experimental information within a thermodynamic model of competitive binding to jointly learn a holistic view of the in vivo protein-DNA interaction landscape. We show that our framework learns an interaction landscape with increased accuracy, explaining multiple sets of data in accordance with thermodynamic principles of competitive DNA binding. The resulting model of genomic occupancy provides a precise mechanistic vantage point from which to explore the role of protein-DNA interactions in transcriptional regulation.
  
  
  METHODS
  The C source code for compete and Python source code for MCMC-based inference are available at http://www.cs.duke.edu/∼amink.
  
  
  BACKGROUND
  amink@cs.duke.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA.
authors: Jianling
authors: J
authors: Zhong
authors: Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA.
authors: Todd
authors: T
authors: Wasson
authors: Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA.
authors: Alexander J
authors: AJ
authors: Hartemink
mesh_terms: D001667:Binding, Competitive; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics / Q000378:metabolism*; D004268:DNA-Binding Proteins / Q000378:metabolism*; D005786:Gene Expression Regulation; D008954:Models, Biological; D009707:Nucleosomes / Q000235:genetics / Q000378:metabolism; D011485:Protein Binding; D013816:Thermodynamics; D014157:Transcription Factors / Q000378:metabolism; D014158:Transcription, Genetic
chemical_list: D004268:DNA-Binding Proteins; D009707:Nucleosomes; D014157:Transcription Factors; D004247:DNA