Learning protein-DNA interaction landscapes by integrating experimental data through computational models.


Journal Article

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.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.The C source code for compete and Python source code for MCMC-based inference are available at http://www.cs.duke.edu/∼amink.amink@cs.duke.eduSupplementary data are available at Bioinformatics online.

Full Text

Duke Authors

Cited Authors

  • Zhong, J; Wasson, T; Hartemink, AJ

Published Date

  • October 2014

Published In

Volume / Issue

  • 30 / 20

Start / End Page

  • 2868 - 2874

PubMed ID

  • 24974204

Pubmed Central ID

  • 24974204

Electronic International Standard Serial Number (EISSN)

  • 1367-4811

International Standard Serial Number (ISSN)

  • 1367-4803

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btu408


  • eng