Introduction

Computational prediction of transcription factor (TF) binding sites in different cell types is challenging. Recent technology development allows us to determine the genome-wide chromatin accessibility in various cellular and developmental contexts. The chromatin accessibility profiles provide useful information in prediction of TF binding events in various physiological conditions. Furthermore, ChIP-Seq analysis was used to determine genome-wide binding sites for a range of different TFs in multiple cell types. Integration of these two types of genomic information can improve the prediction of TF binding events.We assessed to what extent a model built upon on other TFs and/or other cell types could be used to predict the binding sites of TFs of interest. A random forest model was built using a set of cell type-independent features such as specific sequences recognized by the TFs and evolutionary conservation, as well as cell type-specific features derived from chromatin accessibility data. Our analysis suggested that the models learned from other TFs and/or cell lines performed almost as well as the model learned from the target TF in the cell type of interest. Interestingly, models based on multiple TFs performed better than single-TF models. Finally, we proposed a universal model, BPAC, which was generated using ChIP-Seq data from multiple TFs in various cell types.Integrating chromatin accessibility information with sequence information improves prediction of TF binding.The prediction of TF binding is transferable across TFs and/or cell lines suggesting there are a set of universal "rules". A computational tool was developed to predict TF binding sites based on the universal "rules".

Publications

  1. Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility.
    Cite this
    Liu S, Zibetti C, Wan J, Wang G, Blackshaw S, Qian J, 2017-07-01 - BMC bioinformatics

Credits

  1. Sheng Liu
    Developer

    Department of Ophthalmology, Johns Hopkins University School of Medicine, United States of America

  2. Cristina Zibetti
    Developer

    Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, United States of America

  3. Jun Wan
    Developer

    Department of Ophthalmology, Johns Hopkins University School of Medicine, United States of America

  4. Guohua Wang
    Developer

    Department of Ophthalmology, Johns Hopkins University School of Medicine, United States of America

  5. Seth Blackshaw
    Developer

    Institute for Cell Engineering, Johns Hopkins University School of Medicine, United States of America

  6. Jiang Qian
    Investigator

    Department of Ophthalmology, Johns Hopkins University School of Medicine, United States of America

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Summary
AccessionBT006627
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByJiang Qian