Introduction

Chromatin immunoprecipitation with massively parallel DNA sequencing (ChIP-seq) has greatly improved the reliability with which transcription factor binding sites (TFBSs) can be identified from genome-wide profiling studies. Many computational tools are developed to detect binding events or peaks, however the robust detection of weak binding events remains a challenge for current peak calling tools. We have developed a novel Bayesian approach (ChIP-BIT) to reliably detect TFBSs and their target genes by jointly modeling binding signal intensities and binding locations of TFBSs. Specifically, a Gaussian mixture model is used to capture both binding and background signals in sample data. As a unique feature of ChIP-BIT, background signals are modeled by a local Gaussian distribution that is accurately estimated from the input data. Extensive simulation studies showed a significantly improved performance of ChIP-BIT in target gene prediction, particularly for detecting weak binding signals at gene promoter regions. We applied ChIP-BIT to find target genes from NOTCH3 and PBX1 ChIP-seq data acquired from MCF-7 breast cancer cells. TF knockdown experiments have initially validated about 30% of co-regulated target genes identified by ChIP-BIT as being differentially expressed in MCF-7 cells. Functional analysis on these genes further revealed the existence of crosstalk between Notch and Wnt signaling pathways.

Publications

  1. ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.
    Cite this
    Chen X, Jung JG, Shajahan-Haq AN, Clarke R, Shih IeM, Wang Y, Magnani L, Wang TL, Xuan J, 2016-04-01 - Nucleic acids research

Credits

  1. Xi Chen
    Developer

    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, United States of America

  2. Jin-Gyoung Jung
    Developer

    Department of Pathology, Johns Hopkins Medical Institutions, United States of America

  3. Ayesha N Shajahan-Haq
    Developer

    Department of Oncology, Lombardi Comprehensive Cancer Center, United States of America

  4. Robert Clarke
    Developer

    Department of Oncology, Lombardi Comprehensive Cancer Center, United States of America

  5. Ie-Ming Shih
    Developer

    Department of Pathology, Johns Hopkins Medical Institutions, United States of America

  6. Yue Wang
    Developer

    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, United States of America

  7. Luca Magnani
    Developer

    Department of Surgery and Cancer, Imperial College London

  8. Tian-Li Wang
    Developer

    Department of Pathology, Johns Hopkins Medical Institutions, United States of America

  9. Jianhua Xuan
    Investigator

    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, United States of America

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Summary
AccessionBT007053
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByJianhua Xuan