ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles

Xi Chen, Jin Gyoung Jung, Ayesha N. Shajahan-Haq, Robert Clarke, Ie Ming Shih, Yue Wang, Luca Magnani, Tian Li Wang, Jianhua Xuan

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article numbere65
JournalNucleic acids research
Volume44
Issue number7
DOIs
StatePublished - Dec 23 2015

ASJC Scopus subject areas

  • Genetics

Fingerprint Dive into the research topics of 'ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles'. Together they form a unique fingerprint.

  • Cite this