Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Chair in Cancer Adaptation and Evolution



+44 (0)20 7594 2808l.magnani CV




137ICTEM buildingHammersmith Campus






BibTex format

author = {Chen, X and Jung, JG and Shajahan-Haq, AN and Clarke, R and Shih, IM and Wang, Y and Magnani, L and Wang, TL and Xuan, J},
doi = {nar/gkv1491},
journal = {Nucleic Acids Research},
title = {ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.},
url = {},
volume = {44},
year = {2015}

RIS format (EndNote, RefMan)

AB - 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.
AU - Chen,X
AU - Jung,JG
AU - Shajahan-Haq,AN
AU - Clarke,R
AU - Shih,IM
AU - Wang,Y
AU - Magnani,L
AU - Wang,TL
AU - Xuan,J
DO - nar/gkv1491
PY - 2015///
SN - 1362-4962
TI - ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.
T2 - Nucleic Acids Research
UR -
UR -
VL - 44
ER -