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De novo identification of in vivo binding sites from ChIP-chip data

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Advances in high-throughput technologies such as ChIP-chip and the completion of human genomic sequences allow analysis of the mechanisms of gene regulation on a systems level. In this study, we have developed a computational genomics approach (ChIPModules) and a motif discovery approach (ChIPMotifs) to mine the ChIP-chip data. The ChIPModules approach begins with experimentally determined binding sites and integrates positional weight matrices, a comparative genomics approach, and statistical learning methods to identify transcriptional regulatory modules. Using E2F1 ChIP-chip data performed on ENCODE regions in both HeLa and MCF7 cells, we have identified five regulatory modules for E2F1. One of modules was validated by using ChIP-chip with arrays containing ~14,000 human promoters. The ChIPMotifs approach incorporates a bootstrap re-sampling method to statistically infer the optimal cutoff threshold for a position weight matrix (PWM) of a motif identified from ChIP-chip data by ab initio motif discovery programs. Using OCT4 ChIP-chip data, we developed an in vivo OCT4 PWM. We then used this PWM and our ChIPModules to identify transcription factors co-localizing with OCT4 in a testicular germ cell tumor (Ntera2 cells).


This work was supported in part by Public Health Service grant CA45250, HG003129, and DK067889 to P.J.F. and a bioinformatics start-up funding to V.X.J at the University of Memphis. As part of our analyses, we used ChIP-chip data collected as part of the ENCODE Project Consortium.

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Correspondence to Victor Jin.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Jin, V., Rabinovich, A., O'Geen, H. et al. De novo identification of in vivo binding sites from ChIP-chip data. BMC Bioinformatics 9 (Suppl 7), P24 (2008).

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