Statistical Identification of Co-regulatory Gene Modules using Multiple ChIP-Seq Experiments

Xi Chen; Xu Shi; Ayesha N. Shajahan-Haq; Leena Hilakivi-Clarke; Robert Clarke; Jianhua Xuan

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Paper

Statistical Identification of Co-regulatory Gene Modules using Multiple ChIP-Seq Experiments

Topics: Biostatistics and Stochastic Models; Data Mining and Machine Learning; Model Design and Evaluation; Next Generation Sequencing; Sequence Analysis

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 0BIOSTEC, 109-116, 2014 , ESEO, Angers, Loire Valley, France

Authors: Xi Chen ¹ ; Xu Shi ¹ ; Ayesha N. Shajahan-Haq ² ; Leena Hilakivi-Clarke ² ; Robert Clarke ² and Jianhua Xuan ¹

Affiliations: ¹ Virginia Tech, United States ; ² Georgetown University, United States

Keyword(s): ChIP-Seq, TFBS, Target Genes, MCMC, Co-regulatory Modules.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Biostatistics and Stochastic Models ; Data Mining and Machine Learning ; Model Design and Evaluation ; Next Generation Sequencing ; Sequence Analysis

Abstract: ChIP-Seq experiments provide accurate measurements of the regulatory roles of transcription factors (TFs) under specific condition. Downstream target genes can be detected by analyzing the enriched TF binding sites (TFBSs) in genes’ promoter regions. The location and statistical information of TFBSs make it possible to evaluate the relative importance of each binding. Based on the assumption that the TFBSs of one ChIP-Seq experiment follow the same specific location distribution, a statistical model is first proposed using both location and significance information of peaks to weigh target genes. With genes’ binding scores from different TFs, we merge them into a weighted binding matrix. A Markov Chain Monte Carlo (MCMC) based approach is then applied to the binding matrix for co-regulatory module identification. We demonstrate the efficiency of our statistical model on an ER-α ChIP-Seq dataset and further identify co-regulatory modules by using eleven breast cancer related TFs from ENCODE ChIP-Seq datasets. The results show that the TFs in individual module regulate common high score target genes; the association of TFs is biologically meaningful, and the functional roles of TFs and target genes are consistent. (More)

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Paper citation in several formats:

Chen, X.; Shi, X.; N. Shajahan-Haq, A.; Hilakivi-Clarke, L.; Clarke, R. and Xuan, J. (2014). Statistical Identification of Co-regulatory Gene Modules using Multiple ChIP-Seq Experiments. In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2014) - BIOINFORMATICS; ISBN 978-989-758-012-3; ISSN 2184-4305, SciTePress, pages 109-116. DOI: 10.5220/0004736801090116

@conference{bioinformatics14,
author={Xi Chen. and Xu Shi. and Ayesha {N. Shajahan{-}Haq}. and Leena Hilakivi{-}Clarke. and Robert Clarke. and Jianhua Xuan.},
title={Statistical Identification of Co-regulatory Gene Modules using Multiple ChIP-Seq Experiments},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2014) - BIOINFORMATICS},
year={2014},
pages={109-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004736801090116},
isbn={978-989-758-012-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2014) - BIOINFORMATICS
TI - Statistical Identification of Co-regulatory Gene Modules using Multiple ChIP-Seq Experiments
SN - 978-989-758-012-3
IS - 2184-4305
AU - Chen, X.
AU - Shi, X.
AU - N. Shajahan-Haq, A.
AU - Hilakivi-Clarke, L.
AU - Clarke, R.
AU - Xuan, J.
PY - 2014
SP - 109
EP - 116
DO - 10.5220/0004736801090116
PB - SciTePress