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This article is part of the supplement: Selected articles from the Twelfth Asia Pacific Bioinformatics Conference (APBC 2014): Genomics

Open Access Proceedings

A nucleosomal approach to inferring causal relationships of histone modifications

Ngoc Tu Le1*, Tu Bao Ho1, Bich Hai Ho2 and Dang Hung Tran3

  • * Corresponding author: Ngoc T Le

Author Affiliations

1 Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan

2 Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Hanoi, Vietnam

3 Hanoi National University of Education, 36 Xuan Thuy, Cau Giay, Hanoi, Vietnam

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BMC Genomics 2014, 15(Suppl 1):S7  doi:10.1186/1471-2164-15-S1-S7

Published: 24 January 2014

Abstract

Motivation

Histone proteins are subject to various posttranslational modifications (PTMs). Elucidating their functional relationships is crucial toward understanding many biological processes. Bayesian network (BN)-based approaches have shown the advantage of revealing causal relationships, rather than simple cooccurrences, of PTMs. Previous works employing BNs to infer causal relationships of PTMs require that all confounders should be included. This assumption, however, is unavoidably violated given the fact that several modifications are often regulated by a common but unobserved factor. An existing non-parametric method can be applied to tackle the problem but the complexity and inflexibility make it impractical.

Results

We propose a novel BN-based method to infer causal relationships of histone modifications. First, from the evidence that nucleosome organization in vivo significantly affects the activities of PTM regulators working on chromatin substrate, hidden confounders of PTMs are selectively introduced by an information-theoretic criterion. Causal relationships are then inferred from a network model of both PTMs and the derived confounders. Application on human epigenomic data shows the advantage of the proposed method, in terms of computational performance and support from literature. Requiring less strict data assumptions also makes it more practical. Interestingly, analysis of the most significant relationships suggests that the proposed method can recover biologically relevant causal effects between histone modifications, which should be important for future investigation of histone crosstalk.