A nucleosomal approach to inferring causal relationships of histone modifications

Abstract

MotivationHistone 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.ResultsWe 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.

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“…To incorporate these hidden associations, a new treemap visualization scheme for the directly associated concepts and treemap with linking where co-occurrences are the relation strength measure the indirectly associated concepts [123]. This web tool has been used to extract information about the indirect interactions between post-translational modifications of histone proteins [129].…”

ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data

“…ENCODE identified conditional-dependence relationships among groups of up to approximately 100 data sets in specific genomic contexts [ 20 ]. Other authors used partial correlation on 21 data sets [ 32 ], Bayesian networks for 38 data sets [ 34 ], and partial correlation combined with penalized regression for 27 human data sets [ 49 ] and for 139 mouse embryonic stem cell data sets [ 25 ]. Still other authors used a Markov random field with 73 data sets in D. melanogaster [ 65 ], a Boltzmann machine with 116 human transcription factors [ 40 ], and bootstrapped Bayesian networks in 112 regulatory factors in D. melanogaster [ 3 , 57 ].…”