ArticlePLoS computational biology2013
Finding associations among histone modifications using sparse partial correlation networks.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Mutant EZH2 alters the epigenetic network and increases epigenetic heterogeneity in B cell lymphoma.PLoS biology · 2025Article
- asteRIa enables robust interaction modeling between chromatin modifications and epigenetic readers.Nucleic acids research · 2024Article
- Rescuing biologically relevant consensus regions across replicated samples.BMC bioinformatics · 2023Article
- Broad domains of histone marks in the highly compactGenome research · 2022Article
- Epigenomic profiling of primate lymphoblastoid cell lines reveals the evolutionary patterns of epigenetic activities in gene regulatory architectures.Nature communications · 2021Article
- CpG content-dependent associations between transcription factors and histone modifications.PloS one · 2021Article
- The Distance Precision Matrix: computing networks from non-linear relationships.Bioinformatics (Oxford, England) · 2019Article
- Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions.Biophysical journal · 2017Article
- Integration of multi-omics data for prediction of phenotypic traits using random forest.BMC bioinformatics · 2016Article
- Comprehensive benchmarking reveals H2BK20 acetylation as a distinctive signature of cell-state-specific enhancers and promoters.Genome research · 2016Article
- ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data.Genome biology · 2016Article
- Integrating Epigenomics into the Understanding of Biomedical Insight.Bioinformatics and biology insights · 2016Review
- Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues.Nature biotechnology · 2015Article
- Metabolic regulation of histone post-translational modifications.ACS chemical biology · 2015Review
- Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform.BioData mining · 2015Article
- Inference of interactions between chromatin modifiers and histone modifications: from ChIP-Seq data to chromatin-signaling.Nucleic acids research · 2014Article
- Global quantitative modeling of chromatin factor interactions.PLoS computational biology · 2014Article
- Integrative radiation systems biology.Radiation oncology (London, England) · 2014Review
- Computational and experimental methods to decipher the epigenetic code.Frontiers in genetics · 2014Article
- Inferring disease transmission networks at a metapopulation level.Health information science and systems · 2014Article
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3 authors.
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Abstract
Histone modifications are known to play an important role in the regulation of transcription. While individual modifications have received much attention in genome-wide analyses, little is known about their relationships. Some authors have built Bayesian networks of modifications, however most often they have used discretized data, and relied on unrealistic assumptions such as the absence of feedback mechanisms or hidden confounding factors. Here, we propose to infer undirected networks based on partial correlations between histone modifications. Within the partial correlation framework, correlations among two variables are controlled for associations induced by the other variables. Partial correlation networks thus focus on direct associations of histone modifications. We apply this methodology to data in CD4+ cells. The resulting network is well supported by common knowledge. When pairs of modifications show a large difference between their correlation and their partial correlation, a potential confounding factor is identified and provided as explanation. Data from different cell types (IMR90, H1) is also exploited in the analysis to assess the stability of the networks. The results are remarkably similar across cell types. Based on this observation, the networks from the three cell types are integrated into a consensus network to increase robustness. The data and the results discussed in the manuscript can be found, together with code, on http://spcn.molgen.mpg.de/index.html.
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