ArticlePLoS computational biology2014
Global quantitative modeling of chromatin factor interactions.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 36 citations in OpenAlex.
- Formation of topologically associated chromatin domains using quantum annealing.Scientific reports · 2026Article
- Rescuing biologically relevant consensus regions across replicated samples.BMC bioinformatics · 2023Article
- AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.Nucleic acids research · 2019Article
- Learning the Formation Mechanism of Domain-Level Chromatin States with Epigenomics Data.Biophysical journal · 2019Article
- The Distance Precision Matrix: computing networks from non-linear relationships.Bioinformatics (Oxford, England) · 2019Article
- Semi-nonparametric modeling of topological domain formation from epigenetic data.Algorithms for molecular biology : AMB · 2019Article
- HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures.BMC bioinformatics · 2018Article
- Iterative random forests to discover predictive and stable high-order interactions.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- StereoGene: rapid estimation of genome-wide correlation of continuous or interval feature data.Bioinformatics (Oxford, England) · 2017Article
- ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data.Genome biology · 2016Article
- Probabilistic modelling of chromatin code landscape reveals functional diversity of enhancer-like chromatin states.Nature communications · 2016Article
- Inferential modeling of 3D chromatin structure.Nucleic acids research · 2015Article
- Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues.Nature biotechnology · 2015Article
- 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
- Computational and experimental methods to decipher the epigenetic code.Frontiers in genetics · 2014Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Chromatin is the driver of gene regulation, yet understanding the molecular interactions underlying chromatin factor combinatorial patterns (or the "chromatin codes") remains a fundamental challenge in chromatin biology. Here we developed a global modeling framework that leverages chromatin profiling data to produce a systems-level view of the macromolecular complex of chromatin. Our model ultilizes maximum entropy modeling with regularization-based structure learning to statistically dissect dependencies between chromatin factors and produce an accurate probability distribution of chromatin code. Our unsupervised quantitative model, trained on genome-wide chromatin profiles of 73 histone marks and chromatin proteins from modENCODE, enabled making various data-driven inferences about chromatin profiles and interactions. We provided a highly accurate predictor of chromatin factor pairwise interactions validated by known experimental evidence, and for the first time enabled higher-order interaction prediction. Our predictions can thus help guide future experimental studies. The model can also serve as an inference engine for predicting unknown chromatin profiles--we demonstrated that with this approach we can leverage data from well-characterized cell types to help understand less-studied cell type or conditions.
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