ArticleMethods (San Diego, Calif.)2021
Decoding regulatory structures and features from epigenomics profiles: A Roadmap-ENCODE Variational Auto-Encoder (RE-VAE) model.
Article in Methods (San Diego, Calif.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Fast Fourier transform is a training-free, ultrafast, highly efficient, and fully interpretable approach for epigenomic data compression.Scientific reports · 2025Article
- siVAE: interpretable deep generative models for single-cell transcriptomes.Genome biology · 2023Article
- Variational autoencoding of gene landscapes during mouse CNS development uncovers layered roles of Polycomb Repressor Complex 2.Nucleic acids research · 2022Article
- Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner.Nucleic acids research · 2021Article
- Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning.Briefings in bioinformatics · 2021Article
- Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.Nucleic acids research · 2021Article
- Integrative computational epigenomics to build data-driven gene regulation hypotheses.GigaScience · 2020Article
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4 authors.
Funding
Abstract
The development of chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing (ChIP-seq) technologies has promoted generation of large-scale epigenomics data, providing us unprecedented opportunities to explore the landscape of epigenomic profiles at scales across both histone marks and tissue types. In addition to many tools directly for data analysis, advanced computational approaches, such as deep learning, have recently become promising to deeply mine the data structures and identify important regulators from complex functional genomics data. We implemented a neural network framework, a Variational Auto-Encoder (VAE) model, to explore the epigenomic data from the Roadmap Epigenomics Project and the Encyclopedia of DNA Elements (ENCODE) project. Our model is applied to 935 reference samples, covering 28 tissues and 12 histone marks. We used the enhancer and promoter regions as the annotation features and ChIP-seq signal values in these regions as the feature values. Through a parameter sweep process, we identified the suitable hyperparameter values and built a VAE model to represent the epigenomics data and to further explore the biological regulation. The resultant Roadmap-ENCODE VAE (RE-VAE) model contained data compression and feature representation. Using the compressed data in the latent space, we found that the majority of histone marks were well clustered but not for tissues or cell types. Tissue or cell specificity was observed only in some histone marks (e.g., H3K4me3 and H3K27ac) and could be characterized when the number of tissue samples is large (e.g., blood and brain). In blood, the contributive regions and genes identified by RE-VAE model were confirmed by tissue-specificity enrichment analysis with an independent tissue expression panel. Finally, we demonstrated that RE-VAE model could detect cancer cell lines with similar epigenomics profiles. In conclusion, we introduced and implemented a VAE model to represent large-scale epigenomics data. The model could be used to explore classifications of histone modifications and tissue/cell specificity and to classify new data with unknown sources.
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