ArticleBMC biology2022
Accurate prediction of functional states of cis-regulatory modules reveals common epigenetic rules in humans and mice.
Article in BMC biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 18 citations in OpenAlex.
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- Prediction and analysis of anti-aging peptides using data augmentation and machine learning algorithms.BMC biology · 2025Article
- Prediction of target genes and functional types of cis-regulatory modules in the human genome reveals their distinct properties.BMC biology · 2025Article
- EPIPDLF: a pretrained deep learning framework for predicting enhancer-promoter interactions.Bioinformatics (Oxford, England) · 2025Article
- Identification of co-localised transcription factors based on paired motifs analysis.IET systems biology · 2024Article
- Validated Negative Regions (VNRs) in the VISTA Database might be Truncated Forms of Bona Fide Enhancers.Advanced genetics (Hoboken, N.J.) · 2024Article
- Integrated convolution and self-attention for improving peptide toxicity prediction.Bioinformatics (Oxford, England) · 2024Article
- Article
- Underlying causes for prevalent false positives and false negatives in STARR-seq data.NAR genomics and bioinformatics · 2023Article
- Prediction accuracy of regulatory elements from sequence varies by functional sequencing technique.Frontiers in cellular and infection microbiology · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting cis-regulatory modules (CRMs) in a genome and their functional states in various cell/tissue types of the organism are two related challenging computational tasks. Most current methods attempt to simultaneously achieve both using data of multiple epigenetic marks in a cell/tissue type. Though conceptually attractive, they suffer high false discovery rates and limited applications. To fill the gaps, we proposed a two-step strategy to first predict a map of CRMs in the genome, and then predict functional states of all the CRMs in various cell/tissue types of the organism. We have recently developed an algorithm for the first step that was able to more accurately and completely predict CRMs in a genome than existing methods by integrating numerous transcription factor ChIP-seq datasets in the organism. Here, we presented machine-learning methods for the second step.
resultsWe showed that functional states in a cell/tissue type of all the CRMs in the genome could be accurately predicted using data of only 1~4 epigenetic marks by a variety of machine-learning classifiers. Our predictions are substantially more accurate than the best achieved so far. Interestingly, a model trained on a cell/tissue type in humans can accurately predict functional states of CRMs in different cell/tissue types of humans as well as of mice, and vice versa. Therefore, epigenetic code that defines functional states of CRMs in various cell/tissue types is universal at least in humans and mice. Moreover, we found that from tens to hundreds of thousands of CRMs were active in a human and mouse cell/tissue type, and up to 99.98% of them were reutilized in different cell/tissue types, while as small as 0.02% of them were unique to a cell/tissue type that might define the cell/tissue type.
conclusionsOur two-step approach can accurately predict functional states in any cell/tissue type of all the CRMs in the genome using data of only 1~4 epigenetic marks. Our approach is also more cost-effective than existing methods that typically use data of more epigenetic marks. Our results suggest common epigenetic rules for defining functional states of CRMs in various cell/tissue types in humans and mice.
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