ArticlePLoS computational biology2021
Differential contribution to gene expression prediction of histone modifications at enhancers or promoters.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.Nucleic acids research · 2025Article
- Mitotic bookmarking redundancy by nuclear receptors in pluripotent cells.Nature structural & molecular biology · 2024Article
- A computational pipeline to learn gene expression predictive models from epigenetic information at enhancers or promoters.STAR protocols · 2023Article
- Screening and identification of hub gene and differential gene and mutation sequence analysis of related genes in colorectal cancer based on bioinformatics analysis.Journal of gastrointestinal oncology · 2022Article
- OCT4 activates a Suv39h1-repressive antisense lncRNA to couple histone H3 Lysine 9 methylation to pluripotency.Nucleic acids research · 2022Article
- Argonaute proteins regulate a specific network of genes through KLF4 in mouse embryonic stem cells.Stem cell reports · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ChIP-seq signal of histone modifications at promoters is a good predictor of gene expression in different cellular contexts, but whether this is also true at enhancers is not clear. To address this issue, we develop quantitative models to characterize the relationship of gene expression with histone modifications at enhancers or promoters. We use embryonic stem cells (ESCs), which contain a full spectrum of active and repressed (poised) enhancers, to train predictive models. As many poised enhancers in ESCs switch towards an active state during differentiation, predictive models can also be trained on poised enhancers throughout differentiation and in development. Remarkably, we determine that histone modifications at enhancers, as well as promoters, are predictive of gene expression in ESCs and throughout differentiation and development. Importantly, we demonstrate that their contribution to the predictive models varies depending on their location in enhancers or promoters. Moreover, we use a local regression (LOESS) to normalize sequencing data from different sources, which allows us to apply predictive models trained in a specific cellular context to a different one. We conclude that the relationship between gene expression and histone modifications at enhancers is universal and different from promoters. Our study provides new insight into how histone modifications relate to gene expression based on their location in enhancers or promoters.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.