ArticleNucleic acids research2012
Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed, 145 citations in OpenAlex.
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- Computational modeling of chromatin accessibility identified important epigenomic regulators.BMC genomics · 2022Article
- Altered transcription factor targeting is associated with differential peripheral blood mononuclear cell proportions in sarcoidosis.Frontiers in immunology · 2022Article
- Differential contribution to gene expression prediction of histone modifications at enhancers or promoters.PLoS computational biology · 2021Article
- Multifaceted Regulation of MicroRNA Biogenesis: Essential Roles and Functional Integration in Neuronal and Glial Development.International journal of molecular sciences · 2021Review
- Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources.BMC bioinformatics · 2021Article
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- CpG content-dependent associations between transcription factors and histone modifications.PloS one · 2021Article
- Revealing Epigenetic Factors of circRNA Expression by Machine Learning in Various Cellular Contexts.iScience · 2020Article
- Leveraging functional annotation to identify genes associated with complex diseases.PLoS computational biology · 2020Article
- NFATc2-dependent epigenetic upregulation of CXCL14 is involved in the development of neuropathic pain induced by paclitaxel.Journal of neuroinflammation · 2020Article
- DOT1L-mediated murine neuronal differentiation associates with H3K79me2 accumulation and preserves SOX2-enhancer accessibility.Nature communications · 2020Article
- Supervised enhancer prediction with epigenetic pattern recognition and targeted validation.Nature methods · 2020Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription factor (TF) binding and histone modification (HM) are important for the precise control of gene expression. Hence, we constructed statistical models to relate these to gene expression levels in mouse embryonic stem cells. While both TF binding and HMs are highly 'predictive' of gene expression levels (in a statistical, but perhaps not strictly mechanistic, sense), we find they show distinct differences in the spatial patterning of their predictive strength: TF binding achieved the highest predictive power in a small DNA region centered at the transcription start sites of genes, while the HMs exhibited high predictive powers across a wide region around genes. Intriguingly, our results suggest that TF binding and HMs are redundant in strict statistical sense for predicting gene expression. We also show that our TF and HM models are cell line specific; specifically, TF binding and HM are more predictive of gene expression in the same cell line, and the differential gene expression between cell lines is predictable by differential HMs. Finally, we found that the models trained solely on protein-coding genes are predictive of expression levels of microRNAs, suggesting that their regulation by TFs and HMs may share a similar mechanism to that for protein-coding genes.
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