ArticleGenome biology2012
Modeling gene expression using chromatin features in various cellular contexts.
Article in Genome biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers, 1 of them a synthesis that pooled it.
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Who cites it
143 citing papers in PubMed, 1 synthesis or guideline pooled it, 295 citations in OpenAlex.
- Pooled it
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- Domestication shaped the chromatin landscape of grain amaranth.Nature communications · 2025Article
- GECSI: Large-scale chromatin state imputation from gene expression.bioRxiv : the preprint server for biology · 2025Article
- Towards universal modeling of transcript isoform expression levels.bioRxiv : the preprint server for biology · 2025Article
- High resolution of full-length RNA sequencing deciphers massive transcriptome complexity during zebrafish embryogenesis.BMC biology · 2025Article
- Prediction of gene expression using histone modification patterns extracted by Particle Swarm Optimization.Bioinformatics (Oxford, England) · 2025Article
- DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions.Plant communications · 2024Article
- Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart.Communications biology · 2024Article
- Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes.Genome research · 2024Article
- CatLearning: highly accurate gene expression prediction from histone mark.Briefings in bioinformatics · 2024Article
- Article
- Predicting gene expression state and prioritizing putative enhancers using 5hmC signal.Genome biology · 2024Article
- JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.Nucleic acids research · 2024Article
- Summary of ChIP-Seq Methods and Description of an Optimized ChIP-Seq Protocol.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Organ-specific characteristics govern the relationship between histone code dynamics and transcriptional reprogramming during nitrogen response in tomato.Communications biology · 2023Article
- MiREx: mRNA levels prediction from gene sequence and miRNA target knowledge.BMC bioinformatics · 2023Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Long-read genome assemblies reveal aZoological research · 2023Article
- A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors.Methods in molecular biology (Clifton, N.J.) · 2023Article
83 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 8 institutions in 3 countries.
Funding
Abstract
backgroundPrevious work has demonstrated that chromatin feature levels correlate with gene expression. The ENCODE project enables us to further explore this relationship using an unprecedented volume of data. Expression levels from more than 100,000 promoters were measured using a variety of high-throughput techniques applied to RNA extracted by different protocols from different cellular compartments of several human cell lines. ENCODE also generated the genome-wide mapping of eleven histone marks, one histone variant, and DNase I hypersensitivity sites in seven cell lines.
resultsWe built a novel quantitative model to study the relationship between chromatin features and expression levels. Our study not only confirms that the general relationships found in previous studies hold across various cell lines, but also makes new suggestions about the relationship between chromatin features and gene expression levels. We found that expression status and expression levels can be predicted by different groups of chromatin features, both with high accuracy. We also found that expression levels measured by CAGE are better predicted than by RNA-PET or RNA-Seq, and different categories of chromatin features are the most predictive of expression for different RNA measurement methods. Additionally, PolyA+ RNA is overall more predictable than PolyA- RNA among different cell compartments, and PolyA+ cytosolic RNA measured with RNA-Seq is more predictable than PolyA+ nuclear RNA, while the opposite is true for PolyA- RNA.
conclusionsOur study provides new insights into transcriptional regulation by analyzing chromatin features in different cellular contexts.
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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.