ArticleGenome research2012
Understanding transcriptional regulation by integrative analysis of transcription factor binding data.
Article in Genome research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers.
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Who cites it
114 citing papers in PubMed.
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- Deciphering mitochondrial dysfunction in keratoconus: Insights into ACSL4 from machine learning-based bulk and single-cell transcriptome analyses and experimental validation.Computational and structural biotechnology journal · 2025Article
- Predicting gene expression state and prioritizing putative enhancers using 5hmC signal.Genome biology · 2024Article
- Differential Impact of CD43 and CD28 on T-Cell Differentiation Depending on the Order of Engagement with the TCR.International journal of molecular sciences · 2024Article
- Peak Scores Significantly Depend on the Relationships between Contextual Signals in ChIP-Seq Peaks.International journal of molecular sciences · 2024Article
- Quantification of absolute transcription factor binding affinities in the native chromatin context using BANC-seq.Nature biotechnology · 2023Article
- MiREx: mRNA levels prediction from gene sequence and miRNA target knowledge.BMC bioinformatics · 2023Article
- Preliminary Study of Whole-Genome Bisulfite Sequencing and Transcriptome Sequencing in VHL Disease-Associated ccRCC.Molecular diagnosis & therapy · 2023Article
- Molecular mechanisms of multi-omic regulation in breast cancer.Frontiers in oncology · 2023Review
- Expression regulation of genes is linked to their CpG density distributions around transcription start sites.Life science alliance · 2022Article
- Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis.Molecules (Basel, Switzerland) · 2022Article
- Article
- Mutual dependency between lncRNA LETN and protein NPM1 in controlling the nucleolar structure and functions sustaining cell proliferation.Cell research · 2021Article
- Lipidome alterations in human prefrontal cortex during development, aging, and cognitive disorders.Molecular psychiatry · 2020Article
- Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks.PLoS computational biology · 2020Article
- Hepatic transcriptome of the freeze-tolerant Cope's gray treefrog, Dryophytes chrysoscelis: responses to cold acclimation and freezing.BMC genomics · 2020Article
- A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases.International journal of environmental research and public health · 2020Article
54 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TF-binding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner.
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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.