ArticleProceedings of the National Academy of Sciences of the United States of America2009
ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 196 papers.
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Who cites it
196 citing papers in PubMed, 359 citations in OpenAlex.
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- Modelling transcription with explainable AI uncovers context-specific epigenetic gene regulation at promoters and gene bodies.PLoS genetics · 2025Article
- Tfcp2l1 as a central integrator of hypoxia, dedifferentiation, and tumor progression.Journal of experimental & clinical cancer research : CR · 2025Review
- Integrative ATAC-Seq and RNA-Seq Analysis Reveals Key Transcription Factors Mediating Low Salinity Adaptation in Penaeid Shrimp.International journal of molecular sciences · 2025Article
- Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.PLoS computational biology · 2025Article
- Causal modeling of gene effects from regulators to programs to traits: integration of genetic associations and Perturb-seq.bioRxiv : the preprint server for biology · 2025Article
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- KARR-seq reveals cellular higher-order RNA structures and RNA-RNA interactions.Nature biotechnology · 2024Article
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- MiREx: mRNA levels prediction from gene sequence and miRNA target knowledge.BMC bioinformatics · 2023Article
- Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data.NPJ systems biology and applications · 2023Review
- CXCR2 expression during melanoma tumorigenesis controls transcriptional programs that facilitate tumor growth.Molecular cancer · 2023Article
- Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets.Genome research · 2023Article
- Transcriptional kinetic synergy: A complex landscape revealed by integrating modeling and synthetic biology.Cell systems · 2023Article
- A computational pipeline to learn gene expression predictive models from epigenetic information at enhancers or promoters.STAR protocols · 2023Article
- The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.Genome biology · 2023Article
- The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data.Bioinformatics (Oxford, England) · 2023Article
- Mapping Transcription Regulation with Run-on and Sequencing Data Using the Web-Based tfTarget Gateway.Methods in molecular biology (Clifton, N.J.) · 2023Article
136 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Next-generation sequencing has greatly increased the scope and the resolution of transcriptional regulation study. RNA sequencing (RNA-Seq) and ChIP-Seq experiments are now generating comprehensive data on transcript abundance and on regulator-DNA interactions. We propose an approach for an integrated analysis of these data based on feature extraction of ChIP-Seq signals, principal component analysis, and regression-based component selection. Compared with traditional methods, our approach not only offers higher power in predicting gene expression from ChIP-Seq data but also provides a way to capture cooperation among regulators. In mouse embryonic stem cells (ESCs), we find that a remarkably high proportion of variation in gene expression (65%) can be explained by the binding signals of 12 transcription factors (TFs). Two groups of TFs are identified. Whereas the first group (E2f1, Myc, Mycn, and Zfx) act as activators in general, the second group (Oct4, Nanog, Sox2, Smad1, Stat3, Tcfcp2l1, and Esrrb) may serve as either activator or repressor depending on the target. The two groups of TFs cooperate tightly to activate genes that are differentially up-regulated in ESCs. In the absence of binding by the first group, the binding of the second group is associated with genes that are repressed in ESCs and derepressed upon early differentiation.
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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.