Related Experiment Video
Updated: Mar 18, 2026
06:02
A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
Published on: October 28, 2025
646
ChIP-seq accurately predicts tissue-specific activity of enhancers
Axel Visel1, Matthew J Blow, Zirong Li
1Genomics Division, MS 84-171, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
Nature
|February 13, 2009
Summary
Identifying gene regulatory sequences, like enhancers, is crucial for understanding gene expression. Mapping p300 protein binding sites accurately identifies active enhancers and their functions in vivo.
Area of Science:
- Genomics
- Molecular Biology
- Developmental Biology
Background:
- Identifying regulatory DNA sequences, particularly transcriptional enhancers, is a key challenge in human genome research.
- Enhancers control gene expression but are difficult to locate within the non-coding genome.
- Evolutionary conservation aids enhancer discovery but doesn't predict their activity in vivo.
Purpose of the Study:
- To develop and validate a method for identifying active transcriptional enhancers and their functions.
- To map in vivo binding sites of the enhancer-associated protein p300 in mouse embryonic tissues.
- To assess the accuracy of p300 binding site mapping for predicting enhancer activity.
Main Methods:
- Chromatin immunoprecipitation (ChIP) targeting the p300 protein.
- Massively parallel sequencing (MPS) of immunoprecipitated DNA.
- Transgenic mouse assays to validate enhancer activity of identified sequences.
Main Results:
- Several thousand in vivo p300 binding sites were mapped in mouse embryonic forebrain, midbrain, and limb.
- 86 tested sequences showed reproducible enhancer activity in transgenic assays.
- Enhancer activity was consistently observed in tissues predicted by p300 binding.
Conclusions:
- In vivo mapping of p300 binding is a highly accurate method for identifying enhancers.
- This approach effectively predicts the tissue-specific activity of enhancers.
- Generated datasets are valuable for studying tissue-specific enhancers in human biology and disease.
