ArticleBriefings in bioinformatics2024
The CUT&RUN greenlist: genomic regions of consistent noise are effective normalizing factors for quantitative epigenome mapping.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- PVT1 lincRNA signals an androgen-dependent transcriptional activation program of oncogenes in prostate cancer cells.International journal of cancer · 2026Article
- Defective plasticity in dermatomyositis patients muscle stem cells is associated with sustained intrinsic inflammatory signaling and disruption of the histone H3.3 chromatin loading pathway.NAR molecular medicine · 2026Article
- Fine-tuning BACH2 dosage balances stemness and effector function to enhance antitumor T cell therapy.Nature immunology · 2026Article
- NSD3 stabilizes nuclear compartmentalization and promotes megabase-scale chromatin interactions.bioRxiv : the preprint server for biology · 2026Article
- Oncogenic KRAS/ERK/JUNB signaling suppresses differentiation regulator GATA6 in pancreatic cancer.The Journal of clinical investigation · 2026Article
- HOXA10-TWIST2 antagonism drives partial epithelial-to-mesenchymal transition for embryo implantation.Cell death discovery · 2025Article
- Review
- Elevated EGR1 binding at enhancers in excitatory neurons correlates with neuronal subtype-specific epigenetic regulation.BMC biology · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is a recent development for epigenome mapping, but its unique methodology can hamper proper quantitative analyses. As traditional normalization approaches have been shown to be inaccurate, we sought to determine endogenous normalization factors based on the human genome regions of constant nonspecific signal. This constancy was determined by applying Shannon's information entropy, and the set of normalizer regions, which we named the 'Greenlist', was extensively validated using publicly available datasets. We demonstrate here that the greenlist normalization outperforms the current top standards, and remains consistent across different experimental setups, cell lines and antibodies; the approach can even be applied to different species or to CUT&Tag. Requiring no additional experimental steps and no added cost, this approach can be universally applied to CUT&RUN experiments to greatly minimize the interference of technical variation over the biological epigenome changes of interest.
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