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- W4221036171 abstract "The role of histone modifications in transcription remains incompletely understood. Here, we examine the relationship between histone modifications and transcription using experimental perturbations combined with sensitive machine-learning tools. Transcription predicted the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. Blocking transcription rapidly removed two punctate marks, H3K4me3 and H3K27ac, from chromatin indicating that transcription is required for active histone modifications. Transcription was also required for maintenance of H3K27me3, consistent with a role for RNA in recruiting PRC2. A subset of DNase-I-hypersensitive sites were refractory to prediction, precluding models where transcription initiates pervasively at any open chromatin. Our results, in combination with past literature, support a model in which active histone modifications serve a supportive, rather than an essential regulatory, role in transcription. A machine-learning tool can predict the distribution of histone post-translational modifications using nascent transcription data. Inhibiting transcription impacts H3K4me3, H3K27ac and H3K27me3 dynamics." @default.
- W4221036171 created "2022-04-03" @default.
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- W4221036171 date "2022-03-01" @default.
- W4221036171 modified "2023-10-01" @default.
- W4221036171 title "Prediction of histone post-translational modification patterns based on nascent transcription data" @default.
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- W4221036171 doi "https://doi.org/10.1038/s41588-022-01026-x" @default.
- W4221036171 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35273399" @default.