ArticleNucleic acids research2024
In silico design of DNA sequences for in vivo nucleosome positioning.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Positional interpretation of cis-regulatory code and nucleosome organization with deep learning models.Nature communications · 2026Article
- An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.Bioinformatics (Oxford, England) · 2026Article
- Nucleosome spacing across cell types, diseases, and ages.Nucleic acids research · 2026Review
- Deciphering the 3D genome organization across species from Hi-C data.Nucleic acids research · 2026Article
- Native nucleosome-positioning elements as alternatives to the 601 sequence for nucleosome repositioning studies.Nucleic acids research · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
The computational design of synthetic DNA sequences with designer in vivo properties is gaining traction in the field of synthetic genomics. We propose here a computational method which combines a kinetic Monte Carlo framework with a deep mutational screening based on deep learning predictions. We apply our method to build regular nucleosome arrays with tailored nucleosomal repeat lengths (NRL) in yeast. Our design was validated in vivo by successfully engineering and integrating thousands of kilobases long tandem arrays of computationally optimized sequences which could accommodate NRLs much larger than the yeast natural NRL (namely 197 and 237 bp, compared to the natural NRL of ∼165 bp). RNA-seq results show that transcription of the arrays can occur but is not driven by the NRL. The computational method proposed here delineates the key sequence rules for nucleosome positioning in yeast and should be easily applicable to other sequence properties and other genomes.
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Registered trials
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