ArticleNucleic acids research2019
Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences.
Article in Nucleic acids research, 2019. 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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8 citing papers in PubMed.
- SuperARC: a test for artificial superintelligence based on compressed modelling, recursive prediction and problem complexity.Nature communications · 2026Article
- On the Empirical Agreement Between Compression and Program-Execution Approaches to Algorithmic Complexity: A Controlled Study Using BDM.Entropy (Basel, Switzerland) · 2026Article
- Article
- Deep Learning Based Models for CRISPR/Cas Off-Target Prediction.Small methods · 2025Review
- Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs.Nucleic acids research · 2024Article
- piCRISPR: Physically informed deep learning models for CRISPR/Cas9 off-target cleavage prediction.Artificial intelligence in the life sciences · 2023Article
- Comprehensive computational analysis of epigenetic descriptors affecting CRISPR-Cas9 off-target activity.BMC genomics · 2022Article
- A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions.Entropy (Basel, Switzerland) · 2020Review
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2 authors.
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Abstract
We introduce and study a set of training-free methods of an information-theoretic and algorithmic complexity nature that we apply to DNA sequences to identify their potential to identify nucleosomal binding sites. We test the measures on well-studied genomic sequences of different sizes drawn from different sources. The measures reveal the known in vivo versus in vitro predictive discrepancies and uncover their potential to pinpoint high and low nucleosome occupancy. We explore different possible signals within and beyond the nucleosome length and find that the complexity indices are informative of nucleosome occupancy. We found that, while it is clear that the gold standard Kaplan model is driven by GC content (by design) and by k-mer training; for high occupancy, entropy and complexity-based scores are also informative and can complement the Kaplan model.
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