ArticleBioinformatics (Oxford, England)2017
Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- HDGS-Net: nucleosome occupancy prediction based on a hybrid dilated gated separable convolutional neural network.BMC genomics · 2026Article
- Interpretable deep residual network uncovers nucleosome positioning and associated features.Nucleic acids research · 2024Article
- m5c-iEnsem: 5-methylcytosine sites identification through ensemble models.Bioinformatics (Oxford, England) · 2022Article
- Article
- EpitopeVec: linear epitope prediction using deep protein sequence embeddings.Bioinformatics (Oxford, England) · 2021Article
- Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms.BMC bioinformatics · 2021Article
- Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX).Scientific reports · 2019Article
- iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition.Molecular genetics and genomics : MGG · 2019Article
- Prediction of Gene Expression Patterns With Generalized Linear Regression Model.Frontiers in genetics · 2019Article
- LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks.Bioinformatics (Oxford, England) · 2018Article
- The 1-Particle-per-k-Nucleotides (1PkN) Elastic Network Model of DNA Dynamics with Sequence-Dependent Geometry.Frontiers in physiology · 2017Article
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Abstract
motivationNucleosome positioning plays important roles in many eukaryotic intranuclear processes, such as transcriptional regulation and chromatin structure formation. The investigations of nucleosome positioning rules provide a deeper understanding of these intracellular processes.
resultsNucleosome positioning prediction was performed using a model consisting of three types of variables characterizing a DNA sequence-the number of five-nucleotide sequences, the number of three-nucleotide combinations in one period of a helix, and mono- and di-nucleotide distributions in DNA fragments. Using recently proposed stringent benchmark datasets with low biases for Saccharomyces cerevisiae, Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster, the present model was shown to have a better prediction performance than the recently proposed predictors. This model was able to display the common and organism-dependent factors that affect nucleosome forming and inhibiting sequences as well. Therefore, the predictors developed here can accurately predict nucleosome positioning and help determine the key factors influencing this process. CONTACT: awa@hiroshima-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.
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