AIMC Topic: Nucleosomes
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An integrated machine-learning model to predict nucleosome architecture.
Nucleic acids research
Sep 23, 2024
We demonstrate that nucleosomes placed in the gene body can be accurately located from signal decay theory assuming two emitters located at the beginning and at the end of genes. These generated wave signals can be in phase (leading to well defined n...
DNAcycP: a deep learning tool for DNA cyclizability prediction.
Nucleic acids research
Apr 8, 2022
DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simult...
[Identification of nucleosome positioning using support vector machine method based on comprehensive DNA sequence feature].
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Jun 25, 2020
In this article, based on z-curve theory and position weight matrix (PWM), a model for nucleosome sequences was constructed. Nucleosome sequence dataset was transformed into three-dimensional coordinates, PWM of the nucleosome sequences was calculate...
HMMRATAC: a Hidden Markov ModeleR for ATAC-seq.
Nucleic acids research
Sep 19, 2019
ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragm...
LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks.
Bioinformatics (Oxford, England)
May 15, 2018
MOTIVATION: Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite a multitude of nucleosome positioning resources available on line including experimental datasets o...
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