ArticleBioinformatics (Oxford, England)2018
LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 46 citations in OpenAlex.
- 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
- The importance of DNA sequence for nucleosome positioning in transcriptional regulation.Life science alliance · 2024Article
- Comprehensive computational analysis of epigenetic descriptors affecting CRISPR-Cas9 off-target activity.BMC genomics · 2022Article
- DeepNup: Prediction of Nucleosome Positioning from DNA Sequences Using Deep Neural Network.Genes · 2022Article
- Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data.Genomics, proteomics & bioinformatics · 2022Article
- Galaxy Dnpatterntools for Computational Analysis of Nucleosome Positioning Sequence Patterns.International journal of molecular sciences · 2022Article
- DNAcycP: a deep learning tool for DNA cyclizability prediction.Nucleic acids research · 2022Article
- Genomics enters the deep learning era.PeerJ · 2022Review
- Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms.BMC bioinformatics · 2021Article
- CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification.BMC bioinformatics · 2020Article
- BITS2019: the sixteenth annual meeting of the Italian society of bioinformatics.BMC bioinformatics · 2020Article
- [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 · 2020Article
- Prediction of sgRNA on-target activity in bacteria by deep learning.BMC bioinformatics · 2019Article
- Article
- NucMap: a database of genome-wide nucleosome positioning map across species.Nucleic acids research · 2019Article
- Deep learning for DNase I hypersensitive sites identification.BMC genomics · 2018Article
- Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.Omics : a journal of integrative biology · 2018Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 of genome-wide nucleosome occupancy profiles and computational tools to the analysis on these data, the complex language of eukaryotic Nucleosome positioning remains incompletely understood. Results: Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) to understand nucleosome positioning. We combined Inception-like networks with a gating mechanism for the response of multiple patterns and long term association in DNA sequences. We developed the open-source package LeNup based on the CNN to predict nucleosome positioning in Homo sapiens, Caenorhabditis elegans, Drosophila melanogaster as well as Saccharomyces cerevisiae genomes. We trained LeNup on four benchmark datasets. LeNup achieved greater predictive accuracy than previously published methods. Availability and implementation: LeNup is freely available as Python and Lua script source code under a BSD style license from https://github.com/biomedBit/LeNup. Contact: jhzhang@bit.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.