ArticleComputational and structural biotechnology journal2020
MU-PseUDeep: A deep learning method for prediction of pseudouridine sites.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- RSCNN-PseU: random searching-based convolutional neural network model for identifying RNA pseudouridine.Briefings in bioinformatics · 2025Article
- Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.PloS one · 2025Article
- Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.Briefings in bioinformatics · 2024Article
- Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy.Frontiers in microbiology · 2023Article
- Predicting Pseudouridine Sites with Porpoise.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- Identification of piRNA disease associations using deep learning.Computational and structural biotechnology journal · 2022Article
- Interferon inducible pseudouridine modification in human mRNA by quantitative nanopore profiling.Genome biology · 2021Article
- Porpoise: a new approach for accurate prediction of RNA pseudouridine sites.Briefings in bioinformatics · 2021Article
- Machine learning applications in RNA modification sites prediction.Computational and structural biotechnology journal · 2021Review
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Authors and funding
5 authors.
Funding
Abstract
Pseudouridine synthase binds to uridine sites and catalyzes the conversion of uridine to pseudouridine (Ψ). This binding takes place in a specific context and in the conformation of nucleotides. Most machine-learning methods for Ψ site classification use nucleotide frequency as a feature, which may not fully depict the relevant conformation around a Ψ site. Using the power of deep learning and raw sequence, as well as secondary structure features, our tool MU-PseUDeep is designed to capture both the sequence and secondary structure context, which inputs the raw RNA sequence and the predicted secondary structure to two sets of convolutional neural networks. It has shown considerable improvement in Ψ site prediction over existing tools, XG-PseU, PseUI, and iRNA-PseU for both balanced and imbalanced datasets. To the best of our knowledge, this is the most accurate tool for Ψ site prediction. We also used MU-PseUDeep to scan the human transcriptome, which shows that the genes with predicted Ψ sites are enriched in nucleotide and protein binding, as well as in neurodegeneration pathways. The tool is open source, available at https://github.com/smk5g5/MU-PseUDeep.
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Registered trials
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