ArticleMolecular therapy. Nucleic acids2017
iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC.
Article in Molecular therapy. Nucleic acids, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.
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86 citing papers in PubMed.
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- Fusion_f5C-Pred: a dual-branch feature fusion framework for 5-formylcytosine modification sites prediction.BMC genomics · 2025Article
- YModPred: an interpretable prediction method for multi-type RNA modification sites in S. cerevisiae based on deep learning.BMC biology · 2025Article
- Detection, molecular function and mechanisms of m5C in cancer.Clinical and translational medicine · 2025Review
- Domain-knowledge enabled ensemble learning of 5-formylcytosine (f5C) modification sites.Computational and structural biotechnology journal · 2024Article
- Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.Biology · 2024Review
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- m7GRegpred: substrate prediction of N7-methylguanosine (m7G) writers and readers based on sequencing features.Frontiers in genetics · 2024Article
- A CNN based m5c RNA methylation predictor.Scientific reports · 2023Article
- Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues.Bioinformatics (Oxford, England) · 2023Article
- Review
- Novel insight into RNA modifications in tumor immunity: Promising targets to prevent tumor immune escape.Innovation (Cambridge (Mass.)) · 2023Review
- A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.International journal of molecular sciences · 2022Review
- Review
- DLm6Am: A Deep-Learning-Based Tool for Identifying N6,2'-O-Dimethyladenosine Sites in RNA Sequences.International journal of molecular sciences · 2022Article
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- Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties.International journal of molecular sciences · 2022Article
26 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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Abstract
There are many different types of RNA modifications, which are essential for numerous biological processes. Knowledge about the occurrence sites of RNA modifications in its sequence is a key for in-depth understanding of their biological functions and mechanism. Unfortunately, it is both time-consuming and laborious to determine these sites purely by experiments alone. Although some computational methods were developed in this regard, each one could only be used to deal with some type of modification individually. To our knowledge, no method has thus far been developed that can identify the occurrence sites for several different types of RNA modifications with one seamless package or platform. To address such a challenge, a novel platform called "iRNA-PseColl" has been developed. It was formed by incorporating both the individual and collective features of the sequence elements into the general pseudo K-tuple nucleotide composition (PseKNC) of RNA via the chemicophysical properties and density distribution of its constituent nucleotides. Rigorous cross-validations have indicated that the anticipated success rates achieved by the proposed platform are quite high. To maximize the convenience for most experimental biologists, the platform's web-server has been provided at http://lin.uestc.edu.cn/server/iRNA-PseColl along with a step-by-step user guide that will allow users to easily achieve their desired results without the need to go through the mathematical details involved in this paper.
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