» Articles » PMID: 28427142

IRNA-PseU: Identifying RNA Pseudouridine Sites

Overview
Publisher Cell Press
Date 2017 Apr 22
PMID 28427142
Citations 115
Authors
Affiliations
Soon will be listed here.
Abstract

As the most abundant RNA modification, pseudouridine plays important roles in many biological processes. Occurring at the uridine site and catalyzed by pseudouridine synthase, the modification has been observed in nearly all kinds of RNA, including transfer RNA, messenger RNA, small nuclear or nucleolar RNA, and ribosomal RNA. Accordingly, its importance to basic research and drug development is self-evident. Despite some experimental technologies have been developed to detect the pseudouridine sites, they are both time-consuming and expensive. Facing the explosive growth of RNA sequences in the postgenomic age, we are challenged to address the problem by computational approaches: For an uncharacterized RNA sequence, can we predict which of its uridine sites can be modified as pseudouridine and which ones cannot? Here a predictor called "iRNA-PseU" was proposed by incorporating the chemical properties of nucleotides and their occurrence frequency density distributions into the general form of pseudo nucleotide composition (PseKNC). It has been demonstrated via the rigorous jackknife test, independent dataset test, and practical genome-wide analysis that the proposed predictor remarkably outperforms its counterpart. For the convenience of most experimental scientists, the web-server for iRNA-PseU was established at http://lin.uestc.edu.cn/server/iRNA-PseU, by which users can easily get their desired results without the need to go through the mathematical details.

Citing Articles

Implications of RNA pseudouridylation for cancer biology and therapeutics: a narrative review.

Ding H, Liu N, Wang Y, Adam S, Jin J, Feng W J Transl Med. 2024; 22(1):906.

PMID: 39375731 PMC: 11457414. DOI: 10.1186/s12967-024-05687-6.


m7GRegpred: substrate prediction of N7-methylguanosine (m7G) writers and readers based on sequencing features.

Zheng Y, Li H, Lin S Front Genet. 2024; 15:1469011.

PMID: 39262420 PMC: 11387174. DOI: 10.3389/fgene.2024.1469011.


Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.

Bortoletto E, Rosani U Genes (Basel). 2024; 15(8).

PMID: 39202357 PMC: 11353476. DOI: 10.3390/genes15080996.


STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention.

Yi M, Zhou F, Deng Y Front Genet. 2024; 15:1408688.

PMID: 38873109 PMC: 11169723. DOI: 10.3389/fgene.2024.1408688.


PseUpred-ELPSO Is an Ensemble Learning Predictor with Particle Swarm Optimizer for Improving the Prediction of RNA Pseudouridine Sites.

Wang X, Li P, Wang R, Gao X Biology (Basel). 2024; 13(4).

PMID: 38666860 PMC: 11048358. DOI: 10.3390/biology13040248.


References
1.
Liu Z, Xiao X, Yu D, Jia J, Qiu W, Chou K . pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. Anal Biochem. 2016; 497:60-7. DOI: 10.1016/j.ab.2015.12.017. View

2.
Kumar R, Srivastava A, Kumari B, Kumar M . Prediction of β-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine. J Theor Biol. 2014; 365:96-103. DOI: 10.1016/j.jtbi.2014.10.008. View

3.
Chou K . Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol. 2010; 273(1):236-47. PMC: 7125570. DOI: 10.1016/j.jtbi.2010.12.024. View

4.
Liu B, Fang L, Liu F, Wang X, Chen J, Chou K . Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS One. 2015; 10(3):e0121501. PMC: 4378912. DOI: 10.1371/journal.pone.0121501. View

5.
Chou K . A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J Biol Chem. 1993; 268(23):16938-48. View