Literature DB >> 26552091

A New Method to Predict RNA Secondary Structure Based on RNA Folding Simulation.

Yuanning Liu, Qi Zhao, Hao Zhang, Rui Xu, Yang Li, Liyan Wei.   

Abstract

RNA plays an important role in various biological processes; hence, it is essential when determining the functions of RNA to research its secondary structures. So far, the accuracy of RNA secondary structure prediction remains an area in need of improvement. This paper presents a novel method for predicting RNA secondary structure based on an RNA folding simulation model. This model assumes that the process of RNA folding from the random coil state to full structure is staged and in every stage of folding, the final state of an RNA is determined by the optimal combination of helical regions, which are urgently essential to dynamics of RNA formation. This paper proposes the First Large Free Energy Difference (FLED) in order to find the helical regions most urgently needed for optimal final state formation among all the possible helical regions. Tests on the datasets with known structures from public databases demonstrate that our method can outperform other current RNA secondary structure prediction methods in terms of prediction accuracy.

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Year:  2015        PMID: 26552091     DOI: 10.1109/TCBB.2015.2496347

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  3 in total

1.  A novel method for the identification of conserved structural patterns in RNA: From small scale to high-throughput applications.

Authors:  Marco Pietrosanto; Eugenio Mattei; Manuela Helmer-Citterich; Fabrizio Ferrè
Journal:  Nucleic Acids Res       Date:  2016-08-31       Impact factor: 16.971

2.  FledFold: A Novel Software for RNA Secondary Structure Prediction.

Authors:  Qi Zhao; Yuanning Liu; Yunna Duan; Tao Dai; Rui Xu; Hao Guo; Daiming Fan; Yongzhan Nie; Hao Zhang
Journal:  Lett Org Chem       Date:  2017-06       Impact factor: 0.867

3.  Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models.

Authors:  Qi Zhao; Qian Mao; Zheng Zhao; Tongyi Dou; Zhiguo Wang; Xiaoyu Cui; Yuanning Liu; Xiaoya Fan
Journal:  BMC Genomics       Date:  2018-11-26       Impact factor: 3.969

  3 in total

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