Prediction of RNA secondary structure based on helical regions distribution

被引:13
|
作者
Li, WJ [1 ]
Wu, JJ [1 ]
机构
[1] Inst Basic Med Sci, Lab Bioinformat Engn, Beijing 100850, Peoples R China
关键词
D O I
10.1093/bioinformatics/14.8.700
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: RNAs play an important role in many biological processes and knowing their structure is important in understanding their function, Due to difficulties in the experimental determination of RNA secondary structure, the methods of theoretical prediction for known sequences are often used. Although many different algorithms for such predictions have been developed this problem has not yet been solved. It is thus necessary to develop new methods for predicting RNA secondary, structure. The most-used at present is Zuker's algorithm which can be used to determine the minimum free energy secondary structure. However many RNA secondary structures verified by experiments are not consistent with the minimum free energy secondary structures. In older to solve this problem, a method used to search a group of secondary structures whose free energy is close to the global minimum free energy was developed by Zuker in 1989. When considering a group of secondary structures, if there is no experimental data, we cannot tell which one is better than the others. This case also occurs in combinatorial and heuristic methods. These two kinds of methods have several weaknesses. Here we show how the central limit theorem can be used to solve these problems. Results: An algorithm for predicting RNA secondary structure based on helical regions distribution is presented, which can be used to find the most probable secondary, structure for a given RNA sequence. Ir consists of three steps. First, list all possible helical regions. Second, according to central limit theorem, estimate the occurrence probability of every helical region based on the Monte Carlo simulation. Third, acid the helical region with the biggest probability to the current structure and eliminate the helical regions incompatible with the current structure. The above processes can be repeated until no lore helical regions can be added. Take the current structure as the final RNA secondary structure. In order to demonstrate the confidence of the program, a test on three RNA sequences: tRNA(Phe) Pre-tRNA(Tyr); and Tetrahymena ribosomal RNA intervening sequence, is performed.
引用
收藏
页码:700 / 706
页数:7
相关论文
共 50 条
  • [31] IRfold: An RNA Secondary Structure Prediction Approach
    Hurst, David
    Iliopoulos, Costas S.
    Lim, Zara
    Moraru, Ionut
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, PT I, AIAI 2024, 2024, 711 : 131 - 144
  • [32] IMPROVED METHOD FOR RNA SECONDARY STRUCTURE PREDICTION'
    Xue Mei YUAN Yu LUO Lu Hua LAI Xiao Jie XU Institute of Physical Chemistry
    ChineseChemicalLetters, 1993, (08) : 737 - 740
  • [33] Prediction and differential analysis of RNA secondary structure
    Yu, Bo
    Lu, Yao
    Zhang, Qiangfeng Cliff
    Hou, Lin
    QUANTITATIVE BIOLOGY, 2020, 8 (02) : 109 - 118
  • [34] Statistical parser for RNA secondary structure prediction
    Dang, Y
    Zhang, YL
    Zhang, DM
    PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, 2005, : 3399 - 3403
  • [35] A Review on RNA Secondary Structure Prediction Algorithms
    Oluoch, Ian Kings
    Akalin, Abdullah
    Vural, Yilmaz
    Canbay, Yavuz
    2018 INTERNATIONAL CONGRESS ON BIG DATA, DEEP LEARNING AND FIGHTING CYBER TERRORISM (IBIGDELFT), 2018, : 18 - 23
  • [36] Problems on RNA secondary structure prediction and design
    Condon, A
    AUTOMATA, LANGUAGES AND PROGRAMMING, PROCEEDINGS, 2003, 2719 : 22 - 32
  • [37] FPGA Accelerator for RNA Secondary Structure Prediction
    Diaz-Perez, Arturo
    Garcia-Martinez, Mario A.
    PROCEEDINGS OF THE 2009 12TH EUROMICRO CONFERENCE ON DIGITAL SYSTEM DESIGN, ARCHITECTURES, METHODS AND TOOLS, 2009, : 667 - +
  • [38] Novel Architecture for RNA Secondary Structure Prediction
    Garcia-Martinez, Mario A.
    Posada-Gomez, Ruben
    Alor-Hernandez, Giner
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING, PROCEEDINGS, 2009, 5788 : 416 - 423
  • [39] A Matrix Algorithm for RNA Secondary Structure Prediction
    Krishnan, S. P. T.
    Khurshid, Mushfique Junayed
    Veeravalli, Bharadwaj
    PATTERN RECOGNITION IN BIOINFORMATICS, 2010, 6282 : 337 - +
  • [40] An evolving automaton for RNA secondary structure prediction
    Del Carpio, Carlos A.
    Ismael, Mohamed
    Ichiishi, Eichiro
    Koyama, Michihisa
    Kubo, Momoji
    Miyamoto, Akira
    2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, 2006, : 2226 - +