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
  • [21] RNA secondary structure prediction based on forest representation and genetic algorithm
    Zhang, Taotao
    Guo, Maozu
    Zou, Quan
    ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 4, PROCEEDINGS, 2007, : 370 - +
  • [22] RNA secondary structure prediction with simple pseudoknots based on dynamic programming
    Namsrai, Oyun-Erdene
    Jung, Kwang Su
    Kim, Sunshin
    Ryu, Kenn Ho
    COMPUTATIONAL INTELLIGENCE AND BIOINFORMATICS, PT 3, PROCEEDINGS, 2006, 4115 : 303 - 311
  • [23] Analysis of energy-based algorithms for RNA secondary structure prediction
    Monir Hajiaghayi
    Anne Condon
    Holger H Hoos
    BMC Bioinformatics, 13
  • [24] The research of RNA secondary structure prediction based on Extended NSSEL labels
    He, Jingyuan
    He, Zhongshi
    Zou, Dongsheng
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 5396 - 5400
  • [25] RNA secondary structure prediction based on free energy and phylogenetic analysis
    Juan, V
    Wilson, C
    JOURNAL OF MOLECULAR BIOLOGY, 1999, 289 (04) : 935 - 947
  • [26] ATTfold: RNA Secondary Structure Prediction With Pseudoknots Based on Attention Mechanism
    Wang, Yili
    Liu, Yuanning
    Wang, Shuo
    Liu, Zhen
    Gao, Yubing
    Zhang, Hao
    Dong, Liyan
    FRONTIERS IN GENETICS, 2020, 11
  • [27] Constraint-Based Strategy for Pairwise RNA Secondary Structure Prediction
    Perriquet, Olivier
    Barahona, Pedro
    PROGRESS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2009, 5816 : 86 - 97
  • [28] Evolution Strategy based Evolutionary Algorithm for RNA Secondary Structure Prediction
    Yu, Zhengliang
    Li, Fan
    Zhang, Kai
    PROCEEDINGS OF 2022 THE 6TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND SOFT COMPUTING, ICMLSC 20222, 2022, : 56 - 60
  • [29] A Hopfield Neural Network based algorithm for RNA secondary structure prediction
    Liu, Qi
    Ye, Xiuzi
    Zhang, Yin
    FIRST INTERNATIONAL MULTI-SYMPOSIUMS ON COMPUTER AND COMPUTATIONAL SCIENCES (IMSCCS 2006), PROCEEDINGS, VOL 1, 2006, : 10 - +
  • [30] WELL-DETERMINED REGIONS IN RNA SECONDARY STRUCTURE PREDICTION - ANALYSIS OF SMALL-SUBUNIT RIBOSOMAL-RNA
    ZUKER, M
    JACOBSON, AB
    NUCLEIC ACIDS RESEARCH, 1995, 23 (14) : 2791 - 2798