Prediction of RNA secondary structure based on helical regions distribution.
BACKGROUND
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 order 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. It 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, add 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 more 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: tRNAPhe, Pre-tRNATyr, and Tetrahymena ribosomal RNA intervening sequence, is performed.


BACKGROUND
The program is written in Turbo Pascal 7.0. The source code is available upon request.


BACKGROUND
Wujj@nic.bmi.ac.cn or Liwj@mail.bmi.ac.cn
WuJu
L
L
Laboratory of Bioinformation Engineering, Institute of Basic Medical Sciences, P.O. Box 130(3), Beijing 100850, China.
JiaJin
W
W
D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D008969:Molecular Sequence Data; D009010:Monte Carlo Method; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry*; D012322:RNA Precursors / Q000737:chemistry; D016053:RNA, Protozoan / Q000737:chemistry; D012335:RNA, Ribosomal / Q000737:chemistry; D012360:RNA, Transfer, Phe / Q000737:chemistry; D012365:RNA, Transfer, Tyr / Q000737:chemistry; D013768:Tetrahymena / Q000235:genetics
D012322:RNA Precursors; D016053:RNA, Protozoan; D012335:RNA, Ribosomal; D012360:RNA, Transfer, Phe; D012365:RNA, Transfer, Tyr; D012313:RNA