基于遗传模拟退火混合算法的RNA二级结构预测
2016-07-09黄进宋余庆凌青华
黄进 宋余庆 凌青华
摘要:RNA二级结构预测是生物信息学中非常重要的内容。RNA二级结构的准确预测有助于生物研究者了解RNA分子在生物体内中所起到的重要作用。近年来,基于最小自由能模型的启发式算法常被运用于预测RNA二级结构。遗传算法和模拟退火算法是常见的启发式算法,将遗传算法中的遗传变异机制以及模拟退火的退火机制相结合,形成一种新的算法,以茎区作为种群中的个体进行交叉变异操作,将所得到的结果进行退火操作,从而得到最优解。该算法结合了遗传算法和模拟退火算法的优势,实验结果表明,该方法预测结果具有较高的精度。
关键词:RNA二级结构;最小自由能;茎区;遗传算法;模拟退火算法
DOIDOI:10.11907/rjdk.161267
中图分类号:TP312文献标识码:A文章编号:1672-7800(2016)006-0027-04
参考文献:
[1]邹权,郭茂祖,张涛涛. RNA二级结构预测方法综述[J]. 电子学报,2008,36(2): 331-337.
[2]TINOCO I,UHLENBECK O C, LEVINE M D. Estimation of secondary structure in ribonucleic acids[J]. Nature, 1971, 230(5293): 362-367.
[3]RUTH NUSSINOV,GEORGE PIECZENIK,JERROLD R GRIGGS,et al.Algorithms for loop matchings[J].SIAM Journal on Applied Mathematics,1978,35(1):68-82.
[4]ZUKER M, STIEGLER P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information[J]. Nucleic acids research,1981,9(1):133-148.
[5]胡桂武,彭宏.基于免疫粒子群集成的RNA二级结构预测算法.计算机工程与应用,2007,43(3):26-29.
[6]刘琦,张引,叶修梓. 基于离散Hopfield网络求解极大独立集的茎区选择算法以及在RNA二级结构预测中的应用[J]. 计算机学报,2008,31(1): 51-58.
[7]DAVID H MATHEWS, JEFFRET SABINA, MICHAEL ZUKER, et al . Expand sequence dependence of thermodynamic parameters improves prediction of RNA secondary structured [J]. Journal of Molecular Biology, 1999, 288(5):911-940.
[8]ELENA RIVAS, SEAN R EDDY.A dynamic programming algorithm for RNA structure prediction including pseudoknots[J]. Journal of Molecular Biology, 1999, 285 (5):2053-2068.
[9]KAY C, WIESE E, GLEN. A permutation-based genetic algorithm for the RNA folding problem:a critical look at selection strategies,crossover operators and representation issues[J].BioSystems, 2003, 72(1):29-41.
[10]陈涛.基于茎区的RNA 二级结构预测方法研究[D].长沙:湖南大学,2011.
[11]REN J, RASTEGARI B, CONDON A, et al. Hotknots heuristic prediction of RNA secondary structures including pseudoknots[J].RNA, 2005, 11(10): 1494-1504.
[12]DOUGLAS H TURNER.Approximation algorithm of the RNA pseudoknotted structure prediction based on MFE[C]. IEEE International Conference on Information and Automation,2013:1044-1048.
[13]HYONEMOTO,KASAI. A semi_supervised learning approach for RNA secondary structure prediction[J].Computational Biology and Chemistry,2015(57):72-79.
[14]杨赫. RNA二级结构中假结的预测研究[D]. 长春:吉林大学,2013.
[15]RICHARD BEAL,DONALD ADJEROH.Efficient pattern matching for RNA secondary structure[J]. Theoretical Computer Science,2015(592):59-71.
[16]林娟.RNA二级结构预测的群智能优化算法研究[D].福州:福建农林大学,2011.
[17]BALDI P,BRUNAK S,CHAUVIN Y,et al.Assessing the accuracy of prediction algorithms for classication: an overviews[J]. Bioinformatics,2000,16(5):412-424.
[18]VAN BATENBURG F H D,GULTYAEV A P,PLEIJ C W A.An APL-programmed genetic algorithm for the prediction of RNA secondary structure[J].Journal of theoretical Biology,1995,174(3):269-280.
[19]GARDNER PP,GIEGERICH R.A comprehensive comparison of comparative RNA structure prediction approaches[J]. BMC Bioinformatics, 2004, 5 (1):1-32.
[20]胥杰. 基于混沌模拟退火算法的RNA二级结构预测的研究[D].成都:电子科技大学,2010.
[21]E TEN DAM,K PLEIJ,D DRAPER.Structural and functionalaspects of RNA pseudoknots[J]. Biochemistry, 1992,31(47):11665-11676.