摘要
选择具有最低频率的最优种子是一个复杂的计算问题,往往需要很长时间.提出了一种read的基于频率的合并种子选择算法(FMSS),该算法能够高效地选择接近最优的种子集合,可用于改善现有映射工具的性能.实验对比了平均种子选择方法和当前最优的种子选择策略(OSS,optimal seed solver),结果显示FMSS算法能够用很少的时间代价给出接近OSS的最优种子集合,这表明FMSS算法可集成到现有映射工具中用于处理更大规模的read mapping问题.
The selection of the optimal seed(that is,the seed with the lowest frequency)is a complex calculation problem,which often takes a long time.A frequency-based merge seed selection(FMSS) algorithm is proposed,which can efficiently select the suboptimal set of seeds and improve the performance of existing mapping tools.In the experiment,FMSS was compared with the average seed selection method and the optimal seed solver(OSS).Experimental results show that FMSS can select the optimal set of seeds close to OSS,and the time cost of FMSS is far lower than that of the OSS algorithm.The FMSS algorithm is more suitable for seed selection in terms of time cost and seed selection quality.
作者
马海涛
祁实
于长永
赵宇海
MA Hai-tao;QI Shi;YU Chang-yong;ZHAO Yu-hai(School of Computer Science & Engineering,Northeastern University,Shenyang 110169,China)
出处
《东北大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2019年第5期609-613,共5页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(61772124
61332014
61401080
61402087)
河北省自然科学基金资助项目(F2015501049)
河北省教育厅项目(QN2014339)
中央高校基本科研业务费专项资金资助项目(N150402002)