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Efficient Dominant Point Algorithms for the Multiple Longest Common Subsequence (MLCS) Problem

机译:多重最长公共子序列(MLCS)问题的有效优势点算法

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Finding the longest common subsequence of multiple strings is a classical computer science problem and has many applications in the areas of bioinfor-matics and computational genomics. In this paper, we present a new sequential algorithm for the general case of MLCS problem, and its parallel realization. The algorithm is based on the dominant point approach and employs a fast divide-and-conquer technique to compute the dominant points. When applied to find a MLCS of 3 strings, our general algorithm is shown to exhibit the same performance as the best existing MLCS algorithm by Hakata and Imai, designed specifically for the case of 3 strings. Moreover, we show that for a general case of more than 3 strings, the algorithm is significantly faster than the best existing sequential approaches, reaching up to 2-3 orders of magnitude faster on the large-size problems. Finally, we propose a parallel implementation of the algorithm. Evaluating the parallel algorithm on a benchmark set of both random and biological sequences reveals a near-linear speed-up with respect to the sequential algorithm.
机译:寻找多个字符串的最长的公共子序列是一个经典的计算机科学问题,并且在生物信息学和计算基因组学领域有许多应用。本文针对MLCS问题的一般情况,提出了一种新的顺序算法及其并行实现。该算法基于优势点方法,并采用快速分治技术来计算优势点。当用于查找3个字符串的MLCS时,我们的通用算法显示出与Hakata和Imai专门针对3个字符串的情况设计的现有最佳MLCS算法相同的性能。而且,我们表明,对于多于3个字符串的一般情况,该算法比现有的最佳顺序方法要快得多,在大型问题上,其速度最高可以提高2-3个数量级。最后,我们提出了该算法的并行实现。在随机和生物学序列的基准集上评估并行算法,揭示了相对于顺序算法的近线性加速。

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