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Constructing Neighbor-Joining phylogenetic trees with reduced redundancy computation

机译:用减少的冗余计算构建邻近的系统发育树

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A fast algorithm for constructing Neighbor-Joining phylogenetic trees has been developed. The CPU time is drastically reduced as compared with Saitou and Nei??s algorithm (SN) [4] and Studier and Kepler??s algorithm (SK) [5]. The new algorithm includes three techniques: Firstly, a linear array A[N] is introduced to store the sum of every row of the distance matrix (the same as SK), which can eliminate many repeated (redundancy) computations. Secondly, the value of A[i] are computed only once at the beginning of the algorithm, and are updated by three elements in the iteration. Thirdly, a very compact formula for the sum of all the branch lengths of OTUs (Operational Taxonomic Units) i and j has been designed. The results show that our algorithm is from tens to hundreds times faster than SN and about two times faster than SK when N increases, constructing the tree with 2000 OTUs in 3 minutes on our desktop computer (CPU: Intel Celeron 2.4GHz, RAM: 256MB and OS: Windows 2000 Professional).
机译:开发了一种快速构建邻近的系统发育树木的快速算法。与SAITOU和NEI?S算法(SN)[4]和职位和开普勒算法(SK)[5]相比,CPU时间急剧减少。该新算法包括三种技术:首先,引入线性阵列A [n]以存储距离矩阵(与SK)的每一行的总和,这可以消除许多重复(冗余)计算。其次,[i]的值仅在算法的开头计算一次,并且在迭代中的三个元素更新。第三,设计了一个非常紧凑的公式,用于Otus(运营分类单位)I和J的所有分支长度的总和。结果表明,当N增加时,我们的算法比SN从SN速度快,大约比SK更快,约两倍,在我们的台式计算机上3分钟内使用2000 Otus(CPU:Intel Celeron 2.4Ghz,RAM:256MB和操作系统:Windows 2000 Professional)。

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