Resampling nucleotide sequences with closest-neighbor trimming and its comparison to other methods.
Resampling nucleotide sequences with closest-neighbor trimming and its comparison to other methods.
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DOI：
10.1371/journal.pone.0057684
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Ito K
中科院分区：
文献类型：
--
作者：
Yonezawa K;Igarashi M;Ueno K;Takada A;Ito K
公共数据库中有大量各种病原体的核苷酸序列。数据集的增长导致了计算成本的巨大增加。此外，由于监测活动的差异，数据库中发现的序列数量因国家和年份而异。因此，研究重采样方法以减小采样偏差是十分重要的。提出了一种新的算法-称为最近邻修剪法-从大型核苷酸序列数据集中重新采样给定数量的序列。利用人H3N2流感病毒的核苷酸序列与其他算法进行了性能比较。将最近邻裁剪算法与朴素层次聚类算法和-媒质聚类算法进行了比较。公共数据库中积累的遗传信息存在抽样偏差。最近邻修剪方法可以从给定的数据集中稀疏密集的采样序列。由于核苷酸序列是生命科学中最广泛使用的材料之一，我们预计我们对各种数据集的算法将导致减少采样偏差。
A large number of nucleotide sequences of various pathogens are available in public databases. The growth of the datasets has resulted in an enormous increase in computational costs. Moreover, due to differences in surveillance activities, the number of sequences found in databases varies from one country to another and from year to year. Therefore, it is important to study resampling methods to reduce the sampling bias. A novel algorithm–called the closest-neighbor trimming method–that resamples a given number of sequences from a large nucleotide sequence dataset was proposed. The performance of the proposed algorithm was compared with other algorithms by using the nucleotide sequences of human H3N2 influenza viruses. We compared the closest-neighbor trimming method with the naive hierarchical clustering algorithm and -medoids clustering algorithm. Genetic information accumulated in public databases contains sampling bias. The closest-neighbor trimming method can thin out densely sampled sequences from a given dataset. Since nucleotide sequences are among the most widely used materials for life sciences, we anticipate that our algorithm to various datasets will result in reducing sampling bias.
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