基于Hadoop的多序列比对和系统发育树重建

Q. Zou
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引用次数: 3

摘要

多序列比对(MSA)是计算生物学中的“圣杯”问题,但在同源序列的大量MSA中出现了瓶颈。大多数可用的最先进的软件工具不能处理大规模数据集,或者它们运行得相当慢。同源DNA序列的相似性常常被忽略。缺乏并行化仍然是MSA研究的一个挑战。构建超大序列的系统发育树也是一项耗时的工作。MSA是系统发育重建的前期工作。随着并行计算的发展,我们采用Hadoop平台来解决这两个计算密集型问题。三树和后缀树用于加速多个相似DNA序列的比对。期望时间复杂度由平方时间降为线性时间。在系统发生树重建中,并行进行聚类和多序列比对,并利用邻域连接模型构建基本系统发生树。在两个大于1gb的大型数据集上的实验表明,我们的软件工具可以优于其他常见的系统发育重建工具。此外,数据、软件代码和web服务器都是在http://lab.malab.cn/soft/halign/和http://lab.malab.cn/soft/HPtree/上打开的
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple sequence alignment and reconstructing phylogenetic trees with Hadoop
Multiple sequence alignment (MSA) is the “Holy Grail” problem in computational biology, but bottlenecks arise in the massive MSA of homologous sequences. Most of the available state-of-the-art software tools cannot address large-scale datasets, or they run rather slowly. The similarity of homologous DNA sequences is often ignored. Lack of parallelization is still a challenge for MSA research. Building the phylogenetic trees for ultra-large sequences is also a time-consuming work. MSA is the previous work for phylogenetic reconstruction. With the development of parallel computation, we employed Hadoop platform to solve the two computational intensive problems. Trie trees and suffix trees were used for accelerating multiple similar DNA sequences alignment. The expected time complexity was decreased to linear time from square time. For the phylogenetic tree reconstruction, clustering and multiple-sequence alignment were executed in parallel, and the basic phylogenetic trees were built using the neighbour-joining model. Experiments on two large datasets, both more than 1 GB, show that our software tool can outperform other common phylogenetic reconstruction tools. Furthermore, data, software codes, and web servers were all opened in http://lab.malab.cn/soft/halign/ and http://lab.malab.cn/soft/HPtree/
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