Scalable metagenomic taxonomy classification using a reference genome database.
Scalable metagenomic taxonomy classification using a reference genome database.
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DOI：
10.1093/bioinformatics/btt389
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发表时间：
2013-09-15
期刊：
影响因子：
--
通讯作者：
Allen JE
中科院分区：
文献类型：
--
作者：
Ames SK;Hysom DA;Gardner SN;Lloyd GS;Gokhale MB;Allen JE
动机：生物样本的深度元基因组测序有可能恢复原本难以检测的微生物，并在对样本内容的有限先验知识的情况下准确地描述生物样本的特征。然而，现有的元基因组分类算法不能很好地扩展到分析大型元基因组数据集，平衡分类精度和计算效率是一个基本的挑战。结果：提出了一种通过创建支持可扩展元基因组分类的分类/基因组索引来将计算成本转移到离线计算的方法。在真实和模拟数据上展示了可扩展的性能，以显示在包括病毒、原核生物、真菌和原生生物在内的样本上存在新生物的情况下的准确分类。之前发表的150千兆基Tyrolean Iceman数据集的分类分类被发现在单节点40核大内存机器上花费了20小时，并为样本的元基因组内容提供了新的见解。可获得性：软件是用C++实现的，可通过http://sourceforge.net/projects/lmat免费获得补充信息：补充数据可从生物信息学在线获得。
Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a taxonomy/genome index that supports scalable metagenomic classification. Scalable performance is demonstrated on real and simulated data to show accurate classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: allen99@llnl.gov Supplementary information: Supplementary data are available at Bioinformatics online.
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