期刊
BRIEFINGS IN BIOINFORMATICS
卷 13, 期 1, 页码 107-121出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbr009
关键词
pyrosequencing; 16S rRNA; taxonomy-independent analysis; massive data; clustering; microbial diversity estimation; human microbiome
资金
- NIH Human Microbioime [1UH2DK083993-01]
- University of Michigan
- W.M. Keck Foundation
- Alfred P. Sloan Foundation
- Howard Hughes Medical Institute
- American Cancer Society [MRSGT CCE-107301]
- NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES [UL1TR000064] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [UH2DK083993] Funding Source: NIH RePORTER
Recent advances in massively parallel sequencing technology have created new opportunities to probe the hidden world of microbes. Taxonomy-independent clustering of the 16S rRNA gene is usually the first step in analyzing microbial communities. Dozens of algorithms have been developed in the last decade, but a comprehensive benchmark study is lacking. Here, we survey algorithms currently used by microbiologists, and compare seven representative methods in a large-scale benchmark study that addresses several issues of concern. A new experimental protocol was developed that allows different algorithms to be compared using the same platform, and several criteria were introduced to facilitate a quantitative evaluation of the clustering performance of each algorithm. We found that existing methods vary widely in their outputs, and that inappropriate use of distance levels for taxonomic assignments likely resulted in substantial overestimates of biodiversity in many studies. The benchmark study identified our recently developed ESPRIT-Tree, a fast implementation of the average linkage-based hierarchical clustering algorithm, as one of the best algorithms available in terms of computational efficiency and clustering accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据