Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms.
Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms.
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基于组成的短宏基因组序列分类阐明了微生物的分类和功能富集景观
DOI：
10.1093/nar/gks828
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发表时间：
2013-01-07
影响因子：
14.9
通讯作者：
Qi J
中科院分区：
文献类型：
--
作者：
Liu J;Wang H;Yang H;Zhang Y;Wang J;Zhao F;Qi J
与传统的长元基因组序列分类算法相比，基于数千万个非常短的读数来表征微生物的分类和功能丰度要具有更大的挑战性。我们描述了一种有效的基于组成和系统发育的算法[元基因组组成向量(MetaCV)]来将非常短的元基因组阅读片段(75-100bp)分类到特定的分类和功能组中。我们将MetaCV应用于Meta-Hit数据(109个人类肠道元基因组的371-GB 75-BP读数)，这种基于单次读取的分类方法具有高分辨率，可以表征人类肠道微生物区系的组成和结构，特别是对于低丰度物种。最引人注目的是，在一台具有5个24核节点的服务器上，MetaCV只需10天即可完成所有计算工作。据我们所知，MetaCV得益于成分比较策略，是第一个能够在负担得起的时间内对数百万非常短的阅读进行分类的算法。
Compared with traditional algorithms for long metagenomic sequence classification, characterizing microorganisms’ taxonomic and functional abundance based on tens of millions of very short reads are much more challenging. We describe an efficient composition and phylogeny-based algorithm [Metagenome Composition Vector (MetaCV)] to classify very short metagenomic reads (75–100 bp) into specific taxonomic and functional groups. We applied MetaCV to the Meta-HIT data (371-Gb 75-bp reads of 109 human gut metagenomes), and this single-read-based, instead of assembly-based, classification has a high resolution to characterize the composition and structure of human gut microbiota, especially for low abundance species. Most strikingly, it only took MetaCV 10 days to do all the computation work on a server with five 24-core nodes. To our knowledge, MetaCV, benefited from the strategy of composition comparison, is the first algorithm that can classify millions of very short reads within affordable time.
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