Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms |
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| Authors: | Jiemeng Liu Haifeng Wang Hongxing Yang Yizhe Zhang Jinfeng Wang Fangqing Zhao Ji Qi |
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| Affiliation: | 1State Key Laboratory of Genetic Engineering, 2State Key Laboratory of Surface Physics, 3The T-Life Research Center, 4Institute of Plant Biology, School of Life Sciences, Fudan University, 5School of Life Sciences, Shanghai Jiaotong University, Shanghai 200433, 6Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, People’s Republic of China |
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| Abstract: | 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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