MetaSort untangles metagenome assembly by reducing microbial community complexity

Peifeng Ji, Yanming Zhang, Jinfeng Wang and Fangqing Zhao ()
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Peifeng Ji: Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences
Yanming Zhang: Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences
Jinfeng Wang: Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences
Fangqing Zhao: Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences

Nature Communications, 2017, vol. 8, issue 1, 1-14

Abstract: Abstract Most current approaches to analyse metagenomic data rely on reference genomes. Novel microbial communities extend far beyond the coverage of reference databases and de novo metagenome assembly from complex microbial communities remains a great challenge. Here we present a novel experimental and bioinformatic framework, metaSort, for effective construction of bacterial genomes from metagenomic samples. MetaSort provides a sorted mini-metagenome approach based on flow cytometry and single-cell sequencing methodologies, and employs new computational algorithms to efficiently recover high-quality genomes from the sorted mini-metagenome by the complementary of the original metagenome. Through extensive evaluations, we demonstrated that metaSort has an excellent and unbiased performance on genome recovery and assembly. Furthermore, we applied metaSort to an unexplored microflora colonized on the surface of marine kelp and successfully recovered 75 high-quality genomes at one time. This approach will greatly improve access to microbial genomes from complex or novel communities.

Date: 2017
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DOI: 10.1038/ncomms14306

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