MetaSort untangles metagenome assembly by reducing microbial community complexity.
MetaSort untangles metagenome assembly by reducing microbial community complexity.
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MetaSort 通过降低微生物群落复杂性来理清宏基因组组装
DOI：
10.1038/ncomms14306
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发表时间：
2017-01-23
影响因子：
16.6
通讯作者：
Zhao F
中科院分区：
文献类型：
--
作者：
Ji P;Zhang Y;Wang J;Zhao F
目前大多数分析宏基因组数据的方法依赖于参考基因组。新的微生物群落远远超出了参考数据库的覆盖范围，从复杂的微生物群落从头组装宏基因组仍然是一个巨大的挑战。在这里，我们提出了一种新的实验和生物信息学框架，MetaSort，从宏基因组样本的细菌基因组的有效建设。MetaSort提供了一种基于流式细胞术和单细胞测序方法的分选微型宏基因组的方法，并采用新的计算算法，通过原始宏基因组的互补，从分选的微型宏基因组中有效地恢复高质量的基因组。通过广泛的评估，我们证明了MetaSort在基因组恢复和组装方面具有出色且公正的性能。此外，我们将MetaSort应用于在海带表面上定殖的未探索的微生物菌群，并一次成功地回收了75个高质量的基因组。这种方法将大大改善从复杂或新的社区获得微生物基因组的机会。目前可用的宏基因组数据分析依赖于参考基因组。在这里，作者描述了一种新的从头宏基因组组装方法，MetaSort，该方法从宏基因组样本中构建细菌基因组，以降低微生物群落的复杂性，同时增加基因组的回收和组装。
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. Currently available metagenomic data analysis relies on reference genomes. Here, the authors describe a new de novo metagenomic assembly method, metaSort, that constructs bacterial genomes from metagenomic samples to reduce microbial community complexity while increasing genome recovery and assembly.