Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.
Nat Commun. 2017 Jan 23;8:14306. doi: 10.1038/ncomms14306.
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.
大多数当前分析宏基因组数据的方法都依赖于参考基因组。新的微生物群落远远超出了参考数据库的覆盖范围，而从头开始对复杂微生物群落进行宏基因组组装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，用于从宏基因组样本中有效地构建细菌基因组。metaSort 提供了一种基于流式细胞术和单细胞测序方法的排序迷你宏基因组方法，并采用新的计算算法通过原始宏基因组的互补来从排序迷你宏基因组中高效回收高质量基因组。通过广泛的评估，我们证明了 metaSort 在基因组回收和组装方面具有出色且无偏的性能。此外，我们将 metaSort 应用于海洋巨藻表面定殖的未探索微生物群，并一次成功回收了 75 个高质量基因组。这种方法将极大地提高从复杂或新型群落中获取微生物基因组的能力。
