MetaSort, a new technique for the structural analysis of microbiomes based on strategies to reduce species complexity.

On January 23, the international academic journal Nature Communications published online the results of a study by Zhao Fangqing of the Computational Genomics Laboratory of the Beijing Institute of Life Sciences of the Chinese Academy of Sciences entitled MetaSort untangles metagenome assembly by reducing microbial complexity.
the study proposed new techniques for the analysis of complex microbiome structures based on strategies to reduce species complexity.
the diversity of microbiome community structure is an important basis for community to play ecological function, so analyzing complex microbial community structure has always been the focus of macrogenome research, but it is also difficult.
analysis of microbial community structure in previous studies is mainly achieved by comparing with the reference database, so the study of microbiome in unknown environments is greatly restricted.
to obtain the microbial genome from the individual cell level, single-cell sequencing technology can have important application potential in the analysis of the genome structure of complex microbiomes.
, however, because of the inherent defects of microbial single-cell sequencing technology, such as high cost, low success rate and unetril data coverage, its application in microbiomics research is greatly restricted.
To address these issues, Zhao Fangqing's team proposed metaSort, a new technique for the structural analysis of microbiomes based on strategies to reduce species complexity, combining single-cell sequencing with genome-wide random sequencing techniques to obtain complete sequences of the genomes of different species in the microbiome.
metaSort used fluid cytometics to sequence bacteria in macrogenome samples and then select a specified number of subsets of bacteria within a specified interval.
then, each subset of bacteria was amplified and sequenced using single-cell techniques.
to take advantage of the original macro genome and subset of the bacteria selected, they also proposed two new algorithmic models: BAF and MGA.
these two methods can recover the target genome sequence from the original macro genome data and topologically assemble and identify the variation using the partial genome sequences of the subset rich bacteria.
the technique in oral and intestinal microbial samples in recent studies to demonstrate the effectiveness of the method.
they further studied the unknown microbiome, the seaweed surface symbicism: 72 near-complete genome sequences of microorganisms were successfully obtained through three streaming cell sequencings alone.
the stitched genome sequence by three generations of sequencing technology, which shows that the metaSort method is highly accurate.
has been publicly published on the free open source site SourceForge for researchers to download and use. The advantage of the
metaSort approach is that it provides users with a flexible way to obtain the genome sequences of microorganisms in new environmental samples, allowing users to control whether individual cells are selected or large cell compositions in range and range, and to control the number of selected cells and the complexity of smaller macrogenomes in the region.
, metaSort's subset of cells intersects very little compared to traditional single-cell sequencing, which means higher volumes and lower costs.
In addition, other sub-selecting methods, such as specific nucleic acid probes and antibody-labeled beads, can be applied to metaSort to obtain the target bacteria, which greatly improve the scope of metasort's application, thereby driving research into microbial composition, gene function and metabolic networks in unknown environments.
the work was jointly completed by Zhai Peifeng, an assistant researcher on Zhao Fangqing's team, and Zhang Yanming, a postdoctoral fellow, and was funded by the National Natural Science Foundation of China and the Ministry of Science and Technology's key research and development program.
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