Optimized method for analyzing microbial communities through metagenome binning

Abstract

The invention discloses an optimized method for analyzing microbial communities through metagenome binning. The method comprises the following steps of: filtering sequencing data to obtain high-quality sequencing data, selecting different assembly strategies to obtain contigs according to the source of a sample and the sequencing data volume, and analyzing gene data. Compared with the prior art, the method disclosed by the invention is used for simultaneously carrying out biological information analysis on the whole community and single-bacterium genome analysis independent of isolated cultureaiming at the microbial community. According to the invention, on the metagenome level, an efficient high-quality assembly algorithm which is more suitable for sample characteristics and sequencing data volume is provided, rich and comprehensive information analysis content is contained, and individuality and novelty are visualized; and the method realizes metagenome analysis qualitative change from communities to single bacteria, and the scheme comprises data correction capable of improving accuracy and comprehensive and perfect bin information summarization, is beneficial to screening valuable target bins more conveniently and efficiently, and further comprises systematically perfect mining ideas of subsequent analysis of the target bins.

Description

Technical field [0001] The present invention relates to high-throughput sequencing and sequencing analysis of biological data areas of information technology, especially relates to a method for optimizing the microbial community metagenomic analyzes binning. Background technique [0002] Diverse microbial communities, such as air-borne microbes, intestinal microorganisms, etc. Microorganism, with the popularity of high-throughput sequencing technology, for metagenomic sequencing microbial community diversity and functions along with the rise of the macro may genome sequencing obtaining information of all DNA species of microorganisms within a community. [0003] Macro genetic component box (binning) is a metagenomic sequenced mixing process sequences or different organisms obtained contigs assembled separately categorized by species. . After conventional single species are the complete genome sequence was a pure culture, then de novo genome sequencing was obtained, but there are ...

AI Technical Summary

Problems solved by technology

[0011] 1) Single assembly strategy

The sample type (such as natural environment samples, symbiotic bacteria samples), the amount of sample sequencing data (such as 6G, 10G, 20G, 100G, etc.), the number of biological repeats, etc. were not fully considered, and the default assembly parameters of the software were directly used without testing and evaluation , leading to disadvantages such as non-targeted assembly, low computing efficiency, and taking up more operating resources;

[0012] 2) The annotation adopts the default parameters of the software, without comparison and optimization, and the annotation strategy is single

Although some patents already have a binning analysis process based on metagenomics, there is no time-consuming and resource-consuming data correction part, which will directly affect the accuracy of bin clustering and bin identification;

[0016] 3) It is difficult to provide detailed bin gene annotations

[0017] 3) The reference value of bin species annotation results is extremely low

The default output of the existing process is the genus-level species or NA (unknown genus level) of each species. On the one hand, only the genus level is displayed, which is too abstract, and the user does not have an intuitive understanding and cannot obtain effective reference information. The database checks annotations at other classification levels, and generally one sample may obtain hundreds of high-quality bins, so the workload is relatively large; A single bacterial genome can be used as a specific species, but the existing process does not provide species-level annotations, which may

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