An optimized metagenomic binning method for analyzing microbial communities

Abstract

The present invention discloses an optimized metagenomic binning method for analyzing microbial communities, including filtering sequencing data to obtain high-quality sequencing data, and then selecting different assembly strategies to obtain contigs according to the source of the sample and the amount of sequencing data, Genetic data analysis was then performed. Compared with the prior art, the present invention is a kind of biological information analysis of the whole community and "single bacterium" genome analysis that does not rely on isolation and culture simultaneously for the microbial community. At the level of metagenomics, it provides an efficient and high-quality assembly algorithm that is more suitable for sample characteristics and sequencing data volume, and contains rich and comprehensive information analysis content, and personalized and novel visualization. Realized the qualitative change of metagenomic analysis from community to single bacteria. The scheme includes data correction that can improve accuracy, and comprehensive bin information summary, which is conducive to more convenient and efficient screening of valuable target bins. It also includes a comprehensive system Mining ideas for subsequent analysis of target bins.

Description

technical field [0001] The invention relates to the field of high-throughput sequencing technology and bioinformatics analysis of sequencing data, in particular to an optimized metagenomic binning method for analyzing microbial communities. Background technique [0002] There are many kinds of microorganisms in the community, such as air microorganisms, intestinal microorganisms, soil microorganisms, etc. With the popularization of high-throughput sequencing technology, metagenomics for sequencing the diversity and functions of community microorganisms is also on the rise. Metagenome sequencing can Obtain species DNA information of all microorganisms in a community. [0003] Metagenome binning is the process of classifying contigs obtained from metagenomic sequencing mixed with sequences from different organisms or assembled by species separately. . The traditional whole genome sequence of a single species is obtained after pure culture and de novo sequencing of the whole ...

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 prevent customers from efficiently identifying new species of the same genus and different species; in addition, the genus level is unknown, which does not mean that the higher-level taxonomic level annotations are unknown. For many particularly new species, it is true that they can only be annotated to the order, family and other classification levels, so this part of the bin annotated as NA cannot be scientifically evaluated and judged due to lack of information, and may even miss the possibility of discovering the target new species; And with the bioinformatics analysis level of mainstream customers, it is impossible to re-analyze and identify all NA bins;

[0018] 4) Lack of visual form

Only the simplest table output results are provided, without considering the user's efficient information extraction and summary requirements after obtaining the bin

[0019] 5) The existing process analysis lacks the follow-up data mining of bin

At present, after binning analysis, the bin is regarded as a potential genome and the analysis is interrupted, or only the genome sequence after the bin reassembly is provided, and the data mining ideas and results based on the target bin are not provided.

The binning analysis is only an intermediary, binning is only the starting point for subsequent analysis, and the real data mining has not yet begun

Since bin analysis spans from the total DNA of the community to the "genome", it jumps into a completely different analysis field, so it is very unfriendly to users' in-depth analysis in the later stage, and the requirements for users' biological foundation are too high

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