Method for clustering metagenome sequences by using double-layer probability model

Abstract

The invention relates to the technical field of metagenome sequence clustering, in particular to a method for clustering metagenome sequences by using a double-layer probability model, which comprises the following steps: P1, clustering all sequences in an initial metagenome by using a first-layer probability model to obtain a plurality of primary clusters; p2, clustering each primary cluster again by using a second-layer probability model to obtain a final cluster; the second-layer model comprises a seed selection model, a k-mer frequency probability model and a coverage probability model. According to the method for clustering the metagenome sequences by using the double-layer probability model, all the sequences in the initial metagenome are processed by using the double-layer model, so that the characteristics of different dimensions of the metagenome sequences can be effectively utilized, and the method is suitable for all metagenome sequencing data; the data include intestinal microorganism data, soil microorganism data, water microorganism data and the like.

Description

technical field [0001] The invention relates to the field of metagenomic sequence clustering technology, in particular to a method for clustering metagenomic sequence by using a double-layer probability model. Background technique [0002] As the oldest existing life form on the earth, prokaryotes play an important role in the process of biological evolution, material cycle and environmental change on the earth. Mapping the complete genomes of bacteria and archaea is the basis for the study of their systematic classification, and it is also the basis for the study of microbial evolution, Key data on ecology and community structure and function. With the development of sequencing technology and metagenomic assembly technology, it is possible to recover microbial sequences from sequencing data, but limited by the complexity of real environmental samples, metagenomic sequences obtained by sequence assembly are highly fragmented. It is an effective way to match the sequences in...

AI Technical Summary

Problems solved by technology

[0004] The existing clustering methods do not use the above three features reasonably and effectively, which is manifested in the unreasonable weight setting of different features, the inability of the probability model to fit the features well, and the imperfect method construction, etc.

Although the existing methods have achieved good results on simulated data and real samples, there is still a big gap from the ideal results, mainly due to the low integrity and high pollution of the genome generated by clustering.

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